CULTURA
E SCIENZA
DEL COLORE
COLOR
CULTURE
AND SCIENCE
Rivista dell’Associazione Italiana Colore
rivista semestrale | half-yearly journal
www.gruppodelcolore.it
04
15
CULTURA E SCIENZA DEL COLORE
COLOR CULTURE AND SCIENCE
NUMERO 04 - OTTOBRE 2015
NUMBER 04 - OCTOBER 2015
Rivista dell’Associazione Italiana Colore
http://jcolore.gruppodelcolore.it/
ISSN 2384-9568
DIRETTORE RESPONSABILE | EDITOR-IN-CHIEF
Maurizio Rossi
VICEDIRETTORE | DEPUTY EDITOR
Davide Gadia
COMITATO SCIENTIFICO | SCIENTIFIC COMMITTEE
John Barbur (City University London, UK)
Berit Bergstrom (NCS Colour AB, SE)
Giulio Bertagna (B&B Colordesign, IT)
Janet Best (Natific, UK)
Aldo Bottoli (B&B Colordesign, IT)
Patrick Callet (École Centrale Paris, FR)
Jean-Luc Capron (Université Catholique de Louvain, BE)
Osvaldo Da Pos (Università degli Studi di Padova, IT)
Bepi De Mario (CRASMI, IT)
Hélène DeClermont-Gallernade (Chanel Parfum beauté, FR)
Reiner Eschbach (Xerox, USA)
Alessandro Farini (INO-CNR, IT)
Christine Fernandez-Maloigne (University of Poitiers, FR)
Renato Figini (Konica-Minolta, EU)
Davide Gadia (Università degli Studi di Milano, IT)
Robert Hirschler (Serviço Nacional de Aprendizagem Industrial, BR)
Sandra Krasovec (Fashion Institute of Technology, USA)
Francisco Imai (Canon, USA)
Lia Luzzatto (Color and colors, IT)
Kevin Mansfield (UCL, UK)
Veronica Marchiafava (IFAC-CNR, IT)
Gabriel Marcu (Apple, USA)
Manuel Melgosa (Universidad de Granada, ES)
Anna Grazia Mignani (IFAC-CNR, IT)
Annie Mollard-Desfour (CNRS, FR)
Maria Luisa Musso (Universidad de Buenos Aires, RA)
Claudio Oleari (Università degli Studi di Parma, IT)
Laurence Pauliac (Historienne de l’Art et de l’Architecture, Paris, FR)
Marcello Picollo (IFAC-CNR, IT)
Renata Pompas (AFOL Milano-Moda, IT)
Boris Pretzel (Victoria & Albert Museum, UK)
Noel Richard (University of Poitiers, FR)
Katia Ripamonti (Cambridge Research System, UK)
Alessandro Rizzi (Università degli Studi di Milano, IT)
Maurizio Rossi (Politecnico di Milano, IT)
Jodi L. Sandford (Università di Perugia, IT)
Raimondo Schettini (Università degli Studi di Milano Bicocca, IT)
Gabriele Simone (ST Microelectronics, IT)
Andrea Siniscalco (Politecnico di Milano, IT)
Ferenc Szabó (University of Pannonia, HU)
Mari UUsküla (Tallinn University, EE)
Francesca Valan (Studio Valan, IT)
Ralf Weber (Technische Universität Dresden, DE)
Alexander Wilkie (Charles University in Prague, CZ)
COLLABORATORI | CONTRIBUTORS
Walter Arrighetti, Simone Bianco, Rossella Catanese, Gloria
Menegaz, Emanuela Orlando, Giulia Paggetti, Renata Pompas, Jodi
L. Sandford, Raimondo Schettini, Jada Schumacher, Arvi Tavast,
Mari Uusküla, Veronica Valdegamberi, Paola Valentini
REDAZIONE | EDITORIAL STAFF
Aldo Bottoli, Daria Casciani, Davide Gadia, Veronica Marchiafava,
Francesca Valan
EDITORE | PUBLISHER
Gruppo del Colore – Associazione Italiana Colore
www.gruppodelcolore.it
GRUPPO DEL COLORE
ASSOCIAZIONE ITALIANA COLORE
CULTURA E SCIENZA DEL COLORE
COLOR CULTURE AND SCIENCE
Rivista dell’Associazione Italiana Colore
Registrazione presso il Tribunale di MIlano
al n. 233 del 24.06.2014
04
SOMMARIO | SUMMARY
ENGLISH
Repainting the mechanical ballet. Restoration of colours in
‘Ballet mécanique’ by Fernand Léger
4
On the perceptual/linguistic origin of the twelfth basic
color term in the Italian color lexicon
8
by Rossella Catanese
by Veronica Valdegamberi, Giulia Paggetti, Gloria Menegaz
Motion Picture Colour Science and film ‘Look’: the maths 14
behind ACES 1.0 and colour grading
by Walter Arrighetti
A Cognitive Linguistic Usage Perspective: What is Italian 22
Blu, azzurro, celeste? Do English agree on BLUE semantics?
by Jodi L. Sandford
Society in colour: Italian television and the switch to
colours
31
by Paola Valentini
Color, Mon Dieu: A Case-Study Comparison Between The 36
Church Of The Epiphany (New York City) and Kresge Chapel
(Cambridge, Massachusetts)
by Jada Schumacher
How blue is azzurro? Representing probabilistic equivalency 43
of colour terms in a dictionary
by Arvi Tavast, Mari Uusküla
Adaptive Illuminant Estimation and Correction for Digital 49
Photography
by Simone Bianco, Raimondo Schettini
RUBRICA | COLUMN
I colori di Gualtiero Marchesi | Gualtiero Marchesi’s colours 56
by Emanuela Orlando
RECENSIONI
Jean-Gabriel Causse, Lo stupefacente potere dei colori,
Ponte alle Grazie, Milano, 2015
60
a cura di Renata Pompas
Rossella Catanese
[email protected]
1
1
Dipartimento Storia dell’Arte e
dello Spettacolo,
Sapienza Università di Roma
Repainting the mechanical ballet.
Restoration of colours in ‘Ballet
mécanique’ by Fernand Léger
1. INTRODUCTION
This paper comes from a research [1] carried out
two years ago during the Haghefilm Foundation
internship programme.
Haghefilm Foundation was a non-profit
international organization created by the
laboratories Haghefilm Conservation in order
to improve research and support activities
related to techniques and technologies of
film preservation and restoration [2]. Into this
framework, Haghefilm Foundation’s main goal
was to promote results of the research, but
this often leads to deal with copyright holders
instead of screening and proposing experimental
researches in educational contexts just for free
knowledge.
2. FILM RESTORATION
Since the Eighties of the last century, after
UNESCO conference [3], film has been
institutionally considered as cultural, rather
than a commercial item. It has been given a
systematic, academic and ethical disposal to
film conservation and restoration practices.
Films required conservation policies and cold
storage for their structural fragility [4]. They have
got a number of unchanged features and other
elements that we have tried to “improve” in order
to preserve them. The films have a part called
photosensitive emulsion, from the early years,
when the supports were made of flammable
cellulose nitrate films, and then when they
were made of safety (non-flammable) cellulose
acetate, until polyester bases introduced in the
Eighties and currently used in the productiondistribution market.
This emulsion may have a wide range of
components, including salts such as silver nitrate
or chromogenic couplers in the emulsions of
colour film, but it is interesting to know that the
function of emulsifier is given by the gelatine,
an organic product. This “organic” dimension
in the film, susceptible to degeneration, further
guarantees its syncretic charm. Film is therefore
an art that tremble of vivid emotions and has got
in its own materials an animated, living element.
Restoring an audiovisual text is always an
operation on the formal setting and every
different
medium
introduces
different
formal connotations. The restoration of an
audiovisual support is the re-formalization
4
and the reactivation of the text according to
its structure, examination of the text and of its
gaps, hermeneutic activity which consents to
recreate an identity opening to possible further
interpretative operations [5]. Actually restoration
is always an exegesis work, deep-rooted in
practice, but there is always a philological
research, exploring efficient methodologies to
understand and interpret the work.
The problem of modernity, as historical dialogue
and methodological customs, is a basic
principle in any restoration. In fact resetting a
text is always a dialogue with a past, near or
far, and it becomes a comparison between two
different ages. Every restoration is a liaison with
a memory. A useful definition for the audiovisual
restoration is proposed by Paolo Cherchi Usai:
«Restoration is the set of technical, editorial
and intellectual procedures aimed at
compensating for the loss or degradation of
the moving image artefact, thus bringing it
back to a state as close as possible to its
original conditions» [6].
The audiovisual support is radically different
from the artistic unicum, so also the idea
of original can change if you consider a
philological-textual level or a material level.
It will be different also the gaps treatment:
the video, as film and photography, has got
intrinsically the concept of copy, related to the
idea of technical reproducibility, giving also a
different perception, not one and synchronous.
The introduction of the numeric information
among the images system allowed a wide
flexibility of action on images, but a digital
medium implies a trans-coding process which
uses calculation by discrete, discontinue values,
turning light waves into numeric units (from digit
= decimal number). Even if digital system is a
completely different structure of representation,
these media are now in a transition age [7].
Thorburn and Jenkins wrote:
«in our current moment of conceptual
uncertainty and technological transition,
there is an urgent need for a pragmatic,
historically informed perspective that maps
a sensible middle ground between the
euphoria and the panic surrounding new
media […]» [8].
Cultura e Scienza del Colore - Color Culture and Science | 04/15
Digital technology ensures data repeatability
without alterations and a high potential
of intervention on data. The technological
advances, as the realisation of processors and
more powerful computer buffer, make the digital
arriving in cinema in higher standards.
3. A CASE STUDY:
BALLET MÉCANIQUE
3.1 THE FILM
The EYE Film Institute decided to commission
the restoration of its hand coloured copy of Ballet
mécanique (1924), Fernand Léger’s masterpiece
of the European Twenties Avant-garde, with Kiki
de Montparnasse, Dudley Murphy and Katherine
Murphy.
For EYE, the restoration lab Haghefilm acquired
a 2k digital scan in order to produce a digital
intermediate for both the colour and the black
and white parts (requiring positive editing for
the projection prints). But another experimental
project has been proposed, i.e. to produce
another, unique black and white positive copy
with hand-painted or tinted sections matching,
as closely as possible, the original nitrate. My
duties consisted in running colour tests under
the supervision of Paolo Cherchi Usai, Ulrich
Ruedel and Daniela Currò. Furthermore, I
pursued research about the history of this film
and its moot authorship.
Sought by many film archives in all the world
(New York, Paris, Mexico, Montreal, Bucharest,
Havana, London, München, Wien, Berlin, Los
Angeles, Berkeley, Belgrade, Stockholm, Turin,
Cambridge, Washington, Canberra, Roma,
Ottawa, Copenhagen), there are several versions
of Ballet mécanique. According to Giovanni Lista,
this film has to be regarded as a work in progress,
whose drafting continued for years, without an
original [9].
The nitrate coloured print owned by the Dutch
Filmmuseum came from property of Filmliga, a
cultural association born in 1927 in Amsterdam,
focused on the collection and the diffusion of
Avant-garde movies; it had a great season
between the 1928 and the 1930, and broke up in
the 1931. Then the collection of the association
has been acquired by the Filmmuseum, that
still keep those films and among them Ballet
mécanique [10].
Lawder defined Ballet mécanique as
«the classic example of a fully developed
painting aesthetic transposed into film by a
modern artist» [9].
The film is one of the most contested artworks
of the Twentieth century. Its genesis involved
different persons: Fernand Léger, Dudley Murphy,
Man Ray, Ezra Pound, Georges Antheil. Every
one of them worked on the film giving an artistic
contribution. It has been a collaborative work but
the question of the authorship is still now open
to debate. Some art historians and film scholars
have different interpretations: Standish Lawder
considers Léger as the creator, and Murphy as
an assistant; Judi Freeman and Susan Delson
emphasize the crucial contribution of Murphy
and involves Man Ray in the authorship. William
Moritz minimizes Léger’s contribution, ascribing
the creation mainly to Murphy and Man Ray [1114].
In the earliest known print of the film, the
opening credit reads “F. Leger and Dudley Murphy
present Ballet Mechanique”; and Léger’s notes on
the film, prepared as it was nearing completion
in 1924, call it a “Film by Fernand Léger and Dudley
Murphy” [14-15]. In Ballet mécanique’s film credits
Murphy’s contributions disappeared. Neither
Man Ray nor Pound nor Antheil was even
appointed. Man Ray in his autobiography Self
Portrait affirmed:
«and that is how Dudley realized the Ballet
mécanique, which had a certain success,
with Léger’s name» [16].
3.2 THE RESTORATION PROJECT
Haghefilm scanned both the Dutch 309m positive
nitrate copy, coloured, and the Cinémathèque
Française 291m positive black and white print,
in order to get the best results comparing
editing and sequences; the French 291m copy is
denser, darker and less clear than the Dutch one,
probably because the French print comes from
later generation elements. The editing order is
slightly different. Even if it is black and white,
the French copy presents sections coming from
a coloured source, with clearly denser frames.
I checked the entire Dutch copy shot by shot
and frame by frame and I wrote accurate
descriptions about the content of the shots, the
frame-counting, the evenness of saturation,
the kinds of applied colour, the splices and
the damages. I found that every tinted section
has been spliced and a lot of hand-painted
sections were not spliced, but directly painted
with many overlappings of colours at the frame
lines. According to Paul Read, during the handpainting period the dye used for hand colouring
were quite similar to those used for tinting:
paints or acid dyes in water, they were absorbed
easily by the gelatine of the emulsion side [17].
Then we attended the digital grading. The
Filmmuseum asked for a higher contrasted look
in order to recreate the shocking effect of that
famous and charming avant-garde feature. The
experimental project proposed by Filmmuseum
provided us with the opportunity to paint the
coloured frames like they look on the nitrate
copy. In the chemical lab of the Haghefilm I have
tried many different tinting experiments in order
04/15 | Cultura e Scienza del Colore - Color Culture and Science
5
to learn how to work with tinting processes. I
ran 62 tests in tinting and hand-painting, testing
hues, concentrations, dipping time, mixtures,
applications.
But the positive black and white print made by
the digital intermediate presented a bad grey
density which, overlapping the applied colours,
looks quite different from the original.
We thought it was possible to work digitally
taking out the colours and the brushstrokes, in
order to underline the black and white image
and to get a clear and vivid picture where
applied colours could be visible and faithful to
the original. Basically using the Hue/Saturation
software tools for the RGB channels might
work like a “reverse filter” for showing the colour
subtraction. In this process, theoretically correct,
we were not aware that the applied dyes were
not pure colours. Furthermore, we did not
consider the overlapping of colours, which mixes
hues regardless of the frame line. Some colour
data were mixed in the brushstroke marks and
we were able to find it by looking at the Magenta
sections visible in the Red frame, also visible by
the Blue channel.
So we thought to compare the coloured sections,
triangles and circles, of the Dutch copy with
the ones seen on the black and white French
element, in order to use those sections in the
new black and white print that we will have to
paint and tint. Those sections have been already
scanned and we checked frame by frame
analyzing the two copies in comparison and
producing a new record about the differences.
Many sections have been taken by this element
reducing the digital intervention in compositing
and the time spent in the digital work on the
singles frames.
This restoration has not been finished: one of
Haghefilm’s requests (improving of research on
film restoration and dissemination of its results)
was that the hand coloured print of the film had
to be made available by EYE to the Haghefilm
Foundation for exhibition in educational,
research, and outreach activities with mention
of EYE as the source of the material. EYE could
not agreed to this claim because of the “copyright
dilemma”: the Dutch cinémathèque can’t assign
anyone the right to show the print because it
doesn’t own that right, which is true for almost
every film they preserve and restore. In some
cases, like the so-called “orphan films” (works that
have been abandoned by its owner or copyright
holder), it wouldn’t have been a problem, but
in the case of Ballet mécanique it actually
was, because it is a film were the right holders
are very well known, so it was impossible for
Haghefilm to get the aforementioned rights. Of
course this negative event should be a chance
to think about the mission of films restoration,
about the free spread of research and its results.
6
4. CONCLUSIONS
I believe that this unique experience will be
useful to understand how to balance the digital
technologies, helpful and favourable to get
the best achievements, with the traditional
and fashionable applied colours, rediscovered
example of how film arts has been close to the
fine pictorial art.
BIBLIOGRAPHY
[1] R. Catanese, Lacune binarie. Il resamaranta2015
tauro dei film e le tecnologie digitali, Bulzoni, Rome,
2013; R. Catanese, G. Edmonds, B. Lameris, Hand-Painted
Abstractions: Experimental Color in the Creation and
Restoration of Ballet mécanique, in «The Moving Image»
Vol. 15, Number 1, Spring 2015, pp. 92-98.
[2] G. M. Paletz, The Finesse of the Film Lab: A Report
from a Week at Haghefilm, in «The Moving Image», Vol. 6,
Number 1, Spring 2006.
[3] UNESCO conference, Belgrade 27th October 1980.
http://portal.unesco.org/en/ev.php-URL_ID=13139&URL_
DO=DO_TOPIC&URL_SECTION=201.html (last visit in
06/08/2015).
[4] C. Frick, Saving Cinema. The Politics of Preservation,
Chicago, Oxford University Press, 2011;
[5] P. Bertetto, L’eidetico, l’ermeneutica e il restauro del
film (1991), in S. Venturini, Il restauro cinematografico:
principi, teorie, metodi, Campanotto, Udine, 2006, pp. 104106
[6] P. Cherchi Usai, Silent Cinema. An Introduction, British
Film Institute, London, 2000, p. 66
[7] G. Fossati, From Grain to Pixel. The Archival Life of Film
in Transition, Amsterdam University Press, Amsterdam,
2009
[8] D. Thorburn and H. Jenkins, Rethinking Media Change:
The Aesthetics of Transition, MIT Press, Cambridge, 2003,
p. 2
[9] G. Lista, Léger scénographe et cinéaste, in Id., Fernand
Léger et le spectacle, Editions de la Réunion des Musées
nationaux, Paris, 1995
[10] G. Manduca, La lavandaia sulle scale. Una nota
filologica al ‘Ballet mécanique’, in P. Bertetto e S. Toffetti,
Cinema d’avanguardia in Europa, Il Castoro, Milano, 1996
[11] S. Lawder, Cubist Cinema, New York University Press,
New York, 1975, p. 65
[12] J. Freeman, Bridging Purism and Surrealism:
the Origins and Production of Fernand Léger’s ‘Ballet
Mécanique’, in R.E. Kuenzli, Dada and Surrealist Film,
Locker & Owens, New York, 1987
[13] S. Delson, Dudley Murphy, Hollywood Wild Card,
Minnesota University Press, Minneapolis, 2006
[14] W. Moritz, Americans in Paris: Man Ray and Dudley
Murphy, in J.-K. Horak, Lovers of Cinema: The First
American Film Avant-Garde 1919–1945, University of
Wisconsin Press, Madison, 1995, pp. 118-136 e p. 199
[15] Published in the original French in «L’Esprit Nouveau»
Cultura e Scienza del Colore - Color Culture and Science | 04/15
no. 28, undated (most likely November or December 1924)
[16] Man Ray, Self Potrait, Little Brown, Boston-Toronto,
1963, p. 218
[17] P. Read, ‘Unnatural Colours’: An Introduction to
Colouring Techniques in Silent Era Movies, in «Film
History», vol. 21, no. 1, pp. 9-46
04/15 | Cultura e Scienza del Colore - Color Culture and Science
7
Veronica Valdegamberi
[email protected]
2
Giulia Paggetti
[email protected]
2
Gloria Menegaz
[email protected]
1
1
Department of Linguistics,
University of Verona, Italy
2
Department of Computer
Science, University of Verona,
Italy
On the perceptual/linguistic origin
of the twelfth basic color term in the
Italian color lexicon
ABSTRACT
Color categorization involves a cognitive mechanism that assigns linguistic labels
to perceived colors. In their pioneering study of 1969, Berlin and Kay proposed the
existence of eleven universal basic colors, founding the “universalist” theory on color
naming. Since then different studies have been proposed either supporting or refusing
such a conjecture based on new evidence. One of the most fascinating elements of
this research is the debate on the nature of the color naming mechanism: is it a
predominantly perceptual or linguistic effect? An inherent aspect stemming from Berlin
and Kay’s conjecture is the “weak relativist hypothesis” concerning the emergence of
additional basic color categories besides the established 11, specific to a certain
language. In this paper we investigate these two issues and propose some
arguments in favor of the existence of a twelfth basic color term in Italian. Following
the indications of the results of our previous work, two experiments were performed,
focusing on the linguistic and the perceptual aspects, respectively. Results support
the hypothesis of the existence of an additional basic category named azzurro (light
blue), as it is the case for Turkish, Greek, Russian and Maltese.
1. INTRODUCTION
The seminal work of Berlin and Kay introduced
the concept of universal basic color terms
(BCTs) [1], namely red, green, blue, yellow,
orange, purple, pink, brown, gray, white, and
black. According to their definition, color
terms are operationally defined as basic only
if monolexemic and psychologically salient for
all speakers, but not if restricted in application
to narrow classes of objects, or included in the
signification of other basic color terms. They
proposed that these color words act as focal
points for all the basic color categories in all the
languages with a developed color vocabulary. In
the last decade, the restriction to eleven universal
basic colors has been put into question. Some
researchers provided evidence on the existence
of an additional basic term in the category of
blue for the Greek, Turkish, Russian and, more
recently, Maltese languages [2-6]. In the same
line, our work [7-15] has emphasized the need
for the Italian speakers to assign the term
corresponding to light blue, azzurro, to a separate
category. In particular, in the constrained color
naming experiment described in [7] participants
were instructed to name samples of colors using
only the eleven basic terms identified by Berlin
and Kay. After performing the experiment many
participants reported the uneasiness on using
the term blu (blue) to label colors in the lighter
blue portion of the spectrum. In addition, the
8
lightness of the centroid for the blu category was
well above the lightness of the category focal.
In order to disambiguate the role of linguistic and
perceptual mechanisms in color categorization
and to shed light on the existence of the twelfth
category in Italian, other experiments were
performed and the outcomes of these confirmed
our conjecture. Here we report on both an
unconstrained color listing experiment and a
Stroop test with (in)congruent word/ink parings
[8, 16]. We refer to [7-15,19,20] for additional
information.
This paper is organized as follows. Section 2
describes Experiment 1 (color listing) and briefly
summarizes the outcomes of Experiment 2
(Stroop test). Section 3 illustrates the results and
Section 4 presents the conclusions.
2. METHODS
2.1 EXPERIMENT 1: COLOR LISTING
The color listing experiment was designed
to determine whether there is a measurable
difference in the spontaneous recall frequency
of names associated with basic colors with
respect to other color categories. In this way, the
recall frequency can be considered as a further
indication of the semantic difference between
basic and non-basic colors and thus of the
“basicness” of a given color name. Accordingly,
the recall frequency for the term azzurro was
assessed by a color listing experiment.
Cultura e Scienza del Colore - Color Culture and Science | 04/15
A questionnaire was prepared and submitted to
the informants participating in the experiment. It
consisted of two parts. In the first, demographic
data were requested in order to enable
the identification of other possible factors
influencing the recall rate (e.g. gender, age).
The second part was devoted to the color listing
task. The requested personal data were: gender,
age, education, job, hometown and knowledge
of foreign languages or Italian dialects. The
participants were also asked whether they had
particular involvement in color management in
their everyday life (either for their professional
activity or in their spare time). Thirty-nine people
aged between 15 and 62 volunteered for the
experiment. All of them were native Italian
speakers. The task consisted of listing all the
known color names freelyand without constrains
on the type and number of names provided.
There was no time limit and response time was
not recorded.
2.2 EXPERIMENT 2: STROOP TEST
The Stroop test brought a new argument in
favor of the weak relativity hypothesis providing
additional evidence of the existence of at least
a twelfth color category in the Italian language.
The Stroop test was introduced in 1953 by
John Ridley Stroop [16] and is based on the
assessment of the reaction time in a color
naming task. Basically, color name and color ink
pairs are shown in either congruent (color name
corresponds to ink color) or incongruent (color
name and ink color are different) parings. Stroop
demonstrated that in the second case (e.g. red
ink and green word) the naming of the ink color
takes longer and is more prone to errors than in
the first (green ink and green word).
The aim of the experiment was to assess
whether naming the azzurro (light blue) ink
color would take longer than naming the blu
(dark blue) ink color if both were paired to blu
color word. Five experiments were performed
for assessing the reaction times in different
congruent/incongruent conditions using different
combinations of color names and ink color
parings in order to avoid the interference from
other factors possibly biasing the results of the
experiment. In particular, the linguistic nature of
the mechanism was investigated by separately
performing the experiment on the verde (green)
and the blu (blue) categories, respectively. In the
first, regarding the blu category, six ink colors
were used: dark blue, light blue, red, yellow,
purple and pink. The six colors were paired
with four colors names rosso, (red), blu (blue),
viola (purple) and giallo, (yellow), resulting in 24
possible combinations (e.g. red ink – rosso word,
red ink – blu word etc.).
In the second experiment, regarding the green
category, the two blue ink colors were replaced
by two green colors that were equidistant in
lightness and the word blu was replaced by
the word verde, while keeping all the other
conditions unchanged. The choice of green was
motivated by the fact that the perceived colors
corresponding to the green category cover a
large portion of the spectrum including both high
and low lightness values, as it is the case for the
English blue.
