Post on 13-Jan-2015
description
CGIV 2012
Why we don’t know
how many colors there are
Ján Morovič, Vien Cheung* & Peter Morovič Hewlett-Packard Company
*University of Leeds
Presented by Dr. Vien Cheung at CGIV ‘12, Amseterdam on 7th May 2012
How many colors are there?
3 infinity
16.8 million 28 × 28 × 28 = ~
How many colors are there?
o usefulness in engineering decision processes
o interesting!
o but ... what is color? and what does ‘all possible colors’ mean?
What does ‘all possible colors’ mean?
16 million
CIE system
| 2-10 million
| visual system
physical colors | perceptual colors
Color illusions
o the notion of color is essentially a property of an object does not explain color illusions
Color ‘illusions’
[255 0 0]
physical colors > perceptual colors
physical colors < colors depend upon context
Related studies
2010
1980
1999
2001
2004 2005
2008
1920 o all possible surface colors Schrodinger
Maric-France & Foster
McCann
o color spaces | gamut computations
o viewing condition Morovič et al.
o natural surface
Pointer
Inui et al.
o illumination | adapted white
Heckaman et al.
o natural scenes
Linhares et al.
Our work
o computationally predicting all possible colors
o counting all possible colors ‘by hand’
o discuss the limitations of gamut computation and appearance prediction
Counting colors ‘by hand’
o this exercise can tell us how many colors there are on a gray background, when viewed under a certain light source, etc.!
Counting colors ‘by hand’
Computational prediction
o CIECAM02
o an ecosystem enabling varying color experiences
o color appearance attributes effect on predicting gamut
o explore the effect of various model parameters
Computational prediction
Computational prediction
Light source D50 F11
Surround average dim dark average
Background 20% 20% 20% 20%
Luminance of adapting field
~60 cd/m2 ~60 cd/m2 ~60 cd/m2 ~60 cd/m2
Gamut volume 3.8 MJab 3.5 MJab 3.0 MJab 4.2 MJab
o D50 + F11 = 4.4 MJab
o D50 + F11 + A (3.5 MJab) = 4.5 MJab
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
CIE x
CIE
y
0.2
0.4
0.60.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
5
6
7
8
9
10
11lo
g10
Jab
gam
ut v
olum
e
CIE y
CIE x
Computational prediction
o expanding to standard iluminants to freely varying their SPD
o 242 synthetic light sources
Computational prediction
o CIECAM02 dramatically predicts color gamut with 1011 volumes in Jab space
o i.e. 100,000 times of all possible surface colors under D50
o however, this increment does not agree with experience and is a psychophysical data-based model
o the difficulty of viewing all possible visual ecosystems remains
Computational prediction
o a revised prediction uses 173 measured light sources
400 450 500 550 600 650 7000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
wavelength (nm)
rela
tive
spec
tral p
ower
0.1 0.2 0.3 0.4 0.5 0.6 0.70
0.1
0.2
0.3
0.4
0.5
CIE x
CIE
y
0.2
0.4
0.60.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
5
6
7
8
9
10
11
log1
0 Ja
b ga
mut
vol
ume
CIE y
CIE x
o a total gamut volume of 6.6 MJab
o i.e. the surface, which under D50 (3.8 MJab), result in ~2× that range of colors viewed under a variety of light sources
Computational prediction
−200 −150 −100 −50 0 50 100 150 200−200
−150
−100
−50
0
50
100
150
200
CIE a*
CIE
b*
−200 −150 −100 −50 0 50 100 150 200−200
−150
−100
−50
0
50
100
150
200
CIE a*
CIE
b*
−200−150
−100−50
050
100150
200
−200−150
−100−50
050
100150
2000
20
40
60
80
100
120
CIE a*
CIE b*
CIE
L*
−200−150
−100−50
050
100150
200
−200−150
−100−50
050
100150
2000
20
40
60
80
100
120
CIE a*
CIE b*
CIE
L*
D50 | 173 measured light sources
Computational prediction
CAM
o Note that CIECAM02 does not include many complexities of colour vision such as contrast effects
o using CAM to indicate all possible colors should consider the color gamuts of colour appearance they are derived from
o CIECAM02 (LUTCHI data) – 1.7 MJab
Conclusions
o based upon the available data to-date there are at least ~1.7 million colors
o to go beyond this type of number would require:
o a color appearance model closely mimics the human visual system o extend psychophysical basis