The underlying hypothesis was that statistically
significant mean reaction time difference for
low and high lightness ink colors when paired
to the congruent color word (blu and azzurro,
respectively, for the blue category and verde
for the green category) would have provided
evidence in favor of the predominance of a
linguistic rather than a perceptual mechanism.
Alternative: The underlying hypothesis was that
low and high lightness ink colors, when paired
with incongruent color words (blu and azzurro,
respectively, for the blue category and verde for
the green category) would provide statistically
slower mean reaction times than those for the
congruent pairs, thus supporting a linguistic
rather than perceptual mechanism.
In other words, our claim was that if the
reaction time was the consequence of a
predominantly perceptual effect, the same
trend would have been observed for the two
categories. Conversely, a different trend would
have supported the linguistic hypothesis, since
in this case the blu and verde categories would
have revealed a different underlying linguistic
mechanism affecting reaction time.
The third, fourth and fifth experiments were
aimed at avoiding the influence of other
confounding factors. The Munsell coordinates
and the lightness of the colors used were as
follows: red 7.5R 5/20, L*=51.85, dark blue 5PB
1/10, L*=12.63, light blue 5PB 6/14, L*=60.91,
yellow 5Y 9/12, L*=89.28, purple 2.5P 3/18,
L*=30.64, pink 7.5RP 7/10, L*=44.61, dark
green 10GY 1/10, L*=13.14, light green 10GY
6/14, L*=61.38. We refer to [8] for more details.
3. RESULTS
3.1 EXPERIMENT 1: COLOR LISTING
Results confirmed the psychological salience
of the 11 basic color terms. As shown in Table
1, the basic terms occupy the first positions
and reach very high response frequencies.
The bianco (white) category was named by all
39 participants, while the basic term recalled
with the lowest frequency (33 participants) was
viola (purple). Interestingly, the word chosen to
designate light-blue was azzurro, which at that
time was not considered to be a basic term,
reaching frequencies similar to those of other
basic terms (35 participants). This supports the
hypothesis that Italian speakers do need a term
04/15 | Cultura e Scienza del Colore - Color Culture and Science
9
denoting that particular area of the color space
and azzurro seemed to be the best candidate.
Beyond azzurro, other terms denoting
approximately the same region reached good
frequencies, for instance celeste and turchese.
After viola there was a sharp variation in the
naming frequencies. The total number of
provided names was 136. The 12 colors ordered
according to decreasing frequency of recall were
white bianco (white), rosso (red), giallo (yellow),
nero (black), verde (green), marrone (brown), blu
(blue), rosa (pink), arancione or arancio (orange),
azzurro (light blue), grigio (grey) and viola (purple).
The light blue color comes in this list before two
Table 1 - Frequency recall of colors
Color Name
bianco
rosso
giallo
nero
verde
marrone
blu
rosa
arancio/arancione
azzurro
grigio
viola
lilla
(giallo) oro
(grigio) argento
beige
fucsia
(giallo) ocra
celeste
turchese
indaco
bordeaux / bordo’
(rosso) magenta
(rosso) porpora
Blu oltreoceano-mare/oceano
terra di siena (bruciata)
verde smeraldo
(bianco) panna
verde acqua
(rosa) salmone
giallo limone
amaranto
ciano
ciclamino
avorio
(grigio) antracite
rosso carminio
bronzo
(verde) petrolio
pesca
verde pisello
blu notte
vinaccia
cremisi
nocciola
verde acido
Num.
39
37
37
37
36
35
35
35
35
35
34
33
17
17
16
15
15
14
13
12
12
12
10
10
8
8
7
7
6
6
6
6
5
5
5
5
5
5
4
4
4
4
3
3
3
3
Color Name
giallo canarino
vermiglio
rosa cipria
verde oliva
(rosso) mattone
grigio perla
ecru
senape
blu di prussia
rosso scarlatto
bruno
rosa carne
prugna
acqua marina
ghiaccio
cobalto
sabbia
(verde) cromo
giallo paglierino
verde mela
grigio topo
crema
verde militare
verde prato
verde marcio
giallo arancio
aragosta
rosso vivo
rosso pompadour
bruno giallo
blu di sevres
blu cina
pervinca
verde foglia
verde cinese
verde imperiale
verde nero
verde giallo
verde ftalo
porpora rosa
ocra rossa
verde reale
verde mirto
azzurro marino
verde fluorescente
verde pino
basic colors, gray and purple, which highlights
its psychological saliency in the Italian language.
Interestingly, for other very wide regions of the
color space, like that for green, just one basic
term seems to be sufficient.
In this experiment almost every subject (90%)
10
proposed an alternative name for the blue region
(beside blue). Interestingly, this was not the case
for green (48% only). Moreover, the additional
terms proposed for blue were mainly simple
terms with high consensus, while the terms for
green were compound terms denoting objects
and reaching very low frequencies (about 30
such names were provided).
As can be seen in the table 1, agreement on an
additional term denoting the green region was
not reached, in contrast to the case of the blues.
There are no simple names widely implemented
and known, like azzurro, that might be used in
order to enlarge the lexicon referring to the green
Num.
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Color Name
bianco sporco
corallo
rosa antico
rosso sangue
bianco perla
rame
verdemare
grigioverde
topazio
giada
carta da zucchero
terra
lavanda
violetto
grigio fumo
blu aviazione
rosa pallido
verde muschio
verde vescica
verde montano
inox
ottone
nero grafite
nero di seppia
bianco di zinco
blu ginepro
rosso bandiera
rosso rubino
giallo di napoli
verde bandiera
verde vagone
blu navy
khaki
testa di moro
grigio polvere
castano
verde bosco
granata
castagno
pero
ciliegio
caramello
menta
giallo vivo
Num.
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
region. This is probably why the few participants
who used lightness modifiers (chiaro and scuro light and dark) applied them to the verde term.
A discussion on the existence of a twelfth
color class was initiated by Boynton and Olson
(B&O) in [17] and reconsidered by Sturges
Cultura e Scienza del Colore - Color Culture and Science | 04/15
and Whitfield (S&W) in [18]. Although our
experimental procedure and method were
different, their main findings were confirmed as
detailed in the following sections. Interestingly,
the same conclusion was supported by
experimental evidence obtained in a purely
linguistic framework.
Comparison with B&O and S&W
Basic color terms: The psychological saliency of
the eleven basic color terms was confirmed by
the experiment. Recall frequencies drop from
84.6% (last basic term) to 43.5% (first nonbasic term). The most important difference with
respect to S&W was that in our experiment a
twelfth potential basic color term had appeared
(azzurro). This is most probably due to the
cultural difference (Italian versus English). In
[17,18] the authors foresaw the existence of
a twelfth basic color for the English language
based on the finding that “cream” (B&O) and
“peach”. (S&W) obtained low response times and
a fairly high consensus with respect to the other
non-basic color terms. These two hypotheses
were weakened by two factors: (i) strong
evidence in the data was missing (response
time and consensus), and (ii) the data led to a
different indication for the missing name, which
is supposed to be unique.
On the contrary, the current experiment has
provided a clear indication from the linguistic
point of view, since the name azzurro has a
higher recall frequency than other basic terms.
On top of this, such a term, corresponding to the
light blue region, is in line with recent findings
regarding other languages like Greek, Turkish,
Russian and Maltese [2-6]. It is noteworthy that
Berlin and Kay proposed a hierarchy in color
names that could in principle enclose such a
term without losing generality.
The different paradigm used in our experiment
highlights another important difference
concerning achromatic colors. B&O did not
report on achromatic colors because of their
poor representation in the OSA-UCS space. For
S&W achromatic colors appeared in the last
positions (in terms of consensus and response
time) if compared to the other basic color terms.
In our experiment, bianco (white) and nero (black)
lie at the top of the basic color terms (in terms
of frequency of recall), in agreement with the
hierarchy proposed in by Berlin [1], while this
was not the case for B&O and S&W. Even more
interestingly, the six Hering primaries (red,
green, blue, yellow, white, black) appear shortly
after, followed by the other basic color terms
(gray appears in the last position). We consider
this as a strong indication that our results have a
solid psychological correlate that might include
perception and that seems to be stronger than
for the other considered studies.
Non-basic color terms: The results of the three
studies agree in this respect and highlight
similar findings despite the different paradigms
and languages. An interesting point is that nonbasic terms with the highest recall frequencies
are the same as those in the other two studies.
They seem to refer to the regions where basic
color terms are rarely used, namely those
between white and brown/yellow (“cream,
mustard, beige” for S&W, oro, beige, ocra in our
experiment) and between white and purple/red
(“lilac, violet, burgundy” for S&W, lilla, fucsia in
our experiment).
Overall, our results provide a clear indication
towards the linguistic point of view, since the
name azzurro features a higher recall frequency
than many other basic terms, while confirming
the saliency of the eleven basic color terms.
Impact of other factors
Gender - Several studies have shown that women
tend to produce and use more color names than
men. We tried to investigate the existence of a
correlation between the participants’ gender and
the number of terms provided. Our participants
were 11 males and 28 females. The average
number of terms named by males was 22.63,
against 20.42 for females. In order to better
investigate the issue, the standard deviation of
the parameter was measured, resulting in 9.11
for males and 9.47 for females. The distribution
of the values in the two groups is quite similar.
The results of our experiment show analogous
numbers of terms provided regardless of gender.
However, a gender-balanced sample would have
been required for robust conclusions. Statistical
significance?
Age - Participants were aged between 15 and
62. A correlation between age of participants
and number of terms provided was observed.
The participants were organized into three age
groups (<30; 30-45; >45). While the first and
the last ones provided approximately the same
number of color names, the other one (30-45)
provided a significantly lower number of terms.
You have to include the p value.
Color experience - Participants were also divided
into two groups according to their involvement
in color matters. One group included people
who have had someinvolvement in color
(regardless of the involvement in nature), while
the second group included people who have
not. Participants who reported an involvement
provided a significantly higher number of color
names. Results are summarized in Table 2.
Finally, the recall frequency for non-monolexemic
names using lightness modifiers is given in
Table 3, where chiaro stands for light, scuro for
dark, and vivo for vivid.
Overall, the experiment confirmed the special
status of the 11 basic color terms, but also
04/15 | Cultura e Scienza del Colore - Color Culture and Science
11
Table 2 - Mean and standard deviation
of frequencies over gender, age and
color experience.
Table 3 - Number of recalls of
compound names using a lightness
modifier. Chiaro stands for light, scuro
for dark, vivo for vivid.
Gender
Age
M
F
<30
30-45
>45
Si
No
µ
22.63
20.42
21.23
17.07
25.33
26.63
15.75
σ
9.11
9.47
8.4
6.7
12.22
10.22
3.68
Lightness modifiers
verde
chiaro
verde
scuro
marrone
chiaro
marrone
scuro
grigio
chiaro
bruno
scuro
blu
chiaro
blu
scuro
giallo
chiaro
giallo
scuro
rosso
vivo
giallo
vivo
5
2
2
2
1
1
1
1
1
1
1
5
provided evidence of the need of a twelfth color
name, in the Italian language, at least: azzurro.
3.2. EXPERIMENT 2: STROOP TEST
Results support the hypothesis that two blue
basic color terms do exist. The dark and light blue
colors used in the experiment, having the same
hue but different lightness, appear to belong to
two different color categories. A Student’s t-test
(p<0.05) showed that for every possible pairing
(color ink and color word) the blu color word had
significantly shorter reaction times when paired
with dark blue ink than in all other conditions
(Fig. 1, left plot). This means that the participants
name more quickly the dark blue rather than the
light blue ink color if both are paired with the blu
color word.
In the control experiment, the t-test for each
possible combination of stimuli (ink color and
color word) showed that the color word verde has
significantly shorter reaction time when paired
either to light or dark green colors than in any
other condition (Fig. 1, right plot).
4. CONCLUSIONS
Figure 1 - Average reaction time for
the blu (on the left) and verde (on the
right) term.
12
Color Experience
Our studies brought evidence in favor of the
hypothesis of the existence of a twelfth basic
color class in the Italian language in the light
blue (perceived) color range named azzurro, as
well as in its linguistic reference to perceived
colors. Both the color listing and the Stroop
tests supported such a conjecture. No other
factors (age, gender, color experience) resulted
in having a significant impact on the outcomes
of the two experiments. However, this issue
deserves further investigation.
BIBLIOGRAPHY
[1] Berlin, B., Kay, P.: Basic color terms: their universality
and evolution. Berkeley, CA: University of California Press
(1969).
[2] Androulaki, A., Gômez-Pestaña, N., Mitsakis, C., Jover,
J., Coventry, K., Davies, I.: Basic colour terms in Modern
Greek: Twelve terms including two blues. Journal of Greek
Linguistics 7(1), 3–47 (2006).
[3] Özgen, E., Davies, I.: Turkish color terms: Tests of
Berlin and Kay’s theory of color universals and linguistic
relativity. Linguistics 36(5), 919–956 (1998).
[4] Paramei, G.: Singing the Russian blues: an argument
for culturally basic color terms. Cross-Cultural Research
39(1), 10 (2005).
[5] Winawer, J., Witthoft, N., Frank, M., Wu, L., Wade, A.,
Boroditsky, L.: Russian blues reveal the effect of language
on color discrimination. PNAS 104(19), 7780–7785, (2007).
[6] Borg A. Towards a diachrony of Maltese basic colour
terms. New Directions in Colour Studies. Biggam CP,
Hough CA, Kay CJ, Simmons DR, editors. Amsterdam/
Philadelphia: John Benjamins; (2011).
[7] Paggetti G., Bartoli G., Menegaz G., Re-locating colors in
the OSA space, Attention, Perception and Psychophysics,
vol. 73, n. 2, pp. 491-503 (2011).
Cultura e Scienza del Colore - Color Culture and Science | 04/15
[8] Paggetti, G. Human Perceptual Factors in Imaging: A
Link Between Cognitive And Computer Sciences, Doctoral
dissertation, Doctoral course in Computer Science,
University of Verona (2011).
[9] Paggetti, G., Menegaz, G.: Is light blue (azzurro)
color name universal in the italian language? Color and
Reflectance in Imaging and Computer Vision Workshop,
International Conference on Computer Vision. Crete (Sept
2010).
[10] Paggetti G., Menegaz G., Exact location of consensus
and consistency colors in the OSA-UCS for the italian
language, Color Research and Application, vol. 8, n. 6, pp.
437-447 (2013).
[11] Paramei, G. Menegaz, G. ‘Italian blues’: A challenge
to the universal inventory of basic colour terms, Colore
e colorimetria, Santarcangelo di Romagna (RN), Maggioli
Editore, Proceedings of “IX Conferenza del Colore”,
Firenze, Sept. 19-20, pp. 164-167 (2013).
[12] Paggetti G.; Menegaz G., Locating colors in the Munsell
space: an unconstrained color naming experiment,
Perception, Proceedings of the European Conference on
Visual Perception (ECVP), Alghero, Italy, Sept. 1-6 (2012).
[13] Paramei, G. Menegaz, G., Italian Blues: A challenge
to the universal inventory of basic colour terms, Journal
of the Internatioal Color Association , vol. 13 pp. 27-35
(2014).
[15] Valdegamberi, V., Paggetti, G., Menegaz, G. On the
perceptual/linguistic origin of the twelfth basic color
term in the Italian color lexicon, Colour and Colorimetry
Multidisciplinary Contributions eds. Maurizio Rossi,
Maggioli, Proceedings of VII Conferenza Nazionale del
Colore, Roma, pp. 291-298, Sept. 15-16 (2011).
[16] Stroop, J.: Studies of interference in serial verbal
reactions. Journal of experimental psychology 18(6),
643–662 (1935)
[17] Boynton, R., Olson, C.: Locating basic colors in the
osa space. Color Research and Applications pp. 224–235
(1987)
[18] Sturges, J., Whitfield, T.: Salient features of munsell
colour space as a function of monolexemic naming and
response latencies. Vision Res. 37(3), 307–313 (1997)
[19] Sandford, J. L. Blu, azzurro, celeste - What color
is blue for Italian speakers compared to English
speakers?,Proceedings of the Eighth National Color
Conference. Gruppo del Colore, Alma Mater Studiorum
Università di Bologna, Facoltà di Ingegneria, Bologna,
Italy, 13-14 settembre 2012. Colour and Colorimetry.
Multidisciplinary Contributions. Vol. VIII B, (ed.) Maurizio
Rossi – Dip. Indaco – Politecnico di Milano, Santarcangelo
di Romagna: Maggioli Editore. pp. 281–288 (2012)
[20] Menegaz G.; Paggetti G., Is the azul class unique in
the Spanish language?, Perception, vol. 40, ECVP Abstract
Supplement, 2011, pp. 80-80
[14] G. Paggetti, G. Menegaz, G. Paramei, Color naming in
Italian language, Color Research and Application, pp. 1-14,
DOI:10.1002/col.21953, (2015).
04/15 | Cultura e Scienza del Colore - Color Culture and Science
13
Walter Arrighetti, PhD
[email protected]
1
CTO | Frame by Frame Italia
1
Motion Picture Colour Science and
film ‘Look’: the maths behind ACES
1.0 and colour grading
1. INTRODUCTION
One of the biggest colour-related problems
for the film production and post-production
industry is two-fold: to ensure that the creative
“look” of video content, as envisioned by the
cinematographer, is preserved throughout ([1]–
[2]), and to be able to consistently reproduce
this ([1], [3]). That also has to be independent
both on digital cameras or computers generating
and animating it (as input), and on finished asset
specifications for the end-users to watch and
enjoy it (as output) — be it either in a dark
digital cinema theatre, at a home TV setting,
or using a Video-on-Demand (VoD) or Internetstreaming application, in a day-lit room or even
open sunlight.
In recent years many proprietary/commercial
tools and workflows emerged, each driven by
specific, not always cross-compatible needs (e.g.
on-set grading, Digital Cinema mastering, VoD,
etc.). This results in proliferation of a plethora of
different formats vs. the scarce number of really
interoperable standards.
The author has put lots of efforts to provide a
unified mathematical formalism and usability to
most of the colour-management technologies
used in the post-production world, both on
independent publications and in collaboration
with several entities in the business (like
the SMPTE and the AMPAS). After a minimal
introduction to such colour-mathematical
terminology ([4]–[6]) and ColorLUTs, two brand
new colour-management techniques from highprofile moving-picture digital imaging (CDLs and
ACES) will be described, as they aim at colour
interoperability for the analysis and synthesis of
digital ‘looks’, both on-set (production) and along
the Digital Intermediate (DI) phase.
ACES in particular, which the author has
been active contributor to since 2012, is an
Academy-originated initiative for facilitating
colour interoperability across the Media &
Entertainment industry.
14
and superficially-connected m-dimensional
domain—often even a convex one), with dim
L(Γ )=n. Let the input and output spaces be
both RGB model (m=n=3) and their canonical
bases be the left-handed triple {r,g,b}, so
any input colour c∈IR3 is coordinated as
c=rr+gg+bb and, for the input regular RGB
cube, (r,g,b)∈[0,1]3. The output colour is thus
L(c)=Rr+Gg+Bb; by the Hodge-Helmholtz’
theorem, the orthogonal decomposition holds
([4]–[5]):
L(c) = R (r , g , b)rˆ + G (r , g , b)gˆ + B (r , g , b)bˆ
= T(c) + H (c) = ∇χ (c) + ∇ × η(c) + l
2. COLOUR SCIENCE
MATHEMATICAL FORMALIST
where T(c) and H(c) are the conservative
(curl-free) and the solenoidal (divergence-free)
parts of the colour map, each derived from
a potential field — a scalar one χ(c) for the
former, and a vector one η(c) for the latter.
Due to simple connectedness of Γ, no harmonic
component is present in the above: the constant
‘lift’ term l represents an overall colour bias
(either neglected or incorporated into T) for
chromatically-additive colour models like sRGB,
as well as cie XYZ and dci X'Y'Z'.
The gradient of the gamut mapping can also
be considered, which is a more complete (and
complex) mathematical object, called a (n,m)tensor field, [4], depending on both the source
m channels and the target n channels. T is
the generalized tonal mapping, or transfer
characteristics which, in RGB spaces, models
overall colour correction (incl. lightness and
saturation changes). H is the field describing
local colour-component cross-talks and global
hue shifts. Notably, T field too may incorporate
hue shifts, especially for those colours c, where
the inter-channel ratios are not preserved, i.e.
R(c) : G(c) : B(c) ≠ r : g : b.
Colour-correction languages often use the lumachroma colour model (e.g. the La*b*, Y'UV,
Y'cBcR and Y'cX'cZ' spaces), or the cylindrical
colour model (e.g. the HSL space). In the case
of HSL for example, the author usually suggests
joining hue h and saturation σ together into
a complex parameter called chroma ς and
defined as, [5]:
A gamut mapping between colour spaces ([5]–
[7]) is a vector field L(c), where c∈G is the input
colour in the source gamut G⊆IRm (which is, to
every practical aspects, a connected, linearly-
(where arctan2 is the secondary arc-tangent,
which is reminiscent of quadrant allocation
Cultura e Scienza del Colore - Color Culture and Science | 04/15
for b* and a* and commonly found in all the
programming languages).
3. COLOUR LOOK-UP TABLES,
AKA COLORLUT,
AKA CLUT, AKA SIMPLY 'LUT'
While the above formalism is useful for technical
operations like colour space conversions and
“overall” colour corrections not done on a sceneper-scene basis, more complex transformations
may be needed, especially when creative intent
is included, [8]–[10]. In this case a closedform formula for the transform might not exist;
interpolation, though existing in principle, might
not be computationally compatible with the
creative need of real-time playback of highresolution (often uncompressed) image/video
files (essential for evaluating and creating the
so-called grades). For this reason the continuum
formalism may be well abandoned at this stage,
and replaced by a ColorLUT (colour look-up
table, or clut, [6]) which is a discrete-mapping
representation of it on a finite, m-dimensional grid
of N points per input colour channel, with each
point being a n-tuple in the output colour space
(e.g. in the shape of a N×N×N RGB cube). It is
an explicit mapping between a sample of input
code-values into output code-values (which
may or may not act between the same colour
space), while the result on intermediate source
colours is obtained by interpolation [3]: for this
reason a clut can be also used to approximate
continuum formulae as those for mapping a
colour space into another (e.g. from a RGB one
like Rec.709, [10], to cie XYZ, cfr. Fig. 2c). A
type of clut approximates the mapping channel
by channel (therefore called 1d-lut, or colour
curve in different contexts); another type acts
as a full orthogonal sampling of input colours;
the latter —because it usually maps between
3-channels colour spaces— is specifically
called a 3d-lut whereas, mathematically, it is a
discrete n-dimensional vector field L(s), where
s∈Γ is the input colour codevalue of the source
m-channel gamut Γ⊆IRm (cfr. several samples in
Fig.1, where m=n=3).
The reason why the clut implementation is so
widely used is manifold: first of all, provided
the appropriate density of N input value per
channel is used (17×17×17 samples in Fig. 2
is a common, but yet not enough coarse-grained
choice), it can represent any non-linearities in
the colour transform (accounting from the most
complex primary colour corrections, up to a
35mm film’s dye cross-talk, as is the case for
3d-luts). Secondly, it is implemented via simple
(and usually linear) interpolations on the other
non-sampled colours, fairly scaling with the clut
size N, and has therefore a smaller footprint in
terms of CPU power and memory size needed to
“run” the algorithm, than applying more complex
mathematical formulæ.
Third, a clut can hardly be interpreted only
by specific software able to read its encoding
and is useful only on specific picture(s) it was
intended for: quite a black-box ingredient for the
motion-picture recipes. The latter aspect may
have been advantageous in the past, but is now
mostly a downside, when cinematographers,
colourists and VFX artists really need to transfer
colour corrections from the on-set pre-grading
sessions throughout the whole post-production
pipeline, up to the theatre room, and capable of
doing so in the most advantageous and, above
all, interoperable way (cfr. §5). Moreover, lots of
workflows with so different and “undisciplined”
uses of cluts exist —be it either for technical
and creative intents— such that no generalized
use can be made of a clut as long it is tailored
for a specific project. It is hard to invert (i.e. to
“reverse-engineer”) the mathematical operations
baked into a clut, especially for post/VFX
labs which do not enforce a thorough colour
management across their pipelines. That is even
worsened when materials from different sources
(camera makes, film emulsions, CGI rendering,
…) come all together in place.
Discrete-calculus tools allow the extraction of
quantities essential for the analysis or synthesis
of a colour transformations: estimating colour
differences, hue shifts in degrees, boundary
wedges for evaluating Out-of-Gamut (OoG)
colours, etc..
When technical problems of higher level arise
in colour correction (e.g. colour characterization
of specific input or output devices, or proper
gamut mappings between footage with
different colorimetry), this usually translates
into more sophisticated mathematical tools to
be employed, often derived from Differential
Geometry, Harmonic Analysis and multidimensional interpolations, [4].
For example, a more careful shaping of a
tone-scale curve is usually necessary when
modelling the transfer characteristic of a nondigital device, e.g. sensor noise or the soughtafter 35mm film print emulation (FPE, cfr. Fig.2):
three control points as provided by a CDL ([11])
or a 3-way color-corrector (CC) are no more
enough and the three channel functions R(r),
G(g) and B(b) need to stay non-decreasing (i.e.
invertible). This helps better trim the effective
contrast on all the tonal ranges. When the three
functions are uneven with each other, a hue shift
inevitably occurs, as the hue is not preserved by
the same input triple (r,g,b) any more. Imposing
hue-invariance means adding constrains that
need to be correctly formulated), i.e.:
 ∂B ∂G   ∂R ∂B 
 ∂G ∂R  ˆ
∇ × L(c) = ∇ × H (c) = 
−
−
−
 rˆ + 
b = 0
 gˆ + 
 ∂g ∂b   ∂b ∂r 
 ∂r ∂g 
04/15 | Cultura e Scienza del Colore - Color Culture and Science
15
Figure 1 - Plots of the output gamut
L(Γ) of 3d luts L whose input is the
173-points RGB cube Γ: a. identity
mapping; b. colour-space conversion
between HDTV’s “Rec.709” and
Cineon Printing Density (CPD)
“logarithmic” RGB spaces; c. from
“Rec.709” (gamma γ=2.6) to Digital
Cinema (DCI) CIE XYZ colour space; d.
from CIE XYZ to DCI’s P3 RGB colour
space (γ=2.2 – notice the clipping at
the cubic gamut boundary of P3); e.
from Cineon Printing Density log. RGB
to CIE XYZ colour space; f. scenespecific creative Colour Grading LMT
including 35mm print-film emulation.
Figure 2 – Two different views of
output gamut L(Γ) of a Print-Film
Emulation (PFE) clut L engineered by
the author (Technicolor laboratories,
Rome, 2009), showing the synthesis
work done adding additional points
to the gamut of a Kodak Vision film
in order to expand its latitute prior to
35mm scanning.
16
Cultura e Scienza del Colore - Color Culture and Science | 04/15
More often simpler definitions of hue and
saturation are used though, like
1
2
sat(
=
c)
c − gb − rb − rg
2
hue(c) = arctan 2
3( g − b)
2r − g − b
which allows to have simpler analytical
properties like
 2r − g − b 
1 

∇
=
sat(c)
2g − b − r 
sat(c) 

 2b − r − g 
∇hue(c=
)
∇sat(c)
38
=
sat(c)
sat(c)
Imposing hue-invariance, means in this case
solving the algebraic equation
hue L(c) = hue (c) ⇔
G−B
g −b
=
2 R − G − B 2r − g − b
Another important constrain that is sometimes
necessary, is the existence-of-inverse condition.
This is especially important to guarantee that,
once a grade is ‘burned’ within the raster pixels,
original colours can still be recovered without
degradations.
It’s worthwhile noting that current postproduction tools only ‘burn’ a colour grade as the
last stage of the process (earlier the information
on the grades is carried along the pipeline as
metadata-only by the colour-correction software).
This is formulated as in [2]:
∂L
=
det
∂ ( r , g , b)
∂R
∂r
∂G
∂r
∂B
∂r
4. ON-SET COLOUR GRADING
AND ITS COLOUR LANGUAGE (CDL)
Since things are now shot digitally and stay
digital throughout the pipeline, one of the moviechain blocks to recently take advantage of this
is principal photography, where early colour
correction/grading can be done effectively, onset, just minutes after each clip is shot, cfr. [11],
Fig.3.
Creative colour correction (grading) information
can be transported, from clip to clip, as they are
originally shot, as a series of simple non-linear
transformations controlled by 10 parameters (3
parameters by each of the 3 RGB channels, plus
1), each representing one degree of freedom
of the creative colourist: ‘slope’, ‘offset’, ‘power’
triples, plus a ‘saturation’ parameter. A set of such
quantities, transported for a whole video asset,
cut per cut, makes up the 2014 OSCARS®winning American Society of Cinematographers’
Colour Decision List (ASC CDL) and is a well-known
example of simple mathematical equations
at the creative service of motion picture
colourists [5], [11]. This can also be re-written
by means of three functions S (slope), O (offset)
and P (power) and let s, o, p be the respective
controlling parameters with identity values 1, 0,
1 respectively:
• Slope S(c;s)=sc (CDL analogue for
colourists’ lift, despite slope fixes black
point at codevalue 0.0, whereas lift
fixes whitepoint at codevalue 1.0);
• Offset O(c;o)=c+o (CDL analogue
for colourists’ gain);
• Power P(c;p)=max(0,s)p, which
is the CDL analogue of a gammacorrection.
∂R ∂R
∂g ∂b
∂G ∂G
≠0
∂g ∂b
L(c) = (oR + sR r ) p rˆ + (oG + sG g ) p gˆ + (oB + sBb) p bˆ
∂B=
∂B
P ( O ( S (r ; sR ); oR ) ; pR ) rˆ + P ( O ( S ( g ; sG ); oG ) ; pG ) gˆ
∂g ∂b
R
G
B
+ P ( O ( S (b; sB ); oB ) ; pB ) bˆ
Figure 3 – Example of commercial
colour grading software GUI with the
main 3-way colour-corrector wheels.
04/15 | Cultura e Scienza del Colore - Color Culture and Science
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This is a non-orthogonal decomposition in 1st- and
2nd-degree polynomials (slope + offset), plus a
nonlinear function (power), thus the inner products
may not “behave” well. It can be shown however
(and this is in fact well-known practice done by
every non-mathematician colourists working
on still or moving pictures), that a sufficiently
low number of such operators, governed by a
few parameters (like weighting coefficients in
a linear combination for Linear Algebra) allow
for quite good approximation, thus leading to
orthogonal decompositions of colour operators.
c
L(c) = ∑ l k (c;θ k )
k =1
where all the 1-parameter vector fields lk are
known a priori, whereas the coefficients and
the parameters k themselves are the real
descriptors of the “look”.
5. THE ACADEMY COLOR CODING
SYSTEM (ACES)
The Academy of Motion Picture Arts and
Sciences (AMPAS)’s Science and Technology
Council has been gathering a group of variegate
experts from all the top-level production, postproduction facilities and software houses in the
industry to put forward a solution unifying such
colour management issues: ACES, [1], [12].
The reason behind ACES is the need to
particularly address the plethora of colorimetries
set by manufacturers’ digital equipment (both
image-creating and -reproducing) — even many
more so in the digital era than film processes
ever had in the past. A similar need has already
surfaced in the Digital Cinema industry: that
is why its dci X'Y'Z' colorimetry derives from
the device-independent cie XYZ colour space
([3], [10]). Unfortunately, as neither colorimetric
cameras nor monitors/projectors exist as of yet,
this colour-space choice has lead to reverting
to a one within the RGB model, which is more
practical, as well as ACES mostly pertains to TV
and moving pictures. Every colour-correction
operators in the involved pipelines (from camera
controls, to colour-grading suites, to projectors’
and TVs’ balance controls) are, in fact, RGBbased.
Version 1.0 of ACES, [12], whose project the
author has been cooperating on with the
AMPAS experts since 2012, is a framework
with centralized colour-management paradigm,
developed after many years of pre-testing
among facilities and companies in the industry,
where the image is evaluated according to its
colorimetric digital representation. Please refer
to Fig.4 for a schematic throughout this Chapter.
First of all, ACES defines AP0 and AP1: two
sets of RGB primaries for the four ACES colour
spaces. AP0, whose cie xy chromaticities are
(0.73470,0.26530) for Red, (0.,1.) for Green
and (0.0001,0.0770) for Blue. AP1 primaries’
chromaticities are (0.713,0.293) for Red,
(0.165,0.830) for Green and (0.0128,0.044)
for Blue. Both use cie D60 illuminant
(0.32168,0.33767) as white-point and physical
blackpoint at cie XYZ triple 03.
Within ACES colour pipeline the image is
considered as virtually captured by a Reference
Input Capture Device (RICD), which is an idealized
digital ‘camera’ recording in a RGB colour space
called smpte2065 after the standard that defines
it. Another important aspect is that smpte2065
is a scene-referred colour space, i.e. the codevalues represent mean relative exposures to the
one captured from a perfect reflecting diffuser
— apart from a 15% glare. In AP0, this accounts
for a normally-exposed 18% grey card acquired
by a RICD mapped to the RGB triple (.18, .18,
.18).
Any real camera imagery and colorimetry is
brought into the pipeline by means of a colour
gamut mapping called ACES Input Transform,
which basically converts all the camera’s
colorimetry into smpte2065. Currently, Input
Transforms for most of the patented, cinemagrade cameras like the ARRI alexa, the cameras
Figure 4 - Sketch of the ACES
paradigm: the original scene is
either captured by a real camera
or generated in CGI. Whatever the
source, the corresponding Input
Transform converts the codevalues into the SMPTE2065 colour
space (except for the “ideal” RICD,
which already produces SMPTE2065
pictures). Using the Output Transform
the pictures can then be transferred to
any output device, like monitors (with
any technologies), projectors, TVs, etc.
18
Cultura e Scienza del Colore - Color Culture and Science | 04/15
by RED™, the Fx5 family by Sony, the cameras
by Blackmagic Design, and the Cinema-EOS™
family by Canon, are provided; each maps
the sensor’s proprietary gamut (called ARRI
Log.C, RED.Log, S-Log/S-Gamut, BMD.Log
and CanonLog respectively), parametrized by
shooting settings like equivalent sensitivity (ISO)
or correlated colour temperature (CCT), into
scene-referred smpte2065 codevalues.
The author has also been active in Italy for
promoting the use of ACES with several
initiatives, [1], including a real-world, on-set test
to compare ACES freamework originating from
different, high profile cameras, up to a full VFX
and Digital Cinema mastering pipeline: Fig.5 is
the result of the technical photography session.
At the other end of the pipeline, smpte2065
colorimetry is converted to the gamut of the
displaying device and chromatic adaption by
means of an ACES Output Transform: among
the others there is one, for example, for Digital
Cinema mastering (dci P3) in a dark surround,
two for standard broadcast TV gamut (Rec.709),
and two for UHDTV (Rec.2020) — each having
one for a bright- and one for a dark-surround
adaption. From a Colour Appearance Model
(CAM)’s perspective, the Output Transforms
take care of the viewing environment as well:
so several Output Transforms may exist for the
same device, but under different chromatic
adaptions. All the Output Transforms have a
common first mathematical block, called the
Reference Rendering Transform (RRT). Please refer
to Fig.5 for a block-diagram of ACES version 1.0
main components.
All in all, smpte2065 space uses AP0 primaries,
has trivial transfer characteristics (i.e. it is
photometrically linear, i.e. “gamma-1.0”) and
represents the baseline for all the ACES pipeline
— and the widest gamut as well, which is
also suited for long-term archiving, cfr. Fig.6.
Codevalues are usually encoded as 16 bits/
channel floating-points (‘half-floats’ as per IEEE
754-2008 standard), and archived in a specific
frame-per-file variant of the OpenEXR file
format, cfr. [14]–[15].
It is within this space that images are mainly
worked on, with exceptions when it is technically
convenient or mandatory to use temporary, welldefined colour-spaces for specific purposes:
• ACEScc has AP1-primaries,
“logarithmic” transfer characteristic, 32
bits/channel float encoding optimized
for film-style colour correction, [13];
• ACEScg has AP1-primaries,
photometrically-linear, 16 or 32 bits/
channel integer code-values, optimized
for CG and painting applications that
scarcely support images represented
by floating-point codevalues, [16];
• ACESproxy has AP1-primaries, the
same logarithmic characteristic as
ACEScc, 10 or 12 bits/channel integer
encoding, optimized for real-time
transport of images over physical
links (e.g. the SDI cables family) that
only support integer code-values, yet
logarithmic encoding is still needed for
on-set color correction applications,
[17];
Similarly, output-device compatibility is
provided by first mapping to another ideal
Figure 5 - Real-world ACES testing to
compare ARRI Alexa XT (shown here),
RED™ EPIC and Sony F55 cameras
on the same technical set and ACES
colour space (also courtesy of DIT E.
Zarlenga).
Figure 6 - Main ACES v1.0
components in place: footage shot
in Alexa camera’s native ARRIRAW
frame-per-file format (in Log.C colour
space) is technically processed by an
Input Transform to become scenereferred SMPTE2065 colour space.
This is where color grading is applied
upon (in temporary ACEScc space).
The CDL designed during principal
photography (on-set) pre-grading is
conceptually applied above this grade,
but below optional creative-technical
transforms like PFE. The file is then
ready to be saved in ACES-compliatn
OpenEXR sequence (SMPTE2065
colour space) or can be sent to a
display device passing through an
Output Transform.
04/15 | Cultura e Scienza del Colore - Color Culture and Science
19
output Reference Display Device (RDD) via the
aforementioned RRT, and, whence, by means of
a discrete mathematical formula called Output
Device Transform (ODT), which depends on the
output colour space and, ultimately, on the output
device’s transfer characteristics (e.g. monitors,
projectors, printers, D-Cinema devices, etc.).
ACES images are stored in frame-per-file
ordered sequences, encoding each frame as a
OpenEXR file [14], together with ACES-specific
metadata optionally written as well in a “sidecar”
XML file called ACES clip-container.
Ideally, any sensitive colour operation (both for
technical and creative intent) should take place in
either the smpte2065 or the ACEScc colour spaces
(which act like a PCS in the ICC paradigm), where
any operator acts unambiguously. Creativeintent operations, in particular, are stored in the
so-called Look Modification Transform(s) (LMT),
which are applied before the Output Transform.
6. CONCLUSIONS:
THE COLOUR 'LOOK' OF A FILM
Several professionals in the video, postproduction and DI world, as well as colour
scientists and vendors, have long tried to define
what technically “look” means in this context. In
the author’s opinion, science, experience and
common practices can sum up together to the
statement that currently a “look” is the ensemble
of creative colour decisions made for a specific
set of scenes (e.g. scenes shot to represent the
same lighting and dramatic situation, if not even
possessing location/temporal unity) that neither
pertain the technical properties of the colours
themselves nor the devices/media used to
reproduce them. In this sense “look” is different
from “film look”, as the latter also includes colour
characteristics due to combination of a film’s
emulsion, development and printing processes
(which can of course be emulated).
A look applied to two differently-exposed and
-coloured scenes not to chromatically match
them (that’s what the same-name phase of a
colour correction session is about), but rather
to give both the same visual impact, in the
director’s and/or cinematographer’s minds,
the incisive ‘colour fingerprint’ unique to that
specific product and cinematography; the ‘look
development’ phase has therefore been starting
earlier and earlier in the production phase, up
to taking place on-set, with the help of proper
pre-grading workflow (e.g. the one proposed by
Technicolor DI supervising colourist Peter Doyle
for Harry Potter VII, Dark Shadows and Paddington
full-feature films).
The lack of interoperable schemes, incompatible
file formats and colour operations —even a
unified terminology— has prevented many
colour manipulations from currently happening,
or at least made this much more difficult and
prone to errors or lack of precision. This cannot
be any more delayed since all film processes
have turned completely digital, and this is
where the author’s contributions have been
focusing on in the latest years. Unifying the
post-production terminology and mathematical
Figure 7 - Chromaticity comparison
between ACES (SMPTE2065) gamut
other well-known RGB colour spaces.
20
Cultura e Scienza del Colore - Color Culture and Science | 04/15
formalism (which were traditionally tied up to
different post-production laboratories’ own film
processing technologies and manufacturers’
secret sauces) means creating a common
baseline to start from and communicate with
mainstream Color Science. ACES is another step
up in the attempt to create common processes
and workflow to ease interoperability and help
to future-proff archival footage. Of course all of
this is a process, therefore it is not meant to be
set in stone but rather continually progress as
new technologies, methodologies and, above all,
creative eyes and minds turn up bringing along
their expression techniques and visions — for
this process to drive them all along together.
[7] S. Westland, C. Ripamonti, Computational Colour
Science using MATLAB. Wiley, 2012.
BIBLIOGRAPHY
[13] ACEScc, a Logarithmic encoding of ACES data for use
with Color Grading systems, Specification S-2014-003,
Academy of Motion Picture Arts and Sciences (AMPAS),
v1.0, Dec. 2014.
[1] W. Arrighetti, “The Academy Color Encoding System
(ACES) in a video production and post-production colour
pipeline”, Colour and Colorimetry, XI(B), Maggioli, 2015.
[2] W. Arrighetti, “New trends in Digital Cinema: from onset colour grading to ACES”, at CHROMA: workshop on
colour image between motion picture and media, chroma.
di.unimi.it, Sept. 2013
[3] W. Arrighetti, “Colour Management in motion picture
and television industries”, Colour and Colorimetry, VII(B),
63–70, Maggioli, 2011.
[4] W. Arrighetti, Mathematical models and methods
for Electromagnetism in Fractal Geometries, Ph.D
dissertation, Sapienza University of Rome, Rome, 2007.
[5] W. Arrighetti, “Colour correction calculus (CCC):
engineering the maths behind colour grading”, Colour and
Colorimetry, IX(B), 13–19, Maggioli, 2013.
[8] Colorimetry, Second Edition, Commission Internationale
de L’Éclairage, Publication 15.2, 1986.
[9] M. Petrou, C. Petrou, Image Processing: the
fundamentals, Wiley, 2012.
[10] C. Poynton, Digital Video and HD: algorithms and
interfaces, Morgan-Kaufman, 2012.
[11] A.B. Benitez, L. Blondé, B. Lee, J. Stauder, H. Gu., “ASC
CDL: a step towards Look Management”, Proceedings of
IBC 2007.
[12] Academy Color Encoding Specification (ACES),
Society of Motion Picture and Television Engineers
(SMPTE), Standard 2065-1, 2012.
[14] OpenEXR File Layout, OpenEXR working group, www.
openexr.com, Apr. 2007.
[15] ACES Image Container file layout, SMPTE, Standard
2065-4, 2013.
[16] ACEScg, a working space for CGI render and
compositing, S-2014-004, AMPAS, v1.0, Dec. 2014.
[17] ACESproxy, an Integer Log encoding of ACES image
data, S-2013-001, AMPAS, v2.0, Dec. 2014.
[18] F. Pierotti, “The colour turn: l’impatto digitale sul
colore cinematografico”, Bianco e nero, LXXV(580), 26–34,
Carocci, Rome, Sept.–Dec. 2014.
[6] W. Arrighetti, “Moving Picture Colour Science: the
maths behind Colour LUTs, ACES and film ‘Looks’”, Color
and Colorimetry, VIII(B), 27–34, Maggioli, 2012.
04/15 | Cultura e Scienza del Colore - Color Culture and Science
21
Jodi L. Sandford
[email protected]
1
Department of Letters,
University of Perugia
1
A Cognitive Linguistic Usage Perspective:
What is Italian Blu, azzurro, celeste?
Do English agree on BLUE semantics?
1. INTRODUCTION
The objective of this paper is to ascertain
contemporary Italian linguistic categorization
of the macro-color concept BLUE, and compare
the results to English interpretation of the same
tasks. Native Italian speakers affirm that they
habitually use three blue color terms: blu, azzurro,
and celeste; often idealizing azzurro over blu, as
being “more Italian”. I propose that according to
the task results contemporary blu [blue] is the
more primary and deeply entrenched basic
color term (henceforth BCT); azzurro [azure light blue] could also be a BCT, but should be
considered a secondary BCT; and celeste [sky
blue - pale blue] is a subordinate color term.
English interpretation of the same color object/
concept associations used in this questionnaire
is different due to the lack of a second English
monolexemic basic BLUE color term and
to the difference in culturally specific BLUE
color term collocations. What are the Italian
blue term semantic relations? Do azzurro and
celeste violate the criterion that a BCT not be a
hyponym of another color word, i.e., blu [1]?
Does the semantic relation between the terms
blu and azzurro respond to the cognitive need to
differentiate between the colors of the sky and
the water? Is the principal task color term object/
concept association, based on the cognitive
linguistic approach to linguistic entrenchment,
an original valid method to measure basicness?
Various verification measures of basicness are
employed to answer these queries.
2. BACKGROUND
The spectrum may be partitioned into different
color terms according to language and the
corresponding culture. Color lexemes evolve
dividing the color space into more specific
semantic concepts. Current research, since
Berlin and Kay, generally considers BLUE to be
the last primary BCT to emerge in language.
Some languages present a variation of BLUE
lexicon; often displaying two distinct blue terms;
one for a generic BLUE -primary BCT- and one for
a more specific BLUE -secondary BCT- in English
recognized as a tonal variation of “blue”. The
possibility of there being a twelfth BCT has been
theorized by [1][2], among others. Researchers
have proposed a twelfth BCT as being another
“tone of blue” — also referred to as dual
22
lexicalization of BLUE — in different languages
e.g., Italian, Maltese, Greek, Polish, Russian, and
Turkish, [3][4][5][6][7][8]. BCT criteria are listed
as: i. it is monolexemic, ii. its signification is not
be included in that of any other BCT, iii. it must
not be restricted to a narrow class of objects, iv.
it must be psychologically salient, i.e., occur at
the beginning of elicited lists of color terms, and
have stability of reference across informants
and occasion of use, v. if doubtful it should
have the same distributional potential [1]. The
specific BCT criteria of interest this study are:
iii, it not be restricted to a narrow class of
objects, (the occurrence in a large number of
domains reveals the degree of entrenchment),
and the problematic ii. its meaning must not
be hyponymic (see Glossary for a definition of
several terms).
The definition of each color term provides an
initial idea of what they are understood to mean,
and in what contexts the meaning can vary. In
some dictionaries [9] [10] blu is defined as a
dark azzurro, and is used with the expressions:
cielo blu [blue sky], mare blu [blue sea] | avere
sangue blu [have blue blood]; though blu is
translated as blue, dark blue, and navy blue. Blu
is more productive, BLUE compounds in Italian
are currently constructed with blu + another
term, e.g.; blu marina [navy blue], which of
course do not appear separately in dictionary
entriesi. There are only three entries of blu
lexemes in the Italian dictionary [11]. Azzurro is
defined as the color of a clear sky, somewhere
in between celeste and turchino; translated
with blue, light blue, azure, sky blue; with
expressions such as occhi azzurri [blue eyes]
| principe azzurro [the ideal groom, a prince in
shining armor] | gli azzurri the Italian national
sport team color, | azzurro del cielo [the blue of
the sky]. Azzurro is sited as synonymous with blu
[blue], celeste [sky blue], turchese and turchino
[turquoise], and pervinca [periwinkle]. It entered
Italian before blu and is listed with over fourteen
entries in the dictionary, including a verb form:
azzurrare [9][10]. Celeste, translated as sky
blue, light blue, baby blue, azure, is defined as
analogous to azzurro, specifications of which
are celestino [pale blue], and acquamarina [7]
[8]. There are only two dictionary entries with
the color root celest- [11]. Turchino, with direct
reference to the stone turquoise turchese, is
described as of azzurro cupo [deep or dark], and
blu [12].
Cultura e Scienza del Colore - Color Culture and Science | 04/15
The principal test of this study was developed
to apply a cognitive linguistic approach to
verify the level of entrenchment of the color
term object/concept association. The multiple
senses of color terms create a network that is
accessed and elaborated online for the speaker
to identify the meaning of the color term in use.
The identification of color term entrenchment
and distributional criterion of occurrence in
a large number of domains should give us
a sense and level of basicness and lexical
status. This approach was developed following
Langacker [13] and other cognitive linguists
who sustain a functional approach to linguistic
investigation. They acknowledge the grounding
of language in embodied experience and
social interaction, insisting that this interaction
is critically dependent on conceptualization.
Conceptualization is once again constrained by
four aspects: human cognitive capacities, the
nature of reality, convention, and context [14].
Therefore, meanings experienced more often,
will be encountered more frequently in specific
contexts or associations, and will in turn become
more entrenched on an individual level and
conventionalized in the speech community.
3. METHODOLOGY
This study was carried out in five different phases.
Each phase had a specific objective and served
to initially confirm or contradict the various
results. The first, and most pertinent, phase was
the BLUE color association test; constructed to
verify color term entrenchment and occurrence
in a number of domains. The second phase was
the same BLUE color association test translated
and presented to a pilot group of native
American English speakers, to verify the cross
linguistic saliency of the color object/concept
associations. The third phase comprised a colorlist task to confirm the three BLUE color terms’
cognitive saliency in Italian. The fourth phase
was a color-patch naming task in Italian; and
the fifth phase was a “kind of” survey to verify
informant signification and stability of reference
in Italian. The Italian informants were students at
English translation of Italian BLUE
blue
bluish, grey-blue
azure
light blue
light bluish
sky blue
pale blue
ultramarine
turquoise
indigo
blu
bluastro
azzurro
azzurrino
azzurrognolo
celeste
celestino
oltremarino
turchino
indaco
the University of Perugia. They were from mixed
regional backgrounds across Italy. The American
English informants were a variegated group of
native speakers.
The first and second phase tasks asked
informants to associate 10 BLUE color terms to
38 different Italian prototypical object/concepts.
This task was carried out by two groups of
Italian university students (49 and 48) for a total
of 97 informants (mean age 23). A comparison
group of 15 native American English speakers
carried out the same task translated into English
[9-10] (mean age 45). Azzurro has numerous
possibilities, medium blue, sea blue, bright
blue, heraldry blue, sapphire; I opted toward the
phonetically more similar term azure. A paper
questionnaire was handed out to the class of
students. One page contained the instructions
and one page the list of items with a blank box
next to it, where the informant wrote one of the
10 color terms provided. There was no time
limit. Informants took no more than 15 minutes.
The prototypical object/concepts used as stimuli
in this task were selected from online dictionaries,
databases, and idiomatic expressions, which
were double checked through Google and
confirmed as being the most frequent. The 10
colors selected to associate with the stimuli
were the three most frequent terms blu, azzurro,
and celeste, with the four possible inflected color
terms: blu-astro, azzurr-ognolo, azzurr-ino, and
celest-ino (see endnote), with three other salient
BLUE color terms, turchino, oltremarino, and
indaco. All of these terms except azzurrognolo
appeared later in the listing task, see Table 1.
The third phase, the color-list task, was carried
out by 65 university students (mean age 22). It
aimed to verify the cognitive salience of the three
BLUE terms. This task was based on Davies and
Corbett [15]; informants were asked to write
down as many color terms as they could in five
minutes. The data were then analyzed following
the cognitive salience index elaborated by
Sutrop [16], taking into account two important
aspects of BCT criteria: term frequency and
mean position. The cognitive salience index is
calculated: S = F/(N x mp), where S is salience,
Color
04/15 | Cultura e Scienza del Colore - Color Culture and Science
RGB color coordinates
red=20, green=15, blue=180
red=50, green=65, blue=120
red=60, green=155, blue=240
red=115, green=175, blue=245
red=120, green=145, blue=205
red=129, green=225, blue=255
red=180, green=235, blue=255
red=40, green=25, blue=195
red=10, green=185, blue=205
red=20, green=15, blue=120
Table 1 - The 10 BLUE names in
Italian and English, color patches
set to RGB coordinates indicated by
Moroney in the Italian section of The
Color Thesaurus, Hewlett Packard
Laboratories.
23
F is frequency in the lists, N is number of
informants, and mp is mean position rank in the
lists provided by the informants.
The fourth phase, the BLUE color-patch naming
task, was carried out in a darkened room with a
projection of numbered color patches presented
on a screen. The stimuli were presented one
at a time. They remained on the screen for 10
seconds. 30 informants (mean age 26) wrote
the color names in the box next to the slide
number. The 30 color patches were set to color
RGB parameters distinguished by Moroney for
the Italian color terms [17]. The randomized
10 BLUE color patches corresponded to the 10
color words tested in the first phase, which had
also emerged in the third phase (see Table 1).
The informants were tested twice on different
occasions to verify the consistency of naming.
The fifth phase of this study, the “Kind of BLUE”
was carried out by a group of 30 informants
(mean age 26). They were asked which BLUE
was a kind of BLUE, combining the three terms,
blu, azzurro and celeste, in couples, e.g., Is
azzurro a kind of blu? The informants answered
yes or no.
4. RESULTS
“Color association” task result data are presented
in percentages in Figures 1 and 2. The total
3686 responses given by 97 Italian informants
resulted in 30% of the objects associated
with blu (blue), 7% bluastro (blue-grey), 19%
azzurro (azure), 4% azzurrognolo (dull bluish),
4% azzurrino (light bluish), 11% celeste (sky
blue), 5% celestino (pale blue), 5% oltremarino
(ultramarine), 7% turchino (turquoise), 6%
indaco (indigo), and 3% no answer. If we group
the terms according to tone (dark, medium, light)
the division becomes 48% blu, 34% azzurro,
and 16% celeste. The spread between the three
predominant BLUE color terms does not vary
significantly, and the rank remains unchanged.
Fig. 2 shows marked differences in English
association percentages with the corresponding
terms blu, bluastro, azzurro, celeste, and no answer.
The color/object associations in English actually
seem more evenly distributed, demonstrating
the subordinate value of most of the BLUE color
terms, and the saliency of blue with a higher
percentage of associations. A predominance of
blu, azzurro, and celeste, in that order emerges in
these results.
Figure 3 presents the total Italian results, each
color term and the 38 associated items. Each
item is presented in Table 2 with the color term
with the highest percentage of associated
object/concepts agreement.
The object/concepts associations with the
highest percentage of agreement are: 85%
sangue blu, 84% fata turchina, 82% principe
azzurro, 78% caschi blu, 70% jeans blu, 64%
Madonna celeste, 63% bollino blu, 63% tute
blu, 59% fifa blu, 57% telefono azzurro, 55%
cielo azzurro, 55% fiocco azzurro, 54% strisce
blu, 52% camicia celeste, 51% machine blu,
50% mare blu, 51% pesce azzurro. Of the 38
object/concepts, the number that were mostly
associated with a specific color are 19 with blu;
10 with azzurro; 2 with celeste; 2 with celestino;
2 with turchino; 1 with azzurrino, 1 with bluastro,
and 1 with oltremarino. The most prototypical
object for blu is sangue (85%); for bluastro is
fumo (38%); for azzurro is principe (82%), for
azzurrognolo is fumo (21%); for azzurrino is
airone (23%); for celeste is Madonna (64%); for
celestino is nuvole (23%); for oltremarino is sale
(39%); for turchino is fata (84%) and for indaco
is tute (12%). The English translation of the
object/concepts may be found in Table 2.
A significant result was also the consensus
between the two Italian groups, even though
within the group there were significant
differences in color/object association. 59%
(169 of 380 responses) of possible color-object/
concept associations were made by the same
number or ±1 of informants in each group
separately, e.g., caschi and blu were associated
by 38 people in the first group and 38 in the
second group; cielo azzurro was associated
by 26 and 27. And only 4% (17 of 380) of the
associations made by the two groups were
different by >5 of the number of informants.
That is to say that the correlation coefficient
was statistically relevant between the two
Italian groups for blu, azzurro, azzurrognolo, celeste,
celestino, turchino, and indaco (all between 0.956
and 0.720).
Figure 1 (left) - Percentage of Italian
BLUE color association of 38 objects
by 97 informants.
Figure 2 (right) - Percentage of English
BLUE color association of 38 objects
by 15 informants.
24
Cultura e Scienza del Colore - Color Culture and Science | 04/15
Table 2 also shows the percentage of association
results for English. The results are significantly
different. The highest agreement was 80% for
blue flag, 73% for sky-blue sky, blue team, greyblue heron, and grey-blue smoke. Only 29%
(11 of 38) color object/concept associations
were the same in both languages, and the
percentages varied notably; the number of “no
answers” is relevant in color association saliency.
Table 2 lists the change of the color term
associated next to the item and the percentage
of agreement. The difficulty in responding by the
English informants clearly indicates the lack of
cultural entrenchment of the Italian prototypes.
“Color-listing” task results reveal two cognitively
important aspects: the term frequency and
position in the list. Following Sutrop [16] color
term basicness is estimated independent of the
length of any particular list; see results in Table
2. The color term is given followed by frequency
in the lists of the 65 informants, and the
corresponding rank; the mean position in each
list and the corresponding rank within the total
list of 134 different colors listed; the cognitive
salience index and the final cognitive salience
rank. The cognitive salience ranking of blu in
fourth position, azzurro in ninth, and celeste
in thirteenth, corresponds in essence to the
results acquired through the cognitive linguistic
association task.
“Color-patch naming”results show that the majority
of the informants called blu 6 of 10 BLUE color
patches (not azzurrino, celeste – celestino, nor
turchino), see Table 4. The patch considered blu
was named blu by 94%, azzurro was named
blu by 74%, azzurrino was named azzurro by
60%, celeste was named celeste by 80%. The
half tone azzurrognolo was named blu by 80%,
bluastro was named blu by 65%, oltremarino
and indaco were named blu by 67%. Color
04/15 | Cultura e Scienza del Colore - Color Culture and Science
Figure 3 - Italian BLUE color term 38
object/concept associations by 97
informants (English translation of the
items is indicated in Tab.2).
25
Table 2 - The maximum percentage
of informant agreement on the color
associated with each of the 38 object/
concepts; grey background for same
color-object/concept association in
both language groups; bold print for
majority agreement.
Table 3 - First 20 colors in color
listing - cognitive salience rank of 65
informants; grey background for BLUE
terms
26
Rank Ital. Ass. 38
obj/conc.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
1
2
3
4
5
6
7
8
9
10
1
2
1
2
1
2
1
1
1
Object/concept in
Italian
Color max.
agreement
% Agreement
Italian
sangue
caschi
jeans
bollino
tute
fifa
strisce
macchine
mare
bandiera
tasto
freccia
cartello
energia
fascia
occhi
treno
mani
pellicola
principe
telefono
cielo
fiocco
pesce
squadra
nastro
pomeriggio
pizzeria
porto
Madonna
camicia
nuvole
gabbiano
fata
capelli
airone
fumo
sale
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Blu
Azzurro
Azzurro
Azzurro
Azzurro
Azzurro
Azzurra
Azzurro
Azzurro
Azzurro
Azzurro
Celeste
Celeste
Celestino
Celestino
Turchina
Turchini
Azzurrino
Bluastro
Oltremarino
85%
78%
70%
63%
63%
59%
54%
51%
50%
47%
39%
37%
36%
33%
31%
30%
28%
27%
26%
82%
57%
55%
55%
51%
47%
42%
36%
23%
23%
64%
52%
23%
18%
84%
47%
23%
38%
39%
% Agreement English
47% blue
27% blue
47% indigo
47% blue
53% blue
33% no answer
40% blue
40% blue
33% azure
80% blue
27% no answer
33% no answer
53% blue
27% ultramarine
60% no answer
40% pale blue
47% no answer
27% blue
33% no answer
33% blue
27% blue
73% sky blue
33% blue
40% ultramarine
73% blue
20% sky blue
27% sky blue
47% no answer
27% ultramarine
27% no answer
40% blue
33% pale blue
60% grey-blue
20% light blue
20% blue
73% grey-blue
73% grey-blue
27% pale blue
Object/concept in
English
blood
helmets
jeans
sticker
collar
fear
lines
cars
sea
flag
button
arrow
sign
energy
band
eyes
train
hands
film
prince
phone
sky
bow
fish
team
ribbon
afternoon
pizzeria
port
Madonna
shirt
clouds
seagull
fairy
hair
heron
smoke
salt
Color Name
Frequency
Frequency
Rank
Mean position (mp)
Mp Rank
Cognitive
Salience Index
Cognitive
Salience Rank
rosso
giallo
verde
blu
nero
bianco
arancione
viola
azzurro
rosa
marrone
grigio
celeste
lillà
fucsia
indaco
beige
oro
porpora
rosso bordeaux
63
62
61
60
64
64
58
61
54
54
60
58
44
34
37
29
30
30
20
20
3
4
5.5
7.5
1.5
1.5
9.5
5.5
11.5
11.5
7.5
9.5
13
15
14
18
16.5
16.5
21.5
21.5
3.71
4.70
5.42
6.03
7.89
8.28
8.48
9.61
9.59
9.66
11.16
11.34
10.63
13.38
15.10
12.96
15.20
15.93
13.70
14.20
11
13
14
18
22
24
25
30
29
31
37
38
34
50
63
48
64
71
51
55
0.2612
0.2029
0.1731
0.1531
0.1248
0.1189
0.1052
0.0977
0.0866
0.0860
0.0827
0.0787
0.0637
0.0391
0.0377
0.0344
0.0304
0.0290
0.0225
0.0217
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Cultura e Scienza del Colore - Color Culture and Science | 04/15
blu
named
blu
by 94%
azzurro
named
blu
by 74%
azzurrino
named
azzurro
by 60%
celeste
named
celeste
by 80%
azzurrognolo
named
blu
by 80%
bluastro
named
blu
by 65%
oltremarino
named
blu
by 67%
indaco
named
blu
by 67%
turchese
named
verde-acqua
by 55%
celestino
named
celeste-chiaro
by 50%
referents for the color patches used in this task
also correspond to the legend in Figures 1 and 2.
“Kind of BLUE” task responses show 93% of
Italian informants responded affirmatively to
azzurro as a kind of blu, and 100% affirmed that
celeste is a type of blu, but blu is not a type of
azzurro, nor celeste. Only 10% of informants
claimed that celeste was a type of azzurro.
5. CONCLUSIONS
As expected BCT’s can be distinguished from
non-BCTs by the high scores on the first phase
task entrenchment and occurrence and the third
phase task cognitive saliency, and the fourth
and fifth phase task significations and stability
of reference results. The use of a specific set of
the 10 most common BLUE terms in Italian and
the lack of restrictions between BCT and nonBCT provided a mode of testing the relationship
between the three most common BLUE terms:
blu, azzurro, and celeste. The subordinate terms
were associated less often than the BCT term(s).
Moreover, the use of all the terms determined
a decrease at the expense of the more specific
terms. This provided a further check on
robustness and stability of the BCT terms.
In the past azzurro has been used as the
prototypical basic blue, Grossman [18] identifies
it as the BLUE arcilessema and translates blue
with it. Diagrams and color systems published
in the past translated labels and indications of
blue with azzurro; contemporary texts translate
blue with blu, e.g., there is no change in the
use of the abbreviation RGB (Red-Rosso, GreenVerde, Blue-Blu), and the respective diagrams of
color spaces; identification of receptor cells R-G
(Rosso-Verde), B-Y (Blu-Giallo), and primary colors
(Red-Rosso, Blue-Blu, Yello-Giallo). Moreover,
many recent videos and books for children teach
blu as the basic color not azzurro. Quantification
of a corpus analysis in this sense may be of
interest for future investigation.
This study lends support to the claim that there
has been a semantic shift in BLUE lexicalization.
I argue that azzurro was previously the Italian
BLUE primary BCT. It has been present in Italian
for longer and has diachronically developed
a more elaborate grammaticalization (see
Table 4 - Fourth phase color patch
naming in Italian.
endnote). It no longer demonstrates the same
saliency, however, as it did in the near past.
Summarizing, the study informants identified
azzurro as a type of blu, and but not celeste as a
type of azzurro. Though they may be considered
synonyms, there is a different degree of inclusion
in contemporary Italian; the hypernym is now
shifted to blu and the hyponyms are azzurro
and celeste. In this sense, according to the “non
hyponym” BCT criteria azzurro would not be a
secondary BCT.
Considering, the first phase task, however,
a more dynamic analysis of color term
entrenchment, and an apparently valid measure
of basicness and “application to a broad range of
objects”, azzurro still has robust associations
and collocations in both conceptual metaphoric
and metonymic extensions. For example, the
informants associated the sea with blu, the sky
with azzurro, blood with blu, eyes with both blu
and azzurro, and the prince with azzurro. The
results of the four phases of this study converge
to suggest that contemporary Italian use of
BLUE color terms reveals a twelfth BCT, a tone
of BLUE. Azzurro has not yet been pushed out
of basicness, but may be on its way. It still has
a high cognitive salience rank. Although it is
evident that the number and age of the Italian and
English informants differ notably in this study,
the general tendency of lack of entrenchment
of these color associations for the English pilot
group is convincing. Furthermore, all the tasks
verify current Italian conceptualization and dual
lexicalization of BLUE as language specific and
not corresponding to English.
AFTERWORD2015
In this revised version of my 2011 paper I have
added two tables to give more visual support
to the text. I have also supplemented it with
some explanation of the results in Table 3, and
included a brief glossary. The principle aim
however is to acknowledge recent publications
that have continued to address the linguistic and
cognitive entrenchment and conventions for blu,
azzurro, and celeste for Italian speakers [19] [20]
[21] [22] [23] [24] [25]. Valdegamberi, Paggetti,
Menegaz sustain that there is evidence in favor
04/15 | Cultura e Scienza del Colore - Color Culture and Science
27
of the hypothesis of the existence of the twelfth
basic color category in the Italian language [24].
Ronga, Banzanella, Struddsholm, Salvati include
azzurro in the group of BCTs analyzed in their
study, and find blu rating over azzurro both in the
type to token ratio and in the hapax legomena (i.e.,
types mentioned by a single speaker) with little
variation [25]. Ronga’s earlier findings vary from
these others, in that she stated
“In Italian, in fact, azzurro (light-blue) is
considered a basic colour term and not part
of the realm of blue” [3].
Her paper gives and excellent overview of the
history of azzurro, revealing, I believe, the previous
primary basicness of the term. Paramei, D’Orsi,
Menegaz affirm,
“Our
results
provide
additional
psycholinguistic evidence that for Italian
speakers at least two colour terms are
necessary to name the BLUE area, blu
‘dark blue’ and azzurro ‘light-and-medium
blue’. Both were shown to behave as basic
colour terms, in linguistic and previous
psycholinguistics studies” [22].
Nonetheless, a predominance of current
research confirms the semantic shift of blu
into a more salient role and azzurro into a more
secondary position. Some researchers advance
the possibility of regional influences, and/or
generational differences. Thierry et al. findings
[26] uncover an effect of native language on
implicit color discrimination, and how language
learning seems to modify the location and extent
of categorial perception, which may reorganize
the representation of perceptual color space.
It is conceivable that the second language
acquisition, which is now required in Italian
elementary schools, and is usually English, may
be speeding up a shift in the perception and
the semantics of BLUE. The Italian informants
were university students from mixed regional
backgrounds across all of Italy, but all had studied
scholastic English for 4-8 years. Clearly the general presence of English BLUE
in Italian daily life, through computers, internet,
advertisements, etc., can in itself contribute to
a shift in linguistic convention. These factors
may be an explanation of the displacement of a
cognitive reference point, of the perceptual and
conceptual variations introduced by different
languages that use variations of blue. As
Desgrippes states,
“the cognitive representation of a color
is dynamic: it can evolve with diachronic
language variation or with language shift,
and both older and newer representations
28
remain retrievable depending on the task at
hand” [27],
which seems pertinent to this case, that is, the
present findings may differ from past studies.
To summarize I return to the initial questions
posed: What are the Italian BLUE term semantic
relations? My results point to blu as the primary
BCT, azzurro as a secondary BCT, and celeste as
a subordinate color term. Do azzurro and celeste
violate the criterion that a BCT not be a hyponym
of another color word, i.e., blu? I would say that
azzurro does, though it ranks high in saliency
for the informants of these questionnaires,
and hence, should still be considered a BCT
nonetheless. Does the semantic relation respond
to the cognitive need to differentiate between
the colors of the sky and the water? This
differentiation seems to still be pertinent in the
use of blu and azzurro in Italian. In English the sky
is blue or sky blue, but the water, referring more
often to the ocean than the sea, may be blue,
grey, or green. The sea is azure. Is the principal
task color term object/concept association,
based on the cognitive linguistic approach to
linguistic entrenchment, an original valid method
to measure basicness? Yes, it has demonstrated
to be an effective method to measure basicness.
This type of functional or usage-based approach
to linguistic investigation is a practical means to
verify use of language. Cognitive linguistics sees
language as grounded in embodied experience
and social interaction, which is critically
dependent on conceptualization that is based
on linguistic entrenchment; i.e., “the strength of
conventional constraints to aspects of word meaning
that have attained some sort of default status” [12].
Therefore, meanings experienced and attributed
more often will be encountered more frequently
in specific contexts or associations, and will
become more entrenched in the individual’s
conceptualization and conventionalized in the
community of speakers. This paper suggests
that blu, though still different than English blue,
is undergoing a semantic shift that is detracting
some of azzurro’s and celeste’s vigor.
GLOSSARY
Entrenchment: A color term may be associated with numerous
objects, the greater the number of associations and the
greater the distribution of occurrence in a large number of
domain matrices, the higher the level of entrenchment of
the color term object/concept association; the progressive
entrenchment of configurations that recur in a sufficient
number of events are then established as cognitive routines
[11, 28].
Saliency: A color term is salient if it is readily elicitable, occurs
in the idiolects of most speakers, and is used consistently
by individuals and with a high degree of consensus among
individuals [29].
Cultura e Scienza del Colore - Color Culture and Science | 04/15
Consistency: consistency of color usage relates to the
probability that a color name, if used by a given subject on
the first presentation, will be used again on the second one
[30].
[6] Stanulewicz D., “Polish terms for ‘blue’ in the
perspective of Vantage Theory”, Language Sciences 32
(2), pp. 184-195, 2010.
ACKNOWLEDGEMENTS
[7] Paramei, G.V., Russian Blues: controversy on basicness.
Anthropology of Color: Interdisciplinary Multilevel
Modeling, R.E. MacLaury, G.V. Paramei, D. Dedrick (eds.),
John Benjamins, pp.75-106, Amsterdam/Philadelphia,
2007.
I would like to thank the anonymous reviewers
who offered helpful comments and suggestions
on previous versions of this paper.
NOTES
The Italian dictionary online “Grande Dizonario Hoelpl Italiano di
Aldo Gabrielli, Hoepli, 2011” and “Il Nuovo Zingarelli – Vocabolario
della lingua italiana” (10th edition Nicola Zanichehlli Sp.A. Bologna,
1990, show only the following entries as derivatives, with my
translations of the definitions: blu: bluastro [adj. type of blue],
blucerchiato [adj. circled in blue], blue-jeans [Engl. n. blue colored
cotton pants], blues [Engl. n. a type of music or dance], bluesman
[Engl. n. singer or executer of blues], bluette [Fran. adj. type of blue,
light turquoise, n. that color blue]; azzurro: azzurrabile [adj. being
able to get on the national soccer team referred to as the “Azzurri”],
azzurraggio [n. being able to make a yellowish substance white by
adding that color (azzurro)], azzurramento [n. treatment of lenses,
which takes on a bluish cast, to diminish reflection, making more
transparent], azzurrare [v. tr. to dye that color (azzurro)], azzurrastro
[adj. a color that reminds you of that color azzurro], azzurrato [v.
past participle of azzurrare; adj. lenses that have been treated to
diminish reflection], azzurreggiare [v. intr. that which tends to or to
be that color (azzurro)], azzurrescenza [n. that which tends to that
color (azzurro)], azzurriccio [adj. tending toward that color (azzurro)],
azzurrigno [adj. a dull version of that color (azzurro) tending towards
grey], azzurrino [adj. tending toward a light delicate version of that
color (azzurro), n. the color], azzurrità [n. the quality of being that
color (azzurro)], azzurrite [n. a mineral, used as a dye], azzurrognolo
[a pale, greyish, or faded version of that color (azzurro)]; celeste:
adj. --the first definitions regard first the sky and the heavens in
reference to the supernatural, only the later entry refers to “the color
of the sky free from clouds”, etc.-- the only derivative that includes
its root is celestino, the first entry, [adj. a light form of that color (sky
blue)]. N.B. The Nuovo Zingarelli dictionary includes a color Atlantis
that lists 11 compounds with the color blu: blu notte, blu di Prussia,
blu di Parigi, blu oltremare scuro, blu oltremare chiaro, blu cobalto,
blu azzurro manganese, blu d’oriente, blu pavone, blu ceruleo, blu
turchese, blu zaffiro; and only one term with azzurro: blu azzurro
manganese, and only once celeste.
i
BIBLIOGRAPHY
[1] Berlin, B., P. Kay, Basic Color Terms: Their Universality
and Evolution, University of California Press, Berkeley,
[1969] 1991.
[2] Kay, P., C. McDaniel, “The Linguistic Significance of the
Meanings of Basic Color Terms”, Language 54:3, pp. 610646, 1978.
[3] Ronga, I. “L’eccezione dell’azzurro. Il lessico cromatico:
fra scienza e società”, Cuadernos de Filología Italiana, 16,
pp. 57-79, 2009.
[4] Borg, A., Towards a diachrony of Maltese basic color
terms, New Directions in Colour Studies, C.P. Biggam, C.A.
Hough, C.J. Kay, D.R. Simmons (eds.), John Benjamins,
pp.74-90, Amsterdam/Philadelphia, 2011.
[5] Androulaki, A., N. Gômez-Pestaña, C. Mitsakis, J.L.
Jover, K. Conventry, I. Davies, “Basic colour terms in
Modern Greek- Twelve terms including two blues”,
Journal of Greek, Linguistics 7, pp. 3-47, 2006.
[8] Rätsep, K., Preliminary research on Turkish basic
colour terms with and emphasis on blue, New Direction
in Colour Studies, C.P. Biggam, C.A. Hough, C.J. Kay, D.R.
Simmons (eds.), Benjamins, pp.133-145, Amsterdam/
Philadelphia, 2011.
[9] www.wordreference.com/iten/blu
[10] www.wordreference.com/enit/azure
[11] www.treccani.it/vocabolario/tag/blu/
[12] www.treccani.it/vocabolario/turchino/
[13] Langacker, R.W., Cognitive Grammar, Oxford University
Press, Oxford, 2008.
[14] Croft, W., D.A., Cruse, Cognitive Linguistics, Cambridge
University Press, Cambridge, 2004.
[15] Davies, I., G.G. Corbett, “A practical field method for
identifying basic colour terms,” Languages of the World
9, pp. 25-36, 1995.
[16] Sutrop, U., “List task and Cognitive Salience Index”,
Field Methods 13, pp.263-276, 2001.
[17] Moroney, N., The Color Thesaurus, Hewlett Packard
Laboratories, Palo Alto, 2008.
[18] Grossmann, M., Colori e lessico: Studi sulla struttura
semantica degli aggettivi di colore in catalano, castigliano,
italiano, romeno, latino ed ungherese, Narr Press,
Tübingen, 1988.
[19] Bimler, D., M. Uusküla, “‘Clothed in triple blues’:
sorting out the Italian blues,” Journal of the Optical Society
of America A 31, pp. A332-A340, 2014.
[20] Paggetti, G., G. Menegaz, G.V. Paramei, “Color Naming
in Italian language”, Color Research and Application,
2015. Early View, DOI: 10.1002/col.21953.
[21] Paggetti G., G. Menegaz, “Exact location of consensus
and consistency colors in the OSA-UCS for the Italian
language”, Color Research and Application, 38 (6), pp.
437-447, 2013.
[22] Paramei, G.V., M. D’Orsi, G. Menegaz, “‘Italian blues’:
A challenge to the universal inventory of basic colour
terms”, Journal of the International Colour Association 13,
pp. 27-35, 2014.
[23] Uusküla, M. Linguistic categorization of blue in
Standard Italian, Colour Studies: A Broad Spectrum, C.J.
Kay, C.A. Hough and C.P. Biggam (eds.), John Benjamins,
pp. 67-78, Amsterdam/Philadelphia, 2014.
[24] Valdegamberi, V., G. Paggetti, G. Menegaz, “On the
perceptual/linguistic origin of the twelfth basic color
term in the Italian color lexicon”, Colour and Colorimetry
Multidisciplinary Contributions, Vol. VII B, M. Rossi (ed.),
Optics and Photonics Series Notebooks 21, pp. 291-298,
2011.
04/15 | Cultura e Scienza del Colore - Color Culture and Science
29
[25] Ronga I., C. Bazzanella, E. Strudsholm, L. Salvati,
“Black as night or as a chimney sweep?”, Intercultural
Pragmatics 11 (4), pp. 485-520, 2014. DOI 10.1515/ip2014 - 0022.
[26] Thierry, Guillaume, Panos Athanasopoulos, Alison
Wiggetta, Benjamin Deringa, and Jan-Rouke Kuipersb,
“Unconscious effects of language-specific terminology on
preattentive color perception”, Proceedings of the National
Academy of Sciences 106 (11), pp. 4567–4570, 2009.
[27] Desgrippes, M., “Dynamic cognition: when the best
example falls out the boundary, communication”, Colour
Language and Colour Categorization, Conference 4-7 June
2013, International conference, Institute of the Estonian
Language - Eesti Keele Sihtasutus, Tallinn, Estonia.
[28] Hampe, B., Image schemas in Cognitive Linguistics:
Introduction. From perception to meaning : image schemas
in cognitive linguistics, Beate Hampe and Joseph E. Grady,
eds., Mouton de Gruyter, Berlin, pp. 1-14, 2005.
30
[29] Hardin, C. L., Luisa Maffi (eds.), Introduction,
Color Categories in Thought and Language, Cambridge
University Press, pp. 1-18, Cambridge, 1997.
[30] Robert M. Boynton, Insights gained from naming the
OSA colors, Color categories in thought and language,
Clyde L. Hardin and Luisa Maffi (eds.), Cambridge
University Press, pp. 135-150, Cambridge, 1997.
Original Reference: Sandford J.L. “Blu, azzurro, celesteWhat color is blue for Italian speakers compared to
English speakers?”, Proceedings of the Eighth National
Color Conference. Gruppo del Colore – SIOF - Alma Mater
Studiorum Università di Bologna, Facoltà di Ingegneria,
Bologna, Italy, 13-14 settembre 2012. In Colour and
Colorimetry. Multidisciplinary Contributions. Vol. VIII B, M.
Rossi (ed.) – Dip. Indaco – Politecnico di Milano. Maggioli
Editore, pp.281-288, Santarcangelo di Romagna (RN),
2012. ISBN 88-387-6137-x. EAN 978-88-387-6137-9.
Cultura e Scienza del Colore - Color Culture and Science | 04/15
Society in colour:
Italian television and
the switch to colour
While Italy was proudly celebrating the advent of
its own television system on 3 January 1954 and
the first official broadcasts were starting to go
out, at around the same time, the United States
saw the arrival of colour television. Indeed, in
December 1953, the Federal Communications
Commission (FCC) approved the standard
for colour television set out by the National
Television System Committee (the same one
that established the 525 line standard in 1941).
The NTSC colour television standard was chosen
for its versatility over other similar experiments,
including its CBS rival, since its broadcasts were
perfectly compatible with the black-and-white
receivers which were widespread at the time.
While Italy stood out for black and white, in midJanuary 1954, colour television was authorised
to operate commercially in the United States, and
the analogue colour standard made its entrance
on screen. It was then quick to be adopted in
Canada, South Korea, Japan and many other
countries, and would remain in place up until
the arrival of the ATSC Digital system in 2009.
Italy, however, officially seemed to have to wait
until 1977.
1. POLITICS AND
THE COLOUR EXPERIENCE ITALY
In 1965, a RAI studio in Rome was set up with
three Colour Television RCA TK40 cameras
and a colour TK40 telecine [1-2]. This was the
start of the first “technical broadcasting tests”,
while elsewhere in Europe, Britain, France
and Germany – albeit with an eight-year delay
compared to the United States – introduced
colour television broadcasting in 1967. In Italy,
however, the process came to an abrupt halt,
and only seemed to fully open up again on 1
February 1977 with the official launch of colour
broadcasting. The study of colour television,
which is entirely overlooked in Italy, is often
merely limited to accounts of the (political)
causes of this delay, which fail to deal with its
full complexity. In Italy, this
“continuous succession and interweaving of
speeding up and slowing down, of cuttingedge experiments and prolonged periods
stagnation”
is not uncommon and, according to Peppino
Ortoleva, appears somewhat to be a peculiarity
of Italian history of telecommunications. Indeed,
Paola Valentini
[email protected]
1
AGAS Department of History,
Archaeology, Geography, Art and
Performance Arts,
University of Florence, Italy
1
at the very time when television may have
seemed to be behind, in 1970, Italy held the
record – together with Germany – for the first
direct distance dialing [3]. The switch to colour
television in Italy conceals something more
complex, which cannot be put down merely
to technological developments. It is a complex
story which is very much still to be written, the
main aspects of which will be outlined below.
Often the delay in Italy is associated with the
battle over the standards running through the
world history of colour. It began in the United
States, where the choice of system, despite
becoming the matrix for its successors, was by
no means a painless process. Yet, the contest
was especially fierce in Europe, where there
was clash between two systems, PAL (Phase
Alternation Line) and SECAM (Séquentiel
Couleur à Mémoire), between two powers
(Germany and France), between two political
and expansionist concepts, and indeed between
two opposing worldviews [4]. Nonetheless, the
impression in Italy was that the late arrival of
colour was only the result of uncertainty over the
standard. Accordingly, the final move to opt for
PAL, approved by the Ministry on 5 April 1975,
did not create any particular controversy or
backlash in newspapers, where the news was
reported in a cut and dry way. This is despite the
fact – which has never been properly brought
to light – that this decision not only threw out
the French standard, but the entire Italian ISA
system (Suppression Alternation Identification)
patented in 1972 by the Turin-based company
Indesit.
Indeed, what had been occurring in Italy took the
form of a confusing but lively political debate,
more than an economic-industrial matter. Any
attempt to untangle it is extremely difficult, as it
often used as an opportunity to renew (and decide
how to renew) the expiring television monopoly,
a battle more of principle than substance, more
of ideals than politics or economics. The picture
is as hazy as the approach and stance adopted
by the television phenomenon itself, between
“empty and inane anti-capitalist leftism” and “overindulgence in crass consumerism”, as stated in 1977
by Ugo La Malfa, one the fiercest opponents of
colour [5]. What is remarkable when reviewing
the newspapers’ coverage of colour is the
disconnect between the theoretical discussion
on television and the path it has taken. In 1972,
in the wake of the widespread Eurovision colour
broadcasts on other foreign channels, RAI
decided, on an experimental basis, to broadcast
the Munich Olympics in colour, alternating –
somewhat paradoxically – the SECAM and
04/15 | Cultura e Scienza del Colore - Color Culture and Science
31
PAL standards. As soon as news was given
of this initiative by the Italian Government in
early August, all newspapers – including those
which were left-wing and traditionally almost
indifferent to what they considered to be a mere
and useless appliance – devoted a great deal
of space to the story and to the debate around
colour television. The minute-by-minute report
of the ups and downs in the political reception
of this decision by the Ministry of Posts and
Telecommunications follows a script that would
be repeated for five years. “Waste”, “unnecessary
expense” and “distortion” in the face of Italy’s crisis
and real needs, were the most frequently used
arguments, together with – something that was
perhaps the most accurate premonition given
what would take place at the end of the decade –
an attempt to make people fear colour television
as a “dangerous tonic”, a glare and mirage of an
economic miracle that was now impossible [6].
More than the boycott of vehicle manufacturers,
or than the Italian Communist Party’s invectives
against the persistent misinformation of
television now in colour, what emerges from the
news reports of the time is the depiction of an
Italian society where colour was already a reality
for much of the country:
“consumers in the north [...] already have
colour TV sets: those in the Lombardy
and Piedmont area are served by Swiss
TV from Lugano, those around Liguria and
Tuscany receive French TV programmes
[Telemontecarlo], and those in Adriatic areas
capture Yugoslav broadcasts [Tele Koper]”
[7].
Within a year, the actual framework becomes
clearer: the extraordinary measures put in place
on 22 November 1973 to address the oil crisis
directly associated television with the politics of
austerity, and the “dangerous tonic” was defeated,
putting an end to Italy’s experiments in colour.
Policy imposed black and white (as well as a
compulsory silence as the small screen was
required to be turned off early); darkness fell
upon Italian cities, where shop windows and
signs were turned off and public lighting was
halved.
There was already, however, a growing
relationship between colour and the viewer.
Indeed, whether directly or indirectly, colour
made a significant contribution to stabilising
the television picture in Italy. How the television
picture was experienced by viewers in its first
twenty years is extremely interesting. At least
in Italy, the economic expenditure of mobile
external units set up for live broadcasting and
the poor performance of radio links used to
transmit the signal to the production centre,
meant that, from the outset, television filming
was carried out with film stock and Arriflex
cameras, which were much more manageable
than the bulky “backpacks” for the magnetic
recorder of portable cameras, and were often
preferred by operators with a background in
32
film. Therefore, the base photographic image,
albeit converted electronically, remained for a
long time – at least until the mass spread of
U-Matic technology introduced by Sony in 1971
but becoming massively widespread in RAI in
the second half of the 1970s. This technology
became the workhorse of the first private
networks – the standard for filming not only
documentaries and news reports, but services
including the much-loved sports coverage,
starting, for example, with the football match
summary. Moreover, when TV serials opened up
to the outdoors (being initially made entirely live
in the studio using small models and effects),
they opted for the more flexible film cameras,
mixing cinema film and other entirely electronic
footage with telecine. It is not easy to establish,
in this era of craftsmanship, whether what
was in play was a consolidated practice or a
particular aesthetic choice of the producers
and directors. For instance, already in L’isola
del tesoro (1959), Anton Giulio Majano uses film
to show, in just a few minutes, a pirate falling
from a brig (entirely rebuilt in studio) in the real
waters of Lake Fogliano. Conversely, in Il mulino
del Po (1963), Sandro Bolchi shoots in film the
famous film credits superimposed over the
flowing river, while to achieve the famous flood,
she chooses to flood studio 3 in Milan. What is
certain, however, is that the eye of the viewer
perceives, with some degree of clarity, the
difference in texture between the two images,
which opposes the above-mentioned scenes,
as well as the medium close-ups of newscasts
with recorded services and an entirely different
lighting distance [8].
Colour increases this ontological genetic
instability of the television picture, which the
general “overflow”, so to speak, is not fully able to
conceal; in actual fact, it is fuelled by the external
environment. First, the viewer’s experience starts
to bring about an interaction between the blackand-white television pictures and their colour
print counterparts. The advent of television
runs parallel to the revival of the rotogravure,
characterised by photojournalism and colour
pages, such as in Epoca; in magazines with a
greater focus on the television world, colour
still seemed to interact directly, supplementing
or perhaps attempting to overtake the small
screen. For instance, in 1962, “Bolero Film”
accompanies a service on Canzonissima with
an array of colour shots, half a page large, on
the ballet and costumes worn by Mina in the
episode aired that week [9]; at the same time,
the weekly publication Successo, presented an
unusual interview between the famous singer
and Luchino Visconti, accompanying the text
not only with black-and-white feature of the
restaurant encounter between the two, but also
with several shots of Mina on the set, as Lina
Cavalieri, during the making of a Carosello for
Industria Italiana della Birra (figures 1-2) [10].
The relationship between the text in black and
white and the paratext in colour also relates to
cinema, where it was not uncommon for black-
Cultura e Scienza del Colore - Color Culture and Science | 04/15
and-white film to clash with the colour of flyers
or posters (such as L’avventura, Michelangelo
Antonioni, 1960, or Rocco e i suoi fratelli, Luchino
Visconti, 1960). Nevertheless, film paratexts
now have a narrative or symbolic relationship
with the film subject, representing a form of
novelisation. For this very reason, the preferred
choice was for illustration, such as in the famous
posters of Carlo Campeggi or Angelo Cesselon,
and where the film frame is used, the image
opts for pseudo-toning change or strong colour
backgrounds charged with symbolism. Whereas,
in the case of colour television paratexts, the
relationship seems to be of a calligraphic
and didactic nature with both a “faithful” use
of colour and, at the same time, the powerful
performance of four-colour coated paper, which
becomes an emblem of the post-war “universe of
coloured objects” [11]. As such, the colour image
seems to be a complementary experience to
black-and-white television. The above example
of Mina is not accidental: the significance of
clothing and fashion, make-up and disguise is
an essential part of constructing this character,
and the periodical’s images not only make the
viewer dream of lavish dressing tables, but help
to complete the feel of the scene.
Nonetheless, through its relationship with
paratexts, colour also shows a television picture
which is far removed from that commonlyprojected window on the world: a manufactured
and unstable image, which at the time in
Italy, was incomplete and, therefore, awaiting
integration
2. COLOURING REALITY:
SPORT AND GAMES SHOWS
In 1978, on the pages of Corriere della sera,
Luca Goldoni remarked:
“Today, Italy’s television unity is a past
memory, we have now returned to the
Municipalities and Signorias. Every district
has its own private antenna; people no
longer converse on the train, but rather just
brag about who gets the most channels. If
two travellers realise that they have both
seen the same programme the night before,
they embrace one another” [12].
world, that would only be fuelled by the meaning
constructed by the live coverage from the late
1970s.
The relationship and interference between
the television picture in black and white and
paratexts in colour soon came together with an
audiovisual text identity, where the colour system
varied according to its communication channel.
The crisis and need to renew RAI’s production,
leading to a close dialogue with cinema [14],
which once again would be over-simplistic to
dismiss as a generic “transition to film”. In actual
fact, it was a very complex situation both in its
causes and its consequences: from the inability
to dub with magnetic tape, thereby excluding
this option for large international co-productions
(L’Odissea, Franco Rossi, 1968), through to the
choice between 35 mm (La strategia del ragno,
Bernardo Bertolucci, 1970 or I clowns, Federico
Fellini, 1970) or “inflated” 16 mm (L’Orlando
furioso, Luca Ronconi, 1975). The question of
colour – whose mere adoption was the result
of various and complex causes – has also been
addressed with an equally simplistic approach
and cannot be reduced to how close the writer
was to the world of television. Whilst the choice
of black and white in Francesco d’Assisi (1967)
by Liliana Cavani may be traced back to her
previous television reports, this is not the case
of Leandro Castellani who, despite also being
an exponent of television reporting, is motivated
– as he himself acknowledged – by the latest
developments of the American Cinéma vérité
and its different use of black and white for
his Le cinque giornate di Milano (1971). At this
stage, colour had a fundamental importance
which one must begin to reconsider. Firstly, the
television picture continued to find its colour
elsewhere. Often our memory fails us and we
tend to forget that the greatest successes, such
as Le avventure di Pinocchio by Luigi Comencini
(1972), and the most daring experiments, like
the aforementioned Furioso by Ronconi, were
seen at the time in black and white. Yet, we also
tend to forget that our colour perception of these
works is not so much due to the fact that they
Figure 1 (left) – Photograph of the set
of Carosello by Industria Italiana della
Birra (in Successo, March 1962, p.
63).
Figure 2 (right) – Part of the television
frame of Carosello by Industria Italiana
della Birra
The commercial sale of colour TV sets led to
the spread of the remote control, which came
included with all of them, and the transition to
colour coincided with the widest proliferation
of private and commercial broadcasters and
with the practice of “zapping” [13]. However, as
mentioned above, for some time, viewers did not
all have the same perception of the television
picture, so much so that, in Italy, it somewhat
questioned – or made into a reconstruction –
that sense of immediacy and of living nature
that would would immediately characterise it.
The perspective on colour shows a complex
and protean television picture, which was far
from that mere framework or window onto the
04/15 | Cultura e Scienza del Colore - Color Culture and Science
33
were aried in colour in following decade, but
because of the concurrent availability of colour
in cinema theatres: La strategia del ragno was
aired on Sunday 25 October 1970 but gained
its censorship certificate on 6 October the
following year, embarking on an autonomous life
of film; at a shorter distance apart, Comencini’s
Pinocchio, aired in April 1972, hit the screens of
major cities in August. In this period of revival
of television narratives, things become more
complicated when widening our scope to the
above-mentioned foreign broadcasters in the
Italian language. We might narrow it down to
one case in point: from the 3 October 1971, the
RAI viewer was able to see UFO in black and
white, a great international success from the
British channel ITV, whose episodes were aired
in colour in Italian-speaking Switzerland the year
before under the title Minaccia dello spazio, which
could be received in a large part of Italy. That’s
not all: given its success, the producer of the
the television series, Kent, made feature films
by assembling scenes from “Swiss” TV series
in colour, making it finally possible to show
everyone in colour the Fuchsia-haired female
protagonists or the futuristic base of the SHADO.
It was not by chance that the first of these films,
Allarme rosso… attacco alla Terra! (1973), was
touted as “a great colour film that you’ll never see
on TV”.
Last but not least, one can see how colour
became a central element through a correct
and thorough understanding of these particular
hybrids, these products of “cinema-television”
where the co-occurrence of film and television
material – seen in early TV soaps – becomes
an integral part of the text and subject matter
and, once again, survives the apparent
homogenisation caused by telecine. Indeed, the
factors at play are not only the clash between
cinema and television with different areas of
competence in terms of viewing, or the role of
the frame and its relationship between the big
and small screen, but also the duality of light
and colour between cinema and television. The
latter is the result of the particular interplay in
television between chrominance and luminance
signals (used for the brightness of the image
which is, in fact, the black-and-white signal)
[15]. This aspect is well-known to the great
cinematographers, who often work on these
productions, such as Vittorio Storaro, pushing the
boundaries of film photography or breaking that
of television, devoid of strong tonal contrasts.
Colour television was yet to triumph in the wake
of audiovisual narratives and RAI’s increasingly
sought-after twinning with cinema, which
– as caustically observed in 1977 by Lietta
Tornabuoni – was turning RAI into “an institute
of historical and nostalgic studies”, but saw victory
with a completely unexpected area of the state
monopoly: sports. The regular broadcasts of the
Yugoslav network Tele Capodistria in full colour
(since 6 May 1971) not only attracted viewers
with cartoons or TV series, but also through the
European coverage of major sports events. The
viewer alternated and soon migrated to watch
34
the winter Olympic Games in Sapporo (1972)
on the Istrian channel, being able to see Gustav
Thöni win gold in the giant slalom with his yellow
skis as the Italian flag was raised triumphantly
for the awards ceremony. The experience would
be repeated more often, with the Olympics in
Munich, the FIFA World Cup in West Germany
(1974), the European Football Championship
(1976) and the Olympics in Montreal (1976).
Likewise, private broadcasting began in colour
(reflected in the broadcaster GBR’s yellow, blue
and red initials). The Rome-based broadcaster
later gained international renown for its
footage during the kidnapping of Moro, gaining
authorisation to enter stadiums and provide live
coverage in colour of those games where state
television would only be able to maintain some
degree of appeal thanks to the replays with Carlo
Sassi in Domenica sportiva. Through sport, colour
provided “real” coverage, with the emblematic
example of the Sanremo festival: in 1973, the
final would be televised by Eurovision in colour
but could only be captured on foreign television
and not by RAI repeaters; the following year a
fierce TeleNapoli would offer all three events in
colour.
Soon after the entry into force of the official
state colour television, the dichotomy of a
picture whose artifice became exposed by
colour was finally over, and the colour television
was proven to be a key element for the success
of the television picture as a “window onto the
world”. Colour became the central feature when
constructing the television picture, which finally
displays that supposed transparency which will
becomes one of its key elements.
Following the dissatisfaction of its audience
(although one might take the foreign example
where the second international French Channel
ORTF chose the opening of the Winter Olympics
from Grenoble on 1 October 1967 to inaugurate
their own colour broadcasting), RAI releases
the colour broadcasting of the infamous 1972
Summer Olympics in Munich, where the red
shirts of the Soviet basketball team overpowering
the white jerseys of the US team in the last three
seconds of the hotly contested final was even
able to stir up excitement among Italian viewers.
Yet, television failed on the real “show”, as the
Fedayeen’s attack on the Israeli athletes would
be covered minute by minute by radio reporters
directly linked from within the Olympic village.
Conversely, private televisions now commanded
the film phenomenon both through companies
set up specifically to distribute films to television
(such as the Rusconi Audiovisual system) and
through direct agreements with major production
houses (as in 1979, Berlusconi with Fide and
Titanus). Indeed, RAI was not only battling over
film content, but over being able to offer a real
and credible vision of reality: in 1977, the Giro
d’Italia bicycle race was still broadcast in black
and white, owing – they say – to the high costs
involved in covering such a large event.
RAI would later be able to address its problems
and the crisis of its system, but thanks to colour,
the television picture – and with it the same
Cultura e Scienza del Colore - Color Culture and Science | 04/15
media identity – would stabilise in a mix of
both highly spectacular and everyday events,
as a dirty mirror as it was called, which could
now fully deploy its effects. The game show
is the most mature and conscious platform
where, once again, colour plays a central role.
The move from Tele Milano to Canale 5, from a
small local cable service to the core of the future
Berlusconi - owned network was announced
in December 1979 on a game show, I sogni nel
cassetto, which only apparently copied RAI’s
format – with Mike Bongiorno supported by
his most loyal collaborators, such as the notary
Ludovico Peregrini or Director Lino Procacci –
and a traditional game mechanism focusing
on general knowledge questions. However, as
commented by Orietta Berti in her theme song
I sogni son desideri, the prize is making a dream
come true (albeit within the “limit” of 20 million
lire). No one remembers what the dreams of
competitors were, yet there is a clear colour and
spatial translation created by the aspirational
scenario where nothing is impossible: the TV
set builds the communication situation with
lights and colours, with a predominance of
strong primary colours, and set designs with
warm tones as opposed to the icy blue and
grey colours of state television in Rischiatutto.
The set created by Graziella Evangelista – the
unparalleled set designer who would make an
unforgettable mark on commercial television,
from Ok, il prezzo è giusto! to Il gioco dei nove
and Drive in – contained coloured flowers and
rainbows, superimposed elements and dazzling
psychedelic optical effects. Everything came
together to make the game show space closer
to the fantastic world of the Wizard of Oz, where
reality and dreams, window onto the world and
entertainment coexisted – a far cry from RAI’s
inefficient, sterile, dull, grey cabins and theatre
boxes [16].
The new decade began for Italian society where
this quality of colour television had now become
a well-established reality. The first images of the
Bologna station bombing were shown by private
networks in colour, as were the desperate live
images from Vermicino awaiting an impossible
happy ending, the equally innovative coverage
of Enrico Berlinguer’s funeral, the euphoric
images of the fall of the Berlin wall, the student
who was able to stop the tanks in Tiananmen,
and the chilling scenes that crushed teleological
ambitions of the Shuttle exploding into a
thousand pieces during the festivities in Cape
Canaveral. Similarly, in 1980 a television show
opens on the glitzy, psychedelic TV set of Tele
Milano 58, shining with thousands of colours,
where Mike Bongiorno cheerfully strides on
stage with his programme Allegria!
BIBLIOGRAPHY
[1]RAI Radio Televisione Italiana, Annuario 1965, Edizione
Radio Italiana, Torino 1965.
[2] A. Lari, Sistemi di ripresa e registrazione in RAI dal
1950 ad oggi, Sandit, Albino (BG) 2012.
[3] P. Ortoleva, Telecomunicazioni: un modello italiano?,
«Memoria e ricerca», n. 5, gennaio-giugno 2000, p. 112.
[4]F. Andreas, “Politique de la grandeur” versus “Made
in Germany”: politische Kulturgeschichte der Technik
am Beispiel der PAL-SECAM Kontroverse, R. Oldenbourg,
München 2007.
[5] A. Ronchey, Intervista sul non governo. Ugo La Malfa,
Laterza, Roma-Bari 1977.
[6] G. Crapis, Il frigorifero del cervello: il Pci e la televisione
da ‘Lascia o raddoppia?’ alla battaglia contro gli spot,
Editori Riuniti, Roma 2002.
[7] R.R., Tv a colori per le Olimpiadi, «Corriere della sera»,
2 agosto 1972, p. 7.
[8] M. Mazzanti, L’illuminazione in televisione: manuale
per la progettazione e l’uso tecnico/creativo delle luci
nello studio TV, Quartz color Ianiro publishing division,
Roma 1979.
[9] Anon., Mina. 5 dive in una sola, «Bolero Film», 777, 25
marzo 1962, pp. 30-32.
[10] S. Benelli (a cura di), Luchino Visconti. Mina. La donna,
e altre cose ancora, «Successo», marzo 1962, pp. 60-65.
[11] F. Pierotti, La seduzione dello spettro. Storia e cultura
del colore nel cinema, Le Mani, Genova 2012, p. 219.
[12] L. Goldoni, Il “pirolino” che fa scoprire le mille tv
private, «Corriere della sera», 3 febbraio 1978, p. 14.
[13] P. Ortoleva, Un ventennio a colori. Televisione privata
e società, Giunti, Firenze 1995.
[14]F. Pinto, G. Barlozzetti, C. Salizzato (a cura di), La
televisione presenta… La produzione cinematografica
della Rai 1965-1975, Marsilio, Venezia 1988.
[15] A. Banfi, Corso integrale di televisione a colori, con
la collaborazione di A. Nicolich, N. Stucchi, H. Salan, D.S.
Fleming, 8 voll., Il rostro, Milano 1966.
[16] P. Valentini, Televisione e gioco. Quiz e società italiana,
Archetipo/Clueb, Bologna 2013
04/15 | Cultura e Scienza del Colore - Color Culture and Science
35
Jada Schumacher
[email protected]
1
1
Communication Design
Department,
Fashion Institute of Technology
Color, Mon Dieu:
A Case-Study Comparison Between
The Church Of The Epiphany (New York City)
and Kresge Chapel (Cambridge, Massachusetts)
1. INTRODUCTION
This paper offers a case-study comparison of the
atmospheric effect of the manipulation of color
and light in two modernist religious spaces: The
Church of the Epiphany by Belfatto and Pavarini
in New York City, New York (1965-1967) and
Kresge Chapel by Eero Saarinen in Cambridge,
Massachusetts (1953-1955).
The chosen religious compounds are located in
similar climates and with geographic coordinates
in close proximity; as a result, the sites have
similar locations for sun angles in summer
and winter. They house the worship of differing
religious practices and are located in urban areas
(in contrast to previously researched Midwestern
case-studies). As the religious building is a genre
typically designed to invoke heightened spiritual
experiences, the paper examines how the
design choices of each architectural team affect
the color experience inside. This proposed paper
specifically exposes major massing decisions
(placement of mass and volume of buildings),
material choice, sculpting of light, proximity of
color zones, and location of colors in relation to
visitor experience.
[Author’s Note: The paper probes further into a
topic I began to address in a case-study paper
on religious structures in the Midwestern United
States presented at the International Color
Association AIC 2011 Interaction of Colour and
Light Midterm Meeting of the International Color
Association in Zurich, Switzerland on 07-10 June
2011. The buildings analyzed were Annunciation
Priory by Marcel Breuer in Bismarck, North
Dakota (1959-1963); Christ Church Lutheran
by Eliel Saarinen and Associates in Minneapolis,
Minnesota (1949); and Mount Zion Temple by
Erich Mendelsohn in St. Paul, Minnesota (19501954).] (See Figure 1 for light parti diagrams of
the five spaces mentioned above.)
2. CHURCH OF THE EPIPHANY
In a religious context, light and golden color
abound - think the Egyptian Sun God, Ra, or the
shining flat halos of Cimabue or gold leaf sheets
caked on auspicious statues by the devout
people of Thailand, to only name a few [1]. More
specifically in religious architecture, controlled
and golden light makes its divinely meaningful
36
presence felt more often than not. The crossshaped architectural plans of cathedrals were
oriented with the altar area space facing toward
the East; this placement allowed for light to
shine through the stained glass windows and
illuminate the holy altar area to magnify the
divine effect. This tradition of strategic lighting
carries on in Modernist architecture, albeit with
varied mid-century twists. So much so that long
before a visitor enters the case-study Church of
the Epiphany, the light scoops territorialize the
exterior with their powerful presence.
Walking up Second Avenue from the South, the
Church of the Epiphany’s spiky tower slices
into the airspace above, flanked on the lower
left by a large, thick tower blunted on angle at
the top. (See Figures 2 and 3.) Another robust
angled mass sits to the right of viewer entry
(although the visual presence of this tower is
more noticeable from the Western approach,
due to current landscaping conditions). The
visitor discovers, once inside, that the two
squat towers are the light wells for the holy
niches edging the entry and sanctuary spaces:
the baptismal font and side prayer chapel. The
strong massing presence of the light scoops on
the exterior spells out the importance of light as
a design feature.
The Church of the Epiphany’s light scoops emit
low drama and even - in spring afternoon light
- anticlimactic, rather prosaic, and ineffectual
actual light. Yet, the presence of skylights
(which means a visual absence of ceiling)
above the baptismal font, side prayer niche, and
front altar still offers a perceived closer (and
less impeded) connection to the divine forces
above. (See Figure 4.) The disappointing interior
luminance from the light scoops may have
been an intentional design choice to convey a
religious message of darkness and sin. Or, more
likely, the light quality may be partially due to
grimy and barred skylights in need of cleaning
or poor placement of the skylights themselves.
The light quality also suffers from the church
site - hemmed in by the tall New York City
skyline on surrounding blocks – as is typical in
this crowded cityscape. (See Figure 5.) The sun
just can’t angle in as directly here as at Kresge
Chapel where the site is much more open –
even allowing light to bounce in from ground
level, as will be discussed below. [The above
Cultura e Scienza del Colore - Color Culture and Science | 04/15
Figure 1 – Diagrams of the sculpting
of light in the five spaces. Clockwise
from top left - Kresge Chapel facing
the altar, sectional view of Mount Zion
Temple, sectional view of original
chapel at Annunciation Monastery,
Church of the Epiphany facing the
altar, and Christ Church Lutheran
facing the altari.
interpretation may, in fact, be too charitable. It
could also be argued that the space ineffectively
makes use of the promise of light shown in the
exterior massing and that the building, as a
whole, fails to move visitors in a way that could
be defined as positive enough to encourage a
return visit - except out of religious obligation.]
Belfatto and Pavarini’s church provides almost
no sense of intimacy, even in the offset niches
that end up feeling more like vacuous caverns or
over-scaled chemistry test tubes. In this context,
the word “masses” offers an interesting double
meaning: the name of the scheduled religious
services and also the idea of throngs of people
that function more as numbers than as shining
individuals.
[In fact, the church offers an emotive feeling
similar to Fascist architectural masterpieces such
as the Milano Centrale train station. As a traveller
emerges from long escalator tube at the bustling
Italian thoroughfare, the transit tunnel frames a
first exterior view: a grand horse statue foaming
at the mouth. Navigating the tight underground
passage only to be emotionally trampled upon
by a fiery horse, the visitor ingests a palpable
message of powerlessness. Once past the huge,
seemingly threatening equine and inside the
station, the vibe of vulnerability continues for the
visitor waiting for a train to arrive. The Fascist
iconography and strong frieze imagery, the large
shell of space at an inhuman scale, and the
whirring noise of people and trains reverberating
in the circulation hall encourage the tangible
perception of the individual self as one meek,
weak entity in a vast system.]
The light inside the Church of the Epiphany,
during mass on a spring Saturday afternoon,
showcases – in the midst of this empty
inhumane space – spasmodic glimmers of glory
and a glimpse of hope with shining saturated
greenish yellow rays hitting parishioners during
the service. (See Figures 6 and 7.) Here, select
04/15 | Cultura e Scienza del Colore - Color Culture and Science
37
Figure 2 – Approach to Church of the
Epiphany
Figure 3 - Light wells expressed in
building massing
Figure 4 - Cross-shaped skylight
above altar (Church of the Epiphany)
Figure 5 - Yellow glow in upper right
coming from lower left (Church of the
Epiphany)
groups of parishioners are momentarily lit with
the sun’s angled light, sparking a slightly upbeat
note in the sanctuary space. This color moment
seems to convey that the presence and prayer
of individuals matters for a bit. The parishioners
embalmed in the glow seem momentarily not
nameless, special and bright even, in the midst
of this large, dark building envelope.
Yet, the chromatic light loses some momentum
as it bounces off of neutral, unmemorable
floor materials and on the procession of drab
brickwork wrapping around the sanctuary. The
cool and warm grey pavers in the walkways
soak away some of the light color, as the waxy,
nearly matte finish dulls this light from the
window. (See Figures 8 and 9.)
Note here that the yellow rays have a greenish
cast. The resultant sour yellow provides just a
tinge of the upbeat positivity of yellow hues.
But, mixed with a dour, almost sickly green, the
yellow is depleted by a wash depicting a color of
physical weakness (as in nauseous or ill). Just
as the repeated rituals of repentance, expected
obedience, and required presence at mass of
the Catholic faith provoke human guilt, so too
can the yellow-going-green hue convey ideas of
a flawed people, at the whim of God, clearly here
to meticulously serve God’s will in hopes of a
personal redemption.
The color and light zones – spaced far apart
and composed of matched up jagged shapes
of glass in Belfatto and Pavarini’s compound
- feel sparse and inadequate although they
take up a significant amount of wall square
footage. The windows, lighting the main space,
are supplemented by the rigid grid of crossshaped pendant lighting hung from above. Even
the mundanely wide circulation routes leave
the visitor lacking fulfillment. Alas, the sense
of order, controlled shaping, and scale convey
an inapproachable, all-powerful message of
God. Gratification does not seem to be lastingly
calculated into the design of this dark and hollow
spatial experience. As such, the light and scale
design choices in this vastly impersonal church
space can be interpreted as an obvious, but
important, physical manifestation of prominent
views of the Catholic Church.
All is not lost (with or without God), as it can
be argued that the Church of the Epiphany –
although miserable to visit and disappointing
on the interior - paved the religious road for
churches to come.
3. KRESGE CHAPEL
If the Church of the Epiphany embodies a
hollowness and, at best, a lacking rewardverging-on-punishment, tidy Kresge Chapel
offers a potent, radiant counterpoint. Critics
argue that architect Eero Saarinen’s religious
38
Cultura e Scienza del Colore - Color Culture and Science | 04/15
spaces “adopted open or centralized plans
and iconic forms that unified clergy and
congregation, performer and audience” [4].
This intimacy in plan is reiterated in sectional
design and material choices to offer the visitor
an experience akin to the comforting embrace of
a close friend – an escape from worldly affairs
and from the intellectual pursuits that abound on
a renowned campus such as the Massachusetts
Institute of Technology.
A visitor approaching Kresge Chapel from the
South naturally follows a prescribed path along
a long walk. A strong wall along the East side
of the land and a grove of small trees define
and favorably isolate the chapel site from the
enormous expanse of the university campus.
After entering the building, the circulation
route to the chapel forces the visitor to take a
sharp left through a naturally lit hallway. This
physical movement – a sharp turn – encourages
a change in perception. A visitor can abruptly
leave the day’s trivialities aside, perhaps setting
the scene for shifting into a mode to ponder
spiritual existence.
The hallway wall is covered in nearly
monochrome green abstract stained glass,
seemingly verdant. Upon crossing the threshold
into sanctuary space, the ceiling height rises. The
area is darker than the adjoining hallway, and
eyes must adjust to absorb the essence of the
space. The outside goings on of undergraduates
and backpacks, mere steps away, feel remote,
separate from this interior cavity. Here inside,
the altar area is lit strong and specific from
above. The side lighting seems muted, eerie,
almost intangible, and elusive in quality. In only a
few paces, there has been a strong shift in light
quality and spatial experience.
The sanctuary volume itself rests elegantly
simple and stoically geometric. The cylindrical
building envelope wraps a very small space,
approximately 54 feet in diameter [5]. A mere
120 or so seats are set within the space; the
chairs are easily movable and, although ordered,
do not seem to be ruthlessly arranged. The
chairs - caned with a rough straw - remind of
the casual, modest pragmatism of a visit to a
Scandinavian country cottage in midsummer. The
interior feels flexible, not constrained by certain
religious dogma, yet still specific, spiritual, and
memorable. The interior wall undulates lightly,
further exposing prominent textural variations
in the texture of the brickwork. With a lack of
religious iconography inside the sanctuary itself,
the decor consists solely of material color and
light variations.
Once again, as at the Church of the Epiphany, a
light well is located directly above the altar. Here,
however, the oculus-shaped puncture in the
building envelope provides a hotter, buffered yet
concentrated light. This light reflects off of the
04/15 | Cultura e Scienza del Colore - Color Culture and Science
Figure 6 - Yellow glow hitting the
parishioners’ bodies (Church of the
Epiphany)
Figure 7 - Yellow glow on floor
(Church of the Epiphany)
Figure 8 - Light from North-facing
window with candle glow in entry
39
light, a sense of nurturing softness around the
sides of the space.
For the building’s design, architect Saarinen
stated that he was looking to recreate a
travelling moment in the Greek isles. On a
night in Sparta, he spotted the moon above
in combination with a faint illumination along
the horizon. Saarinen looked to reproduce this
“other-worldly sense” here in the Kresge Chapel
experience [6]. The resultant experiential
interiorscape shines with light from above and
soft enveloping halos from the ground. The
small, cylindrical volume of the interior, the
quality of light, and the warmth of materials in
the sanctuary space create a meditative quality
and a soft attitude of inclusiveness, reinforcing
the non-denominational principles of this space.
Figure 9 – Approach to Kresge Chapel
Figure 10 - Light wells from exterior
(Kresge Chapel)
4. ANALYSIS
Figure 11 - Chapel of St. Ignatius by
Steven Holl in Seattle, Washington)
shiny, Harry Bertoia-designed metallic sculpture
streaming down from above. So, too, the marble
altar, up on a platform, reflects light off of its
smooth shiny whitish surface. (See Figure 10.)
This combination of color, light, and material
leaves the altar area highly charged, giving off
a blinding glow.
A series of lower light scoops surround the base
of the building, allowing light to bounce off of the
exterior moat and into the cylindrical volume.
The lower windows themselves hide from the
view of seated parishioners with a half wall,
allowing for the light of the overhead oculus to
take the foreground while the moat-reflected
light gently illuminates lower portions of the
surrounding enclosure. The oculus focuses
attention; the lower light wells provide ambient
40
Comparison to contemporary religious “color
containers” in varied geographical locations and
time periods can identify precedents, legacies,
and effects of the modernist spaces under
scrutiny.
The breaks in the building envelope (of both
Kresge Chapel and Church of the Epiphany)
clearly reference windows in early churches
(from thick walled Romanesque cathedrals to
lacy Gothic monstrosities). The wall punches
remind, to be sure, of Modernist icons such
as Le Corbusier’s Notre Dame du Haut to
contemporary holy venues such as Fuksas
Architects’ gem in Folignio, Italy [7]. [See
Figure 11.] Indeed, the ceiling punctures at
Rafael Moneo’s Iesu Church in San Sebastian,
Spain – as in a myriad of sacred shelters
including Belfatto and Pavarini’s New York City
venue – float above to illuminate the altar and
anciliary chapels [8]. And, Tadao Ando’s famed
glowing gapped cross in the wall at Church of
Light proves a minimalist interpretation of its
precedent windowed cross on the sanctuary
ceiling plane at the Church of the Epiphany. (The
design also nicely incorporates the religious
traditions of facing churches to the East
which here “allows for light to pour into the space
throughout the early morning and into the day, which
has a dematerializing effect on the interior concrete
walls transforming the dark volume into an illuminated
box”) [9]. This combination of sacred light effects
proves fruitful in an edgy, Modernist way. These
buildings, and many more religious structures,
access that Modernist sacred space pile of
techniques with shining results.
Projects such as the much lauded crape by
Steven Holl in Seattle, Washington (1994-1997)
blend atmospheric qualities with conceptual
qualities to drag the sacred light toolbox solidly
into contemporary creative space. The design of
this Seattle masterpiece focuses on seven light
Cultura e Scienza del Colore - Color Culture and Science | 04/15
wells. Holl slyly angles the light wells to produce
an inventive atmospheric space; light dumps in
from above from each of the cardinal directions.
To boot, the seven light chutes directly reference
the seven tenets of the chapel’s branch of
Catholicism, highlighting different facets of
Catholicism than the ones spatially exemplified
at the Church of the Epiphany. The conceptual
and experiential results are described as:
Seven bottles of light in a stone box; the
metaphor of light is shaped in different
volumes emerging from the roof whose
irregularities aim at different qualities
of light: East facing, South facing, West
and North facing, all gathered together
for one united ceremony. Each of the
light volumes corresponds to a part of
the program of Jesuit Catholic worship.
The south-facing light corresponds to
the procession, a fundamental part
of the mass. The city-facing north
light corresponds to the Chapel of the
Blessed Sacrament and to the mission
of outreach to the community. The
main worship space has a volume of
east and west light. At night, which is
the particular time of gatherings for
mass in this university chapel, the light
volumes are like beacons shining in all
directions out across the campus [10].
Further exploration, outside of the space allotted
in this particular paper, could analyse these
religious spaces during many times of the day
and over the course of all of the seasons in the
year. This data could provide a counterpoint to
possible weather patterns and varied lighting
conditions that may alter visitor experience
on more inconsistent visits. Exploration of
the space during all hours of religious service
(and even during the hours occupied by the
clergy and staff) could provide a further wealth
of information on ritual and use in relation to
color and light decisions. Critical examination
could then extend from the visual analysis
methodologies presented here to scientific color
and light data collection of units of luminance in
conjunction with calculations compensating for
sun angles, roofing material surfaces, incoming
light from nearby surfaces (such as a mirrored
skin of a skyscraper or a snow covered ground),
and similar.
More words (or possibly volumes of text) are
surely needed to tackle the historical iterations
of items in the toolbox, as this paper can only
gloss over several rich and varied threads
of thought. A thorough scouring of Western
religious buildings throughout the entirety of
architectural history could further enlighten on
chronological developments and refinements in
techniques outlined here, as well as point out
some innovative one-off moments or oddities in
Figure12 – Toolkit for Sacred Light
The chapel’s design brilliantly reinforces Jesuit
Catholic religious ideologies with built form and
light manipulation. Just as at Kresge Chapel, the
lighting inspires in both its spirituality and its
ethereality:
“What makes the interior so arresting and
enigmatic are the halos of softly pigmented
light sliced through by shocking patches of
otherworldly color” [11].
Not only does the building’s concept intellectually
captivate, but the space haptically fulfuls sensory
input channels of those who inhabit it. As such,
this magnificent pairing of exquisite space and
idea offers intellectual and experiential rewards
in divine abundance.
5. CONCLUSION
A case-study comparison of the atmospheric
effect of the manipulation of color and light
of these two American East Coast Modernist
religious spaces provides a toolbox of techniques
for use of color and light in contemporary
design. (See Figure 12.) As outlined above, these
techniques can create heightened experiential
spaces, manifest a client’s religious ideology,
and convey large scale socio-cultural messages.
04/15 | Cultura e Scienza del Colore - Color Culture and Science
41
the continuum of religious space design.
As more chromatic explorations launch in
mainstream contemporary architecture and
interiors (and as Mid-Century Modernism has
become a popular contemporary cultural trend),
designers need more wealth and variety in
color application for the creation of emotionally
loaded spaces. Technology forces our world
population to have screen-based (and often
hand-held) experiences of color and light at a
nearly constant frequency. For fulfullment and
inspiration, people need physical environments
offering tactile and encompassing atmospheric
experiences as well. This vein of research
builds more robust repertoires of techniques for
successfully manipulating color with meaningful
intentionality in spiritual and secular spaces of
the present day and beyond.
BIBLIOGRAPHY
[1] But, first, let us review that the long, culturallyestablished tradition that light = sun = divinity /god/
goodness/glory. To examine just a few of the roots of this
narrative tradition, remember that in fairytales, gold is
the symbol of good. Heros and heroines are gold-haired,
wearing gold garb and weaponry, sprinting horseback
in metallic forests. Even from southern climes where
towheads are less common, the fairytale protagonists
are frequently blonde. The presence of the coloring and
light reflectivity of these golden items symbolizes the
radiance of the sun. So, too, in contemporary culture’s
modes of storytelling, the Hollywood cinema technique of
backlighting heros and heroines exemplifies this method
of heightening a perception of a figure’s glory and/or
goodness [2] [3].
42
[2] Harry M. Benshoff, Sean Griffin. America on Film:
Representing Race, Class, Gender, and Sexuality at the
Movies. Malden, MA: Blackweel Publishing, 2004, p. 236.
[3] Max Lüthi, The Fairytale: As Art Form and Portrait of
Man. Translated by Joh Erickson. Bloomington, IN: Indiana
University Press, 1984, pp. 13-16.
[4] Walker Art Center and Minneapolis Institute of Art, Eero
Saarinen Shaping the Future, No. 72, Minneapolis, MN:
Walker Art Center and Minneapolis Institute of Art, June
6, 2008, p. 3.
[5] www.kubuildingtech.org.
[6] Antonio Roman, Eero Saarinen: An Architecture of
Multiplicity. New York: Princeton Architectural
Press, 2003, p. 14.
[7] http://www.designboom.com Andrea DB, “Doriana
Fuksas Interview”, August 26, 2011.
[8] http://www.architectural-review.com/ Michael Webb.
“Raphael Moneo’s Iesu Church in San Sebastian.”
[9] http://www.archdaily.com/ Andrew Kroll, AD Classics:
Church of the Light/Tadao Ando, January 6, 2011.
[10] http://www.stevenholl.com/
[11] http://www.stevenholl.com/ quoting Sheri Olson from
Architectural Record, July 1997.
Cultura e Scienza del Colore - Color Culture and Science | 04/15
How blue is azzurro?
Representing probabilistic equivalency
of colour terms in a dictionary
1. INTRODUCTION
Semasiological dictionaries both store and
present information about source language
words, including their meanings and targetlanguage equivalents. Since this is what
dictionary users are consulting a dictionary
for, it is the natural way of presenting the
dictionary. For compiling and storing dictionary
data, however, the solution is less than optimal.
The core of the semasiological dictionary
data structure is a one-to-many (1:n) relation
between words and meanings, i.e. one word can
have several meanings, while every meaning has
exactly one (source-language) word. In situations
of synonymy, information (e.g. definitions and
equivalents) must be repeated in each synonym
entry, or synonym entries must refer to each other.
For both methods or any combinations thereof,
semasiological compilation has been shown to
cause problems: broken references, synonym
conflicts, circularity and inconsistency [1]–[3].
Many of these problems can be avoided using
the onomasiological approach for compilation,
even if published semasiologically. There is still
a 1:n relation, but in the opposite direction: one
concept can have multiple designations, while
each word has exactly one concept it refers to.
Of the problems listed above, the
onomasiological data structure makes
broken or circular references and synonym
conflicts impossible. It still allows inconsistent
information to be entered for similar concepts,
though. Inconsistencies could only be avoided
by systematic terminology work [4], [5]. In terminology, onomasiology is the preferred
data structure both in the classical theory
[6], [7] and many contemporary approaches
[8]–[11]. Due to its limited scalability [12],
systematic terminology work is less universally
recommended, but has still been successfully
used in specialised dictionaries [13], [14]. As a
more workable alternative, a partially systematic
approach has been used for smaller groups of
concepts within a dictionary (many dictionaries
of the TSK, e.g. [15]).
Onomasiology is much less known and
understood in general lexicography (a notable
exception being the Wordnet lexical database
[16], [17], which is both onomasiological and
partially systematic). In what follows, we argue
that taking concepts into account is as feasible
and beneficial for the compilation of general
language dictionaries as it is in terminology.
We start by analysing the equivalents in bilingual
dictionaries between Spanish, Italian, English
and Estonian, finding that dictionary entries are
inconsistent, circular and lacking discriminative
information. Moreover, they also contradict
the results of our experiments in Castilian
Spanish [18] and Standard Italian [19] using
the empirical-cognitive field method [20], [21],
which we briefly describe in Section 4.
Arvi Tavast
[email protected]
1,2
Mari Uusküla
[email protected]
1,3
Dept. of Translation Studies,
School of Humanities,
Tallinn University
2
Institute of the Estonian
Language, Tallinn
3
Dept. of Estonian,
University of Tartu
1
To conclude, we present extracts from the
results of our fieldwork as probabilistic
conceptual graphs, representing an n:m relation
between words and concepts and encoding the
likelihood of a word designating a concept. We
propose this data structure as an alternative
to the 1:n structures of both semasiology and
onomasiology, arguing that it is more robust
than the former and more intuitive for the
lexicographer than the latter.
2. METHOD OF THE
DICTIONARY STUDY
We analysed the dictionary equivalents for the
Castilian Spanish terms violeta, morado and lila
designating purplish colours, and the Italian
terms blu, azzurro and celeste designating bluish
colours, which are well known for their lack
of direct equivalents in other languages (we
purposefully avoid glossing the example terms
throughout the article.). The second languages
of the bilingual dictionaries were English and
Estonian. The dictionary sources are listed in
[22]. The procedure was the following:
1. Look up the headwords in Spanish
and Italian, getting the English and
Estonian equivalents for each.
2. Look up these equivalents in the
opposite language direction, getting
their back-translations into Spanish
and Italian.
3. Present the results as a directed
graph of word equivalence relations.
4. Weigh the edges of the graph
according to the number of dictionaries
that contained this particular relation.
04/15 | Cultura e Scienza del Colore - Color Culture and Science
43
Variation of hyphenation and parentheses was
ignored.
3. RESULTS OF DICTIONARY STUDY
Figures 1-4 present the results of the dictionary
study. Estonian dictionary data is more sparse
due to the smaller number and volume of
dictionaries with this language. Dictionaries
between English and Spanish (Figure 1) stand
out in terms of having a clear convergence
of equivalent pairs across dictionaries. The
remaining three language pairs exhibit much
more variation. The following can be observed
to some degree in all four language pairs:
• Equivalents tend to
orthographic
similarities
languages.
follow
across
• Headword selection is not
comprehensive, with even the most
frequent colour terms sometimes
missing from dictionaries.
• Dictionaries of a single language
direction contradict each other and do
not justify their choice of equivalents,
leaving the user with a seemingly
random set of equivalent candidates.
• Dictionaries of two opposite language
directions contradict each other.
In Spanish-Estonian dictionaries, the term
morado could be either punakasvioletne ‘reddish
purple’, tume punakaslilla ‘dark reddish purple’,
tumelilla ‘dark purple’ or the Estonian basic
term lilla ‘purple’. The term violet could be
violett, violetne or lilla (the difference between
violett/violetne and lilla in Estonian could mainly
be accounted for through people’s idiolects or
individual preferences (see more on Estonian
purple terms in [23]). The term lila could be
(hele)lilla or lilla. Each of the three terms was
absent from at least one of the dictionaries.
According to the dictionaries, all three Spanish
terms could correspond to the Estonian basic
term lilla. In Estonian-Spanish dictionaries the
term lilla had also a many different counterparts
Figure 1 – Purplish colours in
Spanish-English and English-Spanish
dictionaries
Figure 2 - Purplish colours in SpanishEstonian and Estonian-Spanish
dictionaries
44
Cultura e Scienza del Colore - Color Culture and Science | 04/15
(violado, violáceo, de color lila, violeta, lila, morado),
without any explanation about the conceptual
differences. The terms tend to get counterparts
through homography, perhaps neglecting the
spectrum that the colour terms represent. We
could think that the similar word shape is due
to the same etymological background and that
assures the counterparts, but languages develop
differently and words, though from the same
source, develop different meanings through
cultural impact [24]. For example, Spanish
violeta and lila have a very transparent source.
However, morado, being a loan from Latin mōrum
‘mulberry’ has a very deep cultural meaning.
Perhaps the cultural importance and a different
origin of morado has made the terms for purple
divide differently from other languages.
4. METHOD OF THE FIELDWORK
The field data was obtained using an empiricalcognitive field method following [20], [21]. The
method consists of two tasks. In the list task, the
participants were asked to name all the colour
terms they could think of. In the colour-naming
task, 65 matt-surfaced coloured stimuli from
the Color-aid Corporation 220 selection were
presented to the participants one by one in a
random sequence and they were asked to name
the perceived stimuli with the appropriate colour
term . 65 stimuli were 5x5 cm plywood squares
constituting a “coarse, but evenly spread
sample of colour space” ([21]; for seletion
criteria see [25]). Color-aid is based on Ostwald
colour system: each colour can be described
by CIE coordinates available in [25]. The tiles
were shown to participants in a random order
in natural daylight on a neutral gray surface
(comparable to Munsell N2). Lighting conditions
were similar for all participants. The participants
were allowed to use simple words, compounds
or even phrases.
The Standard Italian data was collected between
2006 and 2008 in Florence (102 participants,
56 female, age range 11-80, mean 38.6). The
Castilian Spanish data was collected in 2012
in Madrid (38 participants, 20 female, age
range 22-85, mean 42.7). Participants were all
Figure 3 – Bluish colours in ItalianEnglish and English-Italian dictionaries
Figure4 - Bluish colours in ItalianEstonian
and
Estonian-Italian
dictionaries
04/15 | Cultura e Scienza del Colore - Color Culture and Science
45
volunteers with different dialectal, educational
and occupational backgrounds (for details see
[19]). The interviews were carried out in Castilian
Spanish or Standard Italian by a proficient L2
speaker. Estonian data was collected in 2014 (20
participants, 12 female, age range 25-48, mean
31.7). All participants had normal colour vision,
as ascertained using either the City University
[26] or Ishihara’s [27] colour vision tests
5. FIELDWORK RESULTS
In the list task, we calculated the naming
frequency of a colour term, its mean position in
the list and the cognitive salience index which
unifies these two parameters [28]. In the colour
naming task, we took into account the term
frequency, the number of tiles assigned to each
colour term, and calculated dominance and
specificity indices [20] to examine the consensus
rate among the participants [18], [29]. We
observe which Color-aid tiles the Castilian
Spanish terms for purple and Standard Italian
terms for blue are attached to. Spanish morado
was given with the highest frequency to tiles
VBV (60%) and VRV (58%), violeta to tile VBV-T4
(42%) and lila to tile VRV-S3 (37%). Italian blu
was most frequently attached to colour tile BVB
(named by 54% of participants), while BGB-T3
was regarded as celeste by 57% of respondents.
Azzurro was used to describe colour tile BGB by
44% of participants.
Figures 5 and 6 represent extracts from our
fieldwork results. The graphs differ from the
bilingual graphs of Figures 1-4 by the addition of
concepts (here represented by codes of colour
stimuli) between the languages. Edge weights
encode the percentage of respondents naming
this stimulus with this term and were cut off
at 10% (which is why the total of weights for
each stimulus is generally less than 100). The
graphs contain all stimuli and terms within 3
hops from the original terms, resulting in the
inclusion of some terms that intuitively would
not belong there. Italian verde, for instance, is
included among the bluish colours on Figure 6
because 10% of Estonian respondents called
the stimulus BG-S2 sinine ‘blue’ and 24% of the
Italian respondents called the same stimulus
verde.
6. DISCUSSION
The observed overlaps across categories and
shifts across languages illustrate the inadequacy
of direct univocal equivalences postulated in
dictionaries. Colour terms are an exceptionally
easy semantic domain to perform such analysis
on, due to the relative ease of presenting colour
stimuli to participants - a similar experiment
with modal verbs or abstract nouns would be
quite complex if not impossible.
However, the same overlaps and shifts are
still there regardless of how much is known
about the concept, causing the same dangers
of misrepresenting linguistic reality in the
dictionary. This paper suggests that words
should be related to each other only through
concepts, since direct relations (synonymy
and equivalence) are not flexible enough to
Figure 5 – Purplish colours in Spanish
and Estonian fieldwork results
46
Cultura e Scienza del Colore - Color Culture and Science | 04/15
represent the complexity of natural language.
We hope that our solution to using an n:m
relation between words and concepts instead
of the 1:n of traditional onomasiology will make
the approach less daunting to lexicographers by
removing the need to have separate headword
entries for each meaning of a word.
We used probabilistic weighting on our graphs
to account for the fact that one meaning of a
word can be more likely than another. Here
the probabilities were obtained from elicited
performance experiments, but other sources
could be used as well, some of which do not
depend on the semantic class, e.g. vector
semantics or word-level alignment of parallel
corpora.
Our fieldwork results are consistent with
previous studies that underline the special
status of blue category in Italian (e.g. [19], [30]–
[32]). The status of the purple category has been
discussed by [34] and [35]. While [35] regard
morado and violeta as synonyms, our field data
indicates that these colour terms have different
conceptual references.
Our probabilistic conceptual graphs are similar
to Dyvik’s semantic mirroring [36], [37] and
the workflow of the EFNILEX project [38], [39],
differing from them by the explicit addition of
concepts into the graph instead of relying on
isolation of subgraphs to identify synsets. The
objectives of [37] do include obtaining relations
between the explicit concepts of Wordnet from
parallel corpora; what we add is retention of
the probability information rather than reducing
it to discrete relations. Finally, from systematic
terminology work ([4], [5]) we differ by the use of
probabilities, but also the n:m relations between
words and concepts.
ACKNOWLEDGEMENTS
We are indebted to all our Castilian Spanish,
Standard Italian and Estonian test participants,
and to Triin Kalda who helped to collect the
Estonian data.
NOTES
1
To be precise, concepts are subjective abstractions of indvidually
perceived or imagined objects, dependent on a wide range
of variables from medical issues to life experience to cultural
norms, not the physical objects (colour cards) themselves. The
experimental stimuli merely invoke the processes of perception and
categorisation, which can then be followed by finding and uttering a
name for the resulting concept. The variation in naming includes the
variation in perception, and since the experimenter has no access
to subjective processes of the participant, these two can not be
separated in the current experiment. In any reference to colour (or
any other phenomena) in this paper, perception is always implied to
be present as an additional degree of freedom. The use of colour
tiles as stimuli in this study is only motivated by the fact that there is
much less room for variation in the perception of colours compared
to the perception of e.g. kindness or running or even bird or table.
BIBLIOGRAPHY
[1] A. Tavast, “Eesti oskussõnastikud 1996-2000,” Keel ja
Kirjandus, vol. 6, 7, pp. 401–414, 489–503, 2002.
[2] A. Tavast, The Translator is Human Too: A Case
Figure 6 – Bluish colours in Italian and
Estonian fieldwork results
04/15 | Cultura e Scienza del Colore - Color Culture and Science
47
for Instrumentalism in Multilingual Specialised
Communication. Tartu: Tartu Ülikooli Kirjastus, 2008.
[3] A. Tavast, “Eesti oskussõnastikud 2001–2010,” Keel ja
Kirjandus, vol. 4, pp. 255–276, 2011.
[4] S. E. Wright and G. Budin, Handbook of Terminology
Management:
Application-oriented
Terminology
Management. Amsterdam: John Benjamins, 2001.
[5] H. Suonuuti, Guide to Terminology. Helsinki: Tekniikan
Sanastokeskus, 2001.
[6] E. Wüster, Einführung in die allgemeine
Terminologielehre und terminologische Lexikographie.
Dordrecht: Springer, 1979.
[7] H. Felber, Terminology Manual. Paris: UNESCO, 1984.
[8] R. Temmerman, Towards New Ways of Terminology
Description: The Sociocognitive Approach. Amsterdam:
John Benjamins, 2000.
[9] M. T. Cabré, La terminología: representación y
comunicación : elementos para una teoría de base
comunicativa y otros artículos. Girona: Documenta
Universitaria, 1999.
[23] V. Oja and M. Uusküla, “Mõnest värvinimetusest ja
nende tähendusvahekordadest eesti ja soome keeles,”
Eesti Rakenduslingvistika Ühingu aastaraamat, no. 6, pp.
195–205, 2010.
[24] C. P. Biggam, The semantics of colour: a historical
approach. Cambridge University Press, 2012.
[25] I. R. Davies, C. Macdermid, G. G. Corbett, H. McGurk, D.
Jerrett, T. Jerrett, and P. Sowden, “Color terms in Setswana:
a linguistic and perceptual approach,” Linguistics, vol. 30,
no. 6, pp. 1065–1104, 1992.
[26] R. Fletcher, The City University Colour Vision Test, 2nd
ed. 1980.
[27] S. Ishihara, Ishihara’s tests for colour-deficiency.
Kanehara & Company, 1996.
[28] U. Sutrop, “List task and a cognitive salience index,”
Field Methods, vol. 13, no. 3, pp. 263–276, 2001.
[29] M. Uusküla, “Mediterranean Ecology and the colour
blue in Standard Italian.,” in The Language of Color in the
Mediterranean, 2nd ed., forthcoming.
[10] P. Faber Benítez, “The cognitive shift in terminology
and specialized translation,” MonTI. Monografías de
Traducción e Interpretación, no. 1, pp. 107–134, 2009.
[30] G. Paggetti, G. Menegaz, and G. V. Paramei, “Color
naming in Italian language,” Color Res. Appl., p. n/a–n/a,
Feb. 2015.
[11] P. Faber Benítez, A Cognitive Linguistics View of
Terminology and Specialized Language. Berlin: Walter de
Gruyter, 2012.
[31] D. Bimler and M. Uusküla, “‘Clothed in triple blues’:
sorting out the Italian blues,” JOSA A, vol. 31, no. 4, pp.
A332–A340, 2014.
[12] H. Picht and J. Draskau, Terminology: An Introduction.
Guildford: University of Surrey, 1985.
[32] G. V. Paramei, M. D’Orsi, and G. Menegaz, “‘Italian
blues’: A challenge to the universal inventory of basic
colour terms,” JAIC - Journal of the International Colour
Association, vol. 13, 2014.
[13] E. Wüster, The Machine Tool: An Interlingual Dictionary
of Basic Concepts: Comprising an Alphabetical Dictionary
and a Classified Vocabulary with Definitions and
Illustrations. London: Technical Press, 1967.
[14] Eduskuntasanasto = Riksdagsordlista = Finnish
Parliamentary Glossary = Soome Parlamendi
Seletussõnastik. Helsinki: Eduskunta, 2008.
[15] T. S. K. Sanastokeskus, Geoinformatiikan sanasto.
Sanastokeskus TSK, 2011.
[16] G. A. Miller, “WordNet: a lexical database for English,”
Communications of the ACM, vol. 38, no. 11, pp. 39–41,
1995.
[17] C. Fellbaum, WordNet. Dordrecht: Springer, 2010.
[18] K. Parker, “Hispaania värvinimed sõnastikes ja
mentaalses leksikonis,” MA thesis, Tallinn University,
Tallinn, 2013.
[19] M. Uusküla, “Linguistic categorization of blue in
Standard Italian,” in Colour Studies: A broad spectrum,
John Benjamins Publishing Company, 2014, pp. 67–78.
[20] I. Davies and G. Corbett, “The basic color terms of
Russian,” Linguistics, vol. 32, no. 1, pp. 65–90, 1994.
[21] I. R. L. Davies and G. G. Corbett, “A practical field
method for identifying probable basic colour terms,”
Languages of the World, vol. 9, no. 1, pp. 25–36, 1995.
[22] A. Tavast, M. Uusküla, K. Parker and U. Sutrop,
“Using probabilistic conceptual graphs for representing
colour terms in dictionaries,” in Color and Colorimetry
Multidsciplinary Contributions, Maggioli Editore, 2013, pp.
48
455-466.
[33] J. Sandford, “Blu, Azzurro, Celeste: what color is
blue for Italian speakers compared to English speakers?”
Colour and Colorimetry: Multidisciplinary Contributions,
pp. 281-288, 2012.
[34] L. Rello, “Términos de color en español: semántica,
morfología y análisis lexicográfico. Definiciones y matices
semánticos de sus afijos.”
[35] J. Lillo, H. Moreira, I. Vitini, and J. Martín, “Locating
basic Spanish colour categories in CIE L* u* v* space:
Identification, lightness segregation and correspondence
with English equivalents,” Psicológica, vol. 28, no. 1, pp.
21–54, 2007.
[36] H. Dyvik, “A translational basis for semantics,”
Language and Computers, vol. 24, pp. 51–86, 1998.
[37] H. Dyvik, “Translations as semantic mirrors: from
parallel corpus to wordnet,” Language and computers, vol.
49, no. 1, pp. 311–326, 2004.
[38] E. Héja and D. Takács, “Automatically generated
customizable online dictionaries,” in Proceedings of the
Demonstrations at the 13th Conference of the European
Chapter of the Association for Computational Linguistics,
2012, pp. 51–57.
[39] E. Héja and D. Takács, “An online dictionary browser
for automatically generated bilingual dictionaries,” in
Proceedings of the 15th EURALEX International Congress,
2012, pp. 468–477.
Cultura e Scienza del Colore - Color Culture and Science | 04/15
Adaptive Illuminant Estimation and
Correction for Digital Photography
ABSTRACT
1
Simone Bianco
[email protected]
1
Raimondo Schettini
[email protected]
DISCo - Dipartimento
di Informatica,
Sistemistica e Comunicazione
Università degli Studi
di Milano-Bicocca
1
In this paper we briefly review our recent research on classification-based color
constancy, where automatically extracted features are used to drive the selection
and combination of the best algorithm(s) for each image We also describe how the
problem of illuminant estimation and correction is deeply intertwined with the one of
color space transformation. Finally, we also highlight research trends in these fields.
1. INTRODUCTION
There are mainly two modules responsible for
the color rendering accuracy in a digital camera:
the former is the illuminant estimation and
correction module, the latter is the color matrix
transformation. These two modules together
form what may be called the color correction
pipeline, or color engine. A simplified processing
pipeline of a typical digital camera is reported
in Fig. 1, where the color engine is highlighted.
The first stage of the color correction pipeline
aims to render the acquired image as it was
acquired under a known canonical illuminant.
This is inspired by the color constancy feature of
the human visual system (HVS), i.e. the ability of
perceiving relatively constant colors when objects
are lit by different illuminants. In the framework
of digital imaging and computer vision, the
illuminant estimation and correction is also
referred to as white balance or computational
color constancy. The second stage of the color
correction pipeline transforms the image data
into a standard color space. This transformation,
usually called color space transformation or
color matrixing, is needed because the spectral
sensitivity functions of the sensor color channels
rarely match those of the desired output color
space. This transformation is usually performed
by using a linear transformation matrix.
Many illuminant estimation solutions have been
proposed in the last few years, although it is
known that the problem addressed is actually
ill-posed as its solution lacks uniqueness and
04/15 | Cultura e Scienza del Colore - Color Culture and Science
Figure 1 – Simplified processing
pipeline of a typical digital camera
49
stability. To cope with this problem, different
solutions usually exploit some assumptions
about the statistical properties of the expected
illuminants and/or of the object reflectances in
the scene. In this paper we briefly review state
of the art methods and our recent research on
classification-based color constancy, where
automatically extracted features are used to
drive the selection and combination of the best
algorithm(s) for each image. Then, we describe
how the problem of illuminant estimation and
correction is deeply intertwined with the one
of color space transformation. And, finally, we
highlight research trends in these fields.
2. REVIEW OF COMPUTATIONAL COLOR CONSTANCY ALGORITHMS
Computational color constancy is the process
of removing unrealistic color casts from
digital images, mostly due to the acquisition
conditions. From a computational perspective,
computational color constancy is a two-stage
process: the illuminant is estimated, and the
image colors are then corrected on the basis
of this estimate. The correction generates
a new image of the scene as if it were taken
under a known, canonical illuminant (see for
example Fig. 2). A generic image acquired by a
where w is the wavelength range of the visible
spectrum, ρ and C(λ) are three-component
vectors. Since the three spectral sensitivities of
the sensor C(λ) are usually respectively more
sensitive to low, medium and high wavelengths,
the three-component vector of the sensor
response ρ = (ρ1,ρ2,ρ3) is also referred to as
the sensor or camera raw RGB = (R,G,B) triplet
(see for example Fig. 3). Assuming that the color
I of the illuminant in the scene observed by the
camera only depends on the illuminant spectral
power distribution I(λ) and on the spectral
sensitivities C(λ) of the sensor, the computational
color constancy problem is equivalent to the
estimation of I by:
I = ∫ I (λ )C(λ )dλ
w
given only the sensor responses ρ(x,y) across
the image. This is an under-determined problem
and therefore cannot be solved without further
assumptions and/or knowledge, such as some
information about the camera being used, and/
or assumptions about the statistical properties
of the expected illuminants and surface
reflectances. The estimation of the color of the
illuminant could be performed if an achromatic
patch is present in the image. This is because
the spectral reflectance S(λ) of an achromatic
Figure 2 – The two stages of the
automatic white balance: the illuminant
is estimated, and the image colors are
then corrected on the basis of this
estimate. The correction generates
a new image of the scene as if it
were taken under a known, canonical
illuminant
Figure 3 - Image formation process
digital camera is mainly characterized by three
physical factors: the illuminant spectral power
distribution I(λ), the surface spectral reflectance
S(λ) and the spectral sensitivities C(λ) of the
sensor. Using this notation, the sensor responses
at the spatial point with coordinates (x,y) can be
then described as:
ρñ( x, y ) = ∫ I (λ ) S ( x, y, λ )C(λ )dλ
w
50
Cultura e Scienza del Colore - Color Culture and Science | 04/15
surface is approximately constant over a wide
range of wavelengths, and thus the sensor
response ρ is proportional to I, i.e. the RGB of
the achromatic patch is proportional to that of
the incident light. To reduce the dimensionality
of the problem, one common method is to not
estimate the whole triplet of the illuminant
color, but a 2D projection of it in a chromaticity
space. In fact, it is more important to estimate
the chromatic components of the scene than its
overall intensity.
The color correction is usually based on a
diagonal model of illumination change derived
from the Von Kries hypothesis. This model
assumes that two acquisitions of the same
scene with the same imaging device but
under different illuminants are related by an
independent gain regulation of the three imaging
channels [1][2]. A diagonal model is generally a
good approximation of change in illumination, as
shown by Finlayson et al.[2].
The colors in a scene, acquired under an
unknown illuminant U can be transformed as
they were taken under the chosen canonical
illuminant by:
C
U
 R'  R / R
G ' = 
  
 B' 
G C / GU
R
 
 G 
C
U
B / B   B 
where RGB = (R,G,B) is a color in the image
acquired under the unknown illuminant, RGB'
= (R',G',B') is the color in the corrected image,
RGBU = (RU,GU,BU) are the sensor responses
of a camera to a reference white under the
unknown illuminant and RGBC = (RC,GC,BC)
are the corresponding responses under the
canonical illuminant. Supposing RGBC is known,
to obtain the color correction matrix, we have
to estimate the illuminant color RGBU. To this
aim several algorithms exist in literature, each
with different assumptions [3]-[11]. To improve
the illuminant estimation, Schaefer et al. [12]
introduced a combined physical and statistical
color constancy algorithm that integrates the
statistics-based
Color by Correlation method with a physics-based
technique, based on the dichromatic reflectance
model, using a weighted combination of their
likelihoods for a given illumination set and
taking the maximum likelihood entry. Cardei and
Funt [13] obtained good illuminant estimation
by combining the results of gray world, white
patch and neural net methods, considering both
linear and non-linear committee methods. We
have investigated the idea of not relying on a
single illuminant estimation method, but instead
of considering a consensus decision that takes
into account the compendium of the responses
of several algorithms. To overcome the limitation
deriving from the estimation of the illuminant
color using the response of a single algorithm,
methods that rely on the consensus of a set of
different algorithms have been proposed [13]
[14]. The underlying idea is that algorithms
that give similar illuminant color estimations
have to be trusted more than algorithms that
give estimates that are far from the others, and
thus the latter ones have to be automatically
discarded. A different kind of algorithms
proposed in the last few years belongs to the
class of classification-based color constancy.
The key idea is to exploit automatically extracted
information about the content of the images and
intrinsic, low level properties of the images.
3. CLASSIFICATION-BASED
COLOR CONSTANCY
We have in the last years proposed three
different strategies of color constancy algorithm
selection: a class-based (CB) [15] and a featurebased (FB) [16], and face-based (FcB) approach
[17].
3.1 CLASS-BASED COLOR CONSTANCY
The class-based (CB) algorithm adopts a
classification step to assign each image to either
the indoor or to the outdoor class (see Fig. 4).
The classifier is trained on low level features
automatically extracted from the images (see
[15] for a detailed description). Two different AWB
algorithms have been used for the two possible
classes in which the image considered can
be classified: on the basis of the classification
result, only the corresponding AWB algorithm
selected has been applied. The algorithms for
the indoor and for the outdoor class are selected
from the one listed in [15], evaluating them on
an independent training set.
3.2 FEATURE-BASED COLOR CONSTANCY
The feature-based (FB) algorithm is based on five
independent AWB algorithms and a classification
step that automatically selects which AWB
Figure 4 – Pipeline of the Class-based
color constancy
04/15 | Cultura e Scienza del Colore - Color Culture and Science
51
algorithm to use for each image (see Fig. 5).
The classifier is trained on low level features
automatically extracted from the images (see
[16] for a detailed description). The features on
which the classification-based color constancy
methods rely for the image representation can
be divided into two groups: general purpose
and specifically designed features. The use of
low level features for the automatic selection
and combination of the best color constancy
algorithm (or combination of multiple algorithms)
permitted to outperform existing state of the
art algorithms on a widely used benchmark
dataset [15],[16]. The class-based and the
feature-based color constancy algorithms can
be thought as exploiting respectively high-level
and low-level features: the CB works on the
output of a scene classifier, more precisely an
indoor/outdoor classifier; the FB works directly
on the low-level features extracted from the
image. The use of medium-level features has
also been investigated [18][19]: they are used in
region-based color constancy algorithms which
are able to automatically select (and/or blend)
among different color corrections, including a
conservative do nothing strategy.
A new class of feature-based algorithms replaces
the use of hand-crafted features with an endto-end learning of features and classifiers using
Convolutional Neural Networks (CNNs) [37].
3.3 FACE-BASED COLOR CONSTANCY
Memory colors could be used as hints to give
a more accurate estimate of the illuminant
color in the scene. For example, Moreno et
al. [20] obtained memory colors for three
different objects (grass, snow and sky) using
psychophysical experiments. They then used
a supervised image segmentation method to
detect memory color objects and exploit them
to color correct the image using a weighted
Von Kries formula. A different approach has
been used in [17] where a face detector
was used to find faces in the scene, and the
corresponding skin colors were used to estimate
the chromaticity of the illuminant. The method
was based on two observations: first, skin colors
tend to form a cluster in the color space, making
it a cue to estimate the illuminant in the scene;
second, many photographic images are portraits
or contain people. An extension of this idea
could be to use “memory objects” in the scene for
color constancy. If we are able to automatically
recognize objects and logos that have intrinsic
colors, we can use them for color constancy.
The performances of the proposed color
constancy algorithms are very good and they
are fully detailed in [14]-[17]. The performance
measure adopted is the median angular
error between the estimated and measured
illuminant. The datasets used are standard and
widely for benchmarking algorithms as they are
with ground truth illuminant measurements [32].
3.4 ADAPTIVE FACE-BASED
COLOR CONSTANCY
The face-based color constancy algorithm
described in the next subsection has been
extended in different ways [33]. Since one
of the assumptions that is often violated in
color constancy is the presence of a uniform
illumination in the scene, we have extended the
applicability of the face-based algorithm to the
case of non-uniform illumination. The method is
adaptive, being able to distinguish and process
in different ways images of scenes taken under
a uniform and those acquired under nonuniform illumination. This was the first algorithm
which automatically modifies its behavior from
global to local color correction according to the
analysis of the image content.
Furthermore, we designed a more efficient
algorithm to estimate the scene illuminant from
extracted skin regions using only their mean
color value instead of the whole gamut. This
algorithm is more suitable for resource-limited
camera devices, such as consumer digital
cameras and camera phones.
4. COLOR SPACE TRANSFORMATION
The second stage of the color correction
pipeline is the device chromatic response
characterization and transforms the image data
into a standard RGB color space (e.g. sRGB,
ITU-R BT.709). This transformation, usually
called color matrixing, is needed because the
spectral sensitivity functions of the sensor color
Figure 5 - Pipeline of the Featurebased color constancy
52
Cultura e Scienza del Colore - Color Culture and Science | 04/15
channels rarely match those of the desired
output color space. Typically this transformation
is a 3-by-3 matrix with 9 variables to be
optimally determined, and both algebraic [21]
and optimization-based methods [22] exist to
find it. The typical color correction pipeline can
be thus described as follows:
where RGBin are the camera raw RGB values,
a is an exposure compensation common gain,
the diagonal matrix diag(rawb,gawb,bawb) is the
channel-independent gain compensation of
the illuminant, the full 3-by-3 matrix a(i,j),(i,j)
= {1,2,3}2 = {1,2,3}G{1,2,3} is the color
space conversion transform from the devicedependent RGB to the sRGB color space, γ is the
gamma correction defined for the sRGB color
space (where for abuse of notation it is intended
to be applied component-wise), and RGBout are
the output sRGB values. Usually the color matrix
transform is optimized for a single illuminant
and is applied as it is for all the illuminants that
can occur. This could lead to high colorimetric
accuracy if the occurring illuminant is the one
for which the matrix has been derived (assuming
that it is correctly compensated by the AWB
module), and low colorimetric accuracy for
different illuminants. In [23] we have shown
how to compute a combined matrix for different
classes of commonly occurring illuminants. If
only a-priori probability distribution about the
illuminant occurrences is known, the best color
matrix can be found offline and applied as it
is for all the shots; if the AWB is able to give
a probability distribution about the illuminant
in the scene (as color-by-correlation [9] does),
an adaptive optimal matrix transform could
be found for each shot. Starting from the
observation that the illuminant estimation is not
error free and being an ill-posed problem [14] a
perfect algorithm does not exist, color correction
matrices, in addition to color space conversion,
can incorporate information about the illuminant
estimation process in order to compensate for
its possible errors [23],[34] (see for example Fig.
6).
4.1 ADAPTIVE COLOR
SPACE TRANSFORMATION
In [35] we designed and tested extended color
correction pipelines for digital cameras able
to obtain a higher color rendering accuracy.
The pipelines proposed in [23],[34] have been
further improved in two ways: i) in the illuminant
estimation and correction stage, the traditional
diagonal model of illuminant change has been
replaced by a generalized diagonal transform
found by optimization; ii) the color matrixing
stage, usually performed using a linear
transformation matrix optimized assuming
that the illuminant in the scene has been
successfully estimated and compensated for,
has been extended exploiting polynomial color
space conversions incorporating knowledge
about illuminant estimation module behavior.
[35]
5. CONCLUSIONS
AND FUTURE WORKS
Both the illuminant estimation process and the
color correction matrix concur in the formation
of the overall perceived image quality. We have
briefly summarized our research activities
concerning them. Interested readers may
refer to the cited works to have a detailed
description of the algorithms as well as an
exhaustive comparison of experimental results
with respect to other algorithms in the state-ofart. Independently from the merits of the single
algorithms, we would like to point it out that
illuminant estimation and color correction have
Figure 6 - Computation of color
correction matrices which, in addition
to color space conversion can
incorporate information about the
illuminant estimation process in order
to compensate for its possible errors
04/15 | Cultura e Scienza del Colore - Color Culture and Science
53
been always studied and optimized separately,
thus ignoring the interactions between them. Our
works, to the best of our knowledge, are the first
ones that have investigated their interactions
and how to optimize them for the overall color
accuracy.
One of the possible further extension of these
works regards the illuminant correction step.
Once the scene illuminant has been estimated
the scene is usually corrected in the RGB device
dependent color space using the diagonal
Von Kries model [2]. Several studies have
investigated the use of different color spaces for
the illuminant correction [24],[25],[26] as well as
non-diagonal models [26]. A different approach
could be to use chromatic adaptation transforms
(CATs) [27] to correct the scene illuminant. CATs
are used in color science and color imaging to
model illumination change given the source and
target illuminants. Since in computational color
constancy the source illuminant is unknown, the
development of more accurate color constancy
algorithms will allow to use more performing
CATs [28]. A different strategy for improving
the reliability of color constancy algorithms is
to increase the quantity of information available
for the color constancy by taking two pictures
of each scene [29]: the first is taken as normal,
while a specially chosen colored filter is placed
in front of the camera when capturing the second
image. The filter is chosen so that the combined
image makes color constancy, or white point
estimation easier to solve. The availability of
more color information available could be also
used to increase the colorimetric accuracy of the
device [30] or even for a spectral reconstruction
of the scene [31]. The recent development of 3D
cameras will surely boost these approaches.
[8] G. D. Finlayson, “Color in perspective,” IEEE Trans.
Pattern Anal. Mach. Intell. 18, 1034–1038, 1996.
[9] G. D. Finlayson, P. Hubel, and S. Hordley, “Color by
correlation,” in Proc. IS&T/SID 5th Color Imaging Conf.:
Color Science, Systems and Applications, pp. 6–11, 1997.
[10] G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color
by correlation: a simple, unifying framework for color
constancy,” IEEE Trans. Pattern Anal. Mach. Intell. 23,
1209–1221, 2001.
[11] D. Forsyth, “A novel algorithm for color constancy,”
Int. J. Comput. Vis. 5, 5–36, 1990.
[12] G. Schaefer, S. Hordley, and G. Finlayson, “A combined
physical and statistical approach to colour constancy,” in
Proc. Int. Conf. Comput. Vis. Pattern. Recogn., pp. 148–
153, 2005.
[13] V. C. Cardei and B. Funt, “Committee-based colour
constancy,” in Proc. IS&T/SID 7th Color Imaging Conf., pp.
311–313, 1999.
[14] S. Bianco, F. Gasparini, and R. Schettini, “Consensus
based framework for illuminant chromaticity estimation,”
Journal of Electronic Imaging, vol. 17(2), pp. 023013,
2008.
[15] S. Bianco, G. Ciocca, C. Cusano, and R. Schettini,
“Improving color constancy using indoor-outdoor image
classification,” IEEE Transactions on Image Processing,
vol. 17(12), pp. 2381–2392, 2008.
[16] S. Bianco, G. Ciocca, C. Cusano, and R. Schettini,
“Automatic color constancy algorithm selection and
combination,” Pattern Recognition, vol. 43, pp. 695–705,
2010.
[17] S. Bianco, R. Schettini, “Color constancy using faces”,
Proceedings of the International Conference on Computer
Vision and Pattern Recognition (CVPR), pp. 65-72, 2012.
[18] F. Gasparini and R. Schettini, “Color balancing of
digital photos using simple image statistics,” Pattern
Recognition, vol. 37(6), pp. 1201–1217, 2004.
BIBLIOGRAPHY
[1] M. D. Fairchild, Color Appearance Models, Addison
Wesley, Boston, Mass. 1997.
[2] G. D. Finlayson, M. S. Drew, and B. Funt, “Diagonal
transform suffice for color constancy,” in Proc. IEEE Int.
Conf. Computer Vision, pp. 164–171, 1993.
[3] K. Barnard, V. Cardei, and B. Funt, “A comparison of
computational color constancy algorithms; part one:
methodology and experiments with synthetic images,” in
IEEE Trans. Image Process. 11(9), 972–984, 2002.
[4] K. Barnard, V. Cardei, and B. Funt, “A comparison of
computational color constancy algorithms; part two:
experiments with image data,” in IEEE Trans. Image
Process. 11(9), 985–996, 2002.
[5] A. Bruna, F. Gasparini, F. Naccari, and R. Schettini,
“Multidomain pixel analysis for illuminant estimation and
compensation,” in Proc. Digital Photography II, Proc. SPIE
6069, 60690C, 2006.
[6] V. Cardei, B. Funt, and K. Barndard, “White point
estimation for uncalibrated images,” in Proc. IS&T/SID 7th
Color Imaging Conf., pp. 97–100, 1999.
54
[7] H. H. Chen, C. Shen, and P. Tsai, “Edge-based automatic
white balance with linear illuminant constraint,” in Visual
Communications and Image Processing 2007, Proc. SPIE
6508, 65081D, 2007.
[19] S. Bianco, F. Gasparini, and R. Schettini, “Regionbased illuminant estimation for effective color correction,”
Proceedings of 15th International Conference on
Image Analysis and Processing (ICIAP 2009), LNCS, vol.
5716/2009, pp. 43–52, 2009.
[20] A. Moreno, B. Fernando, B. Kani, S. Saha, and S.
Karaoglu, “Color correction: A novel weighted von kries
model based on memory colors,” Proc. of the 2011
Computational Color Imaging Workshop (CCIW’11), vol.
LNCS 6626/2011, 2011.
[21] P. M. Hubel, J. Holm, G. D. Finlayson, and M. S. Drew,
“Matrix calculations for digital photography,” Proceedings
of the IS&T/SID Fifth Color Imaging Conference, pp. 105–
111, 1997.
[22] S. Bianco, F. Gasparini, A. Russo, and R. Schettini,
“A new method for RGB to XYZ transformation based on
pattern search optimization,” IEEE Trans. Cons. Electr., vol.
53(3), pp. 1020–1028, 2007.
[23] S. Bianco, A. Bruna, F. Naccari, R. Schettini, “Color
space transformations for digital photography exploiting
Cultura e Scienza del Colore - Color Culture and Science | 04/15
information about the illuminant estimation process”,
Journal of the Optical Society of America A, vol. 29(3),
pp.374-384, 2012.
[24] K. Barnard, F. Ciurea, and B. Funt, “Sensor sharpening
for computational color constancy,” Journal of the Optical
Society of America A, vol. 18, pp. 2728–2743, 2001.
[25] F. Xiao, J. E. Farrell, J. M. Dicarlo, and B. A. Wandell,
“Preferred color spaces for white balancing,” Proceedings
of the SPIE-IS&T, Sensors and Camera Systems for
Scientific, Industrial, and Digital Photography Applications
IV, vol. 5017, pp. 342–350, 2003.
[26] B. Funt and H. Jiang, “Nondiagonal color correction,”
Proceedings of the 2003 International Conference on
Image Processing (ICIP 2003), pp. 481–484, 2003.
[27] CIE 160:2004, “A review of chromatic adaptation
transforms,” ISBN: 9783901906305, 2004.
[28] S. Bianco and R. Schettini, “Two new von kries based
chromatic adaptation transforms found by numerical
optimization,” Color Research and Application, vol. 35(3),
pp. 184–192, 2010.
[29] G. D. Finlayson, S. D. Hordley, and P.Morovic, “Colour
constancy using the chromagenic constraint,” Computer
Vision and Pattern Recognition (CVPR’05), pp. 1079–1086,
2005.
[30] S. Bianco, F. Gasparini, R. Schettini, and L. Vanneschi,
“Polynomial modeling and optimization for colorimetric
characterization of scanners,” J. of Electronic Imaging,
vol. 17(4), pp. 043002, 2008.
[31] R. Shrestha, J. Y. Hardeberg, and A. Mansouri, “Oneshot multispectral color imaging with a stereo camera,”
Proceedings of the SPIE-IS&T, Digital Photography VII, vol.
7876, 2011.
[32] http://colorconstancy.com/
[33] S. Bianco, R. Schettini, “Adaptive color constancy
using faces”, IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 36(8), pp. 1505-1518, 2014.
[34] S. Bianco, A. Bruna, F. Naccari, R. Schettini, “Color
correction pipeline optimization for digital cameras.”
Journal of Electronic Imaging, vol. 22(2), pp. 023014,
2013.
[35] S. Bianco, R. Schettini, “Error-tolerant color rendering
for digital cameras.” Journal of Mathematical Imaging and
Vision, vol. 50(3), pp. 235-245, 2014.
[36] G. D. Finlayson, M. S. Drew, B. V. Funt. “Color
constancy: generalized diagonal transforms suffice.”
Journal of the Optical Society of America A, vol. 11(11),
pp. 3011-3019, 1994.
[37] S. Bianco, C. Cusano, R. Schettini, “Color constancy
using CNNs”, IEEE Computer Vision and Pattern
Recognition Workshops (CVPRW), 2015.
04/15 | Cultura e Scienza del Colore - Color Culture and Science
55
RUBRICA | COLUMN
Emanuela Orlando
www.orlandoprogettazione.it
RUBRICA
COLUMN
I COLORI DI
GUALTIERO
MARCHESI
GUALTIERO
MARCHESI’S
COLOURS
La prima edizione del Premio Colore della
Associazione Italiana Colore ha visto rendere
onore al lavoro di un grande artista che usa
sapientemente sapori e colori in cucina.
Le motivazioni raccontano la scelta di premiare
Gualtiero Marchesi, un cuoco, ma che cuoco!
The first edition of the Colour Price of the Italian
Colour Association has honoured the work of a
great artist who wisely uses flavours and colours
in the kitchen.
The reasons tell the choice to reward Gualtiero
Marchesi, a chef… and what a chef!!
“Il premio colore 2015 va a un grande
Maestro, al cuoco italiano più famoso nel
mondo, a colui che ha saputo rendere il
colore un tratto caratteristico e distintivo di
ogni sua preparazione, in una sintesi perfetta
tra gusto ed estetica, tra sensi e bellezza.
“The 2015 colour award goes to a great
Teacher, to the most famous Italian chef of
the world, to he who knew how to render
colours a distinct characteristic in every step
of his preparations, a perfect combination
between taste and aesthetics, between
senses and beauty.
Nelle sue creazioni il colore è un messaggio
chiaro, diretto, quasi ancestrale, una
parte complementare ed imprescindibile
nell’armonia complessiva dei sensi.
La sua tavolozza è misurata per ogni piatto,
a voler esaltare al massimo l’intenzione
poetica senza fraintendimenti, in un prezioso
lavoro di sintesi.
Ha saputo tradurre il suo amore per l’arte
in piatti memorabili plasmando le materie
prime in forme e colori che diventano a loro
volta arte, prima che cibo. Composizioni
che rendono omaggio alla tradizione dei
grandi Maestri da lui amati, uscendo dalle
tele, dai musei, per diventare esperienza
multisensoriale.
I colori di Gualtiero Marchesi nutrono gli
occhi di bellezza, nutrono di emozioni, di
cultura, di memorie … nutrono la nostra
anima prima del nostro corpo, grazie
Maestro.”
Le motivazioni del riconoscimento ci raccontano
quanta parte il colore abbia nella cucina, arte
sinestetica che coinvolge tutti i sensi a partire
dalla vista, il primo a darci informazioni sulla
qualità degli alimenti, sulla loro edibilità, ma
anche il primo senso a mostrarci la bellezza.
Marchesi cita spesso con orgoglio una frase
della figlia: “il bello puro è il vero buono” e
questo premio rende omaggio ad una sapienza
composta da immaginazione, cultura e abilità,
alla grande sensibilità artistica che Marchesi ha
saputo trasmettere ai suoi figli, nipoti e ai tanti
alunni spesso diventati cuochi famosi a loro
volta.
56
In his creations, colours always have a
direct, clear, and nearly ancestral message
that are complementary and essential parts
to the overall harmony of senses.
His palette is measured in every plate,
in order to enhance the poetic intention
without misunderstandings, in a precious
synthetic work.
He managed to translate all his love of the
art into memorable dishes, moulding his
raw materials in shapes and colours that
become first art, then food. Compositions
that render homage to the tradition of his
beloved Masters, coming out from canvases,
museums, that become an multisensory
experience.
The colours of Gualtiero Marchesi nourish
the eyes of beauty, emotions, culture,
memories.. nourish our soul before our
body, thank you Master.”
These motivations show us the importance of
colours in the kitchen, a synesthetic art that
involves every sense starting from sight, the
first to give us information on the quality of the
food, on their edibility, but also the first of the five
senses that shows us the beauty of the plate.
Often Marchesi proudly quotes his daughter’s
phrase: “the pure beauty is the real good” and this
prize is a homage to the knowledge made up
of imagination, culture and ability, to the great
artistic sensibility that Marchesi has been able to
transmit to his children, grandchildren and to his
numerous pupils, some of whom have become
great chefs themselves.
Cultura e Scienza del Colore - Color Culture and Science | 04/15
RUBRICA | COLUMN
Nella sua storia il colore è sempre presente così
come la cultura, la curiosità, l’amore per l’arte,
la musica. Così racconta nella sua autobiografia
il suo concetto di vivanda:
In his history colours have always been present
just like culture, curiosity, love for art and
music have. He tells us in his autobiography his
concepts:
«è un racconto gastronomico, un intreccio
una recitazione dove gli interpreti sono gli
ingredienti che si vivificano, si organizzano,
interagiscono in un equilibrio di sapori, di
colori, di gusto. ….»
«it is a gastronomic story, a play where
the actors are the main ingredients that
vivify, organise, interact in an equilibrium of
flavours, colours, and taste…»
Con la sua opera ha svolto un fondamentale
ruolo di catalizzatore e stimolo nell’innovazione
e modernizzazione della ristorazione italiana;
ancora dalla sua autobiografia:
With his work he has taken part in a role
as catalyst and stimulus of innovation and
modernisation of Italian restoration:
«la cucina contribuisce, e non
secondariamente, a fare da ambasciatrice
d’italianità e creatrice d’immagine. Fa parte
a buon diritto dello stile italiano al pari di
altre forme riconosciute come il design
industriale, la moda, la musica e l’arte in
generale.»
Uomo cordiale, raffinato e colto, gentile e
spiritoso, ha saputo raccontare i suoi piatti
con semplicità e ironia. Ha parlato della sua
vita di ricerca (“la cucina mi ha insegnato l’umiltà
e la curiosità”, “la cucina è scienza, sta al cuoco
farla diventare arte”) ha condiviso ricordi che
intrecciano persone, passioni ed emozioni.
“Un cuoco senza pregiudizi, un anarchico
che nel piatto riconosce solo la legge
dell’equilibrio, imposta dalla natura. Un
pensiero condiviso con Toulouse-Lautrec”
«cuisine is, and not on a secondary level, an
ambassador of Italy and creates a renowned
image of Italianness. It is a key part of the
Italian style together with other like industrial
design, fashion, music and art»
A cordial, elegant, cultured, humble and
humorous man who was able to recount his
dishes with simplicity and irony. He has talked
about his life and has shared memories that
intertwine people, passions and emotions.
“A chef without prejudices, an anarchic who
recognises the natural law of equilibrium in
his dishes. A thought shared with ToulouseLautrec”:
this is how he would like to be defined.
così gli piacerebbe essere definito.
Figura 1 - Gualtiero Marchesi: riso, oro
e zafferano
Figure 1 - Gualtiero Marchesi: rice,
gold and saffron
04/15 | Cultura e Scienza del Colore - Color Culture and Science
57
RUBRICA | COLUMN
Figura 2- Gualtiero Marchesi: il rosso
e il nero
Figura 3 - Lucio Fontana: il rosso e
il nero
58
Cultura e Scienza del Colore - Color Culture and Science | 04/15
RUBRICA | COLUMN
La cerimonia si è conclusa con un’ovazione
generale, tutti i partecipanti alla conferenza si
sono stretti sul palco intorno a Marchesi per
salutarlo, per una foto con lui, per stringergli
la mano: il Maestro calorosamente ha salutato
tutti senza trascurare baci e abbracci per tutte
le signore.
The ceremony closed with a standing ovation,
when all the participants reunited on stage
around Marchesi to greet him, take a photo, or
simply to shake his hand: the Master warmly
saluted everyone without overlooking kisses and
hugs to all the ladies in the room.
04/15 | Cultura e Scienza del Colore - Color Culture and Science
59
RECENSIONI
A cura di
Renata Pompas
RECENSIONI
Jean-Gabriel Causse,
Lo stupefacente potere dei colori
Ponte alle Grazie, Milano, 2015
L’editore Ponte alle Grazie (marchio di Adriano Salani Editore) dopo il successo
ottenuto dalla pubblicazione dei libri sul colore di Pastoureau promuove un altro
libro sul colore e scritto dal francese Jean-Gabriel Causse, color designer membro
del Comité Français de la Couleur di Parigi, che è già stato tradotto in 19 lingue.
Un libro agile, senza illustrazioni, scritto in tono colloquiale rivolgendosi direttamente
al lettore, ricco di aneddoti e di ‘consigli’.
Il titolo stesso indica il focus dello scritto: stupire il lettore avvicinandolo alle
numerose proprietà dei colori di influenzare la nostra vita personale e collettiva,
fatto che nonostante sia ormai scientificamente provato rimane molto sottovalutato.
Nel 1° capitolo Causse, cercando
di semplificare al massimo la
comprensione degli aspetti più ostici,
affronta un ventaglio ricco e complesso
di questioni che riguardano il colore
L’incipit recita con tono confidenziale:
“A costo di darvi una delusione, vi
informo che il colore non esiste!”
e prosegue sostenendo che se le teorie
contemporanee sulla luce intesa come
trasferimento di energia (fascio di fotoni)
si sono affermate da poco è perché
“c’è chi non ha avuto abbastanza
coraggio si mandare in pensione
l’amico Goethe”.
Con esempi accattivanti (perché le
nuvole appaiono bianche, il cielo azzurro
e il sole rosso all’orizzonte) conduce il
lettore a comprendere alcuni aspetti
della fisica, della percezione, della
Temperatura di colore, della sinestesia
e della riproduzione, ma anche della
percezione dei colori nella fauna e nella
flora.
Nel 2° capitolo l’autore dimostra
come i colori influenzino le facoltà e i
comportamenti umani, assorbito dalla
retina ma anche dalla pelle, incidendo
sull’umore, il bioritmo, l’udito, la
60
Cultura e Scienza del Colore - Color Culture and Science | 04/15
RECENSIONI
memoria, l’emotività e altro. Gli esempi variano dalla neurofisiologia all’etologia, dal
suo uso nella pubblicità, ai suoi effetti sul gusto, l’olfatto, il packaging, la vendita,
sulle prestazioni degli sportivi, il rendimento degli scolari, la condotta dei carcerati:
sarebbe stato infatti dimostrato che le celle dipinte di rosa nel Centro correzionale
della Marina americana di Seattle sono state in grado di ridurre l’aggressività dei
detenuti dopo soli quindici minuti di esposizione. Inoltre, un paragrafo è dedicato ai
“Colori sessuali” perché, come recita l’aletta della copertina,
A cura di
Renata Pompas
“Sapevate che la frequenza dei rapporti sessuali è maggiore all’interno di una
stanza a dominante rossa invece che grigia?”.
E sebbene il colore preferito in tutto il mondo sia il Blu e quello che genera unanime
ostilità sia il giallo, Casse suggerisce di ascoltare istintivamente qual è il nostro colore
preferito per trarne benessere: raccomandazione consigliata da un color designer
che progetta cartelle tendenze colore e che ne mostra un’apertura notevole. Un altro
paragrafo è dedicato all’importanza della luce e al suo rapporto con la melatonina,
infine il capitolo si conclude con un accorato richiamo alle proprietà curative della
Cromoterapia.
Nel 3° capitolo si trovano i consigli, suffragati di esempi e riferimenti, sulla scelta dei
colori nella moda e nell’arredamento in relazione alla loro simbologia e all’apporto
del Feng Shui, suddivisi colore per colore: blu, rosso, rosa, verde, nero, grigio, bianco,
viola, giallo, arancione e bruno/marrone/beige e turchese che, come scrive l’autore,
“già cinquemilacinquecento anni prima di Cristo era un colore chic”.
Nelle Conclusioni Causse scrive che
“Il colore è indispensabile alla nostra vita, eppure non è mai stato così poco
presente accanto a noi, sia sui nostri abiti che nelle nostre case, come lo è oggi.
In quale misura un simile grigiore si può ritenere responsabile del basso morale
degli occidentali? È difficile da stabilire. Io tuttavia sono persuaso che i graffiti
colorati nelle periferie grigie siano opera di ragazzi che inconsapevolmente
provano il bisogno viscerale di uscire da questa cupezza (…) come ha detto
l’umorista Pierre Dac: se la materia grigia fosse più rosa, il mondo avrebbe
meno idee nere”.
Nella Appendice Causse confronta le simbologie dei colori nel mondo, colore per
colore, tratte da: Goethe, Kandinsky, Pastoureau, Grossman e Wisenblit, dizionario
teologico di Elwell, École Polytechnique di Montréal, trattati di Feng Shui e di
medicina ayurvedica.
Da ultimo le ventidue pagine dedicate alle Fonti legittimano il tono leggero e
divertente del libro.
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GRUPPO DEL COLORE
ASSOCIAZIONE ITALIANA COLORE
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