Abstract

Many aspects of visual coding have been successfully predicted by starting from the statistics of natural scenes and then asking how the stimulus could be efficiently represented. We started from the representation of color characterized by uniform color spaces, and then asked what type of color environment they implied. These spaces are designed to represent equal perceptual differences in color discrimination or appearance by equal distances in the space. The relative sensitivity to different axes within the space might therefore reflect the gamut of colors in natural scenes. To examine this, we projected perceptually uniform distributions within the Munsell, CIE L*u*v* or CIE L*a*b* spaces into cone-opponent space. All were elongated along a bluish-yellowish axis reflecting covarying signals along the LM and S(L+M) cardinal axes, a pattern typical (though not identical) to many natural environments. In turn, color distributions from environments were more uniform when projected into the CIE L*a*b* perceptual space than when represented in a normalized cone-opponent space. These analyses suggest the bluish-yellowish bias in environmental colors might be an important factor shaping chromatic sensitivity, and also suggest that perceptually uniform color metrics could be derived from natural scene statistics and potentially tailored to specific environments.

© 2012 Optical Society of America

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    [CrossRef]

2011 (3)

G. Tkacik, P. Garrigan, C. Ratliff, G. Milcinski, J. M. Klein, L. H. Seyfarth, P. Sterling, D. H. Brainard, and V. Balasubramanian, “Natural images from the birthplace of the human eye,” PLoS One 6, e20409 (2011).
[CrossRef]

C. McCollough-Howard and M. A. Webster, “McCollough effect,” Scholarpedia 6, 8175 (2011).
[CrossRef]

M. A. Webster, “Adaptation and visual coding,” J. Vision 11(5), 3 (2011).
[CrossRef]

2010 (4)

E. Goddard, D. J. Mannion, J. S. McDonald, S. G. Solomon, and C. W. G. Clifford, “Combination of subcortical color channels in human visual cortex,” J. Vision 10(5), 25 (2010).

M. V. Danilova and J. D. Mollon, “Parafoveal color discrimination: a chromaticity locus of enhanced discrimination,” J. Vision 10(1), 4 (2010).
[CrossRef]

K. C. McDermott, G. Malkoc, J. B. Mulligan, and M. A. Webster, “Adaptation and visual salience,” J. Vision 10(13), 17 (2010).
[CrossRef]

I. Juricevic, L. Land, A. Wilkins, and M. A. Webster, “Visual discomfort and natural image statistics,” Perception 39, 884–899 (2010).
[CrossRef]

2009 (2)

I. Juricevic and M. A. Webster, “Variations in normal color vision. Simulations V. of adaptation to natural color environments,” Vis. Neurosci. 26, 133–145 (2009).
[CrossRef]

R. Baddeley, and D. Attewell, “The relationship between language and the environment: information theory shows why we have only three lightness terms,” Psychol. Sci. 20, 1100–1107 (2009).

2008 (4)

W. S. Geisler, “Visual perception and the statistical properties of natural scenes,” Annu. Rev. Psychol. 59, 167–192 (2008).
[CrossRef]

S. K. Shevell and F. A. Kingdom, “Color in complex scenes,” Annu. Rev. Psychol. 59, 143–166 (2008).
[CrossRef]

D. Fernandez and A. J. Wilkins, “Uncomfortable images in art and nature,” Perception 37, 1098–1113 (2008).
[CrossRef]

M. A. Webster and D. Leonard, “Adaptation and perceptual norms in color vision,” J. Opt. Soc. Am. A 25, 2817–2825 (2008).
[CrossRef]

2007 (2)

M. A. Webster, Y. Mizokami, and S. M. Webster, “Seasonal variations in the color statistics of natural images,” Network Comp. Neural Syst. 18, 213–233 (2007).
[CrossRef]

T. Wachtler, E. Doi, T. Lee, and T. J. Sejnowski, “Cone selectivity derived from the responses of the retinal cone mosaic to natural scenes,” J. Vision 7(8), 6 (2007).
[CrossRef]

2006 (3)

F. Long, Z. Yang, and D. Purves, “Spectral statistics in natural scenes predict hue, saturation, and brightness,” Proc. Natl. Acad. Sci. USA 103, 6013–6018 (2006).
[CrossRef]

J. D. Mollon, “Monge (The Verriest Lecture),” Vis. Neurosci. 23, 297–309 (2006).

M. A. Aldaba, J. M. Linhares, P. D. Pinto, S. M. Nascimento, K. Amano, and D. H. Foster, “Visual sensitivity to color errors in images of natural scenes,” Vis. Neurosci. 23, 555–559(2006).

2003 (1)

F. Long and D. Purves, “Natural scene statistics as the universal basis of color context effects,” Proc. Natl. Acad. Sci. USA 100, 15190–15193 (2003).
[CrossRef]

2002 (3)

C. A. Parraga, T. Troscianko, and D. J. Tolhurst, “Spatiochromatic properties of natural images and human vision,” Curr. Biol. 12, 483–487 (2002).
[CrossRef]

R. B. Lotto and D. Purves, “The empirical basis of color perception,” Conscious. Cogn. 11, 609–629 (2002).
[CrossRef]

S. M. Nascimento, F. P. Ferreira, and D. H. Foster, “Statistics of spatial cone-excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484–1490 (2002).
[CrossRef]

2001 (4)

T. Wachtler, T. W. Lee, and T. J. Sejnowski, “Chromatic structure of natural scenes,” J. Opt. Soc. Am. A 18, 65–77 (2001).
[CrossRef]

E. P. Simoncelli and B. A. Olshausen, “Natural image statistics and neural representation,” Annu. Rev. Neurosci. 24, 1193–1216 (2001).
[CrossRef]

S. N. Yendrikhovskij, “Computing color categories from the statistics of natural images,” J. Imaging Sci. Technol. 45, 409–417(2001).

B. C. Regan, C. Julliot, B. Simmen, F. Vienot, P. Charles-Dominique, and J. D. Mollon, “Fruits, foliage and the evolution of primate colour vision,” Philos. Trans. R. Soc. Lond. B 356, 229–283 (2001).
[CrossRef]

2000 (1)

1998 (2)

1997 (1)

M. A. Webster and J. D. Mollon, “Adaptation and the color statistics of natural images,” Vis. Res. 37, 3283–3298 (1997).
[CrossRef]

1996 (2)

D. Osorio and M. Vorobyev, “Colour vision as an adaptation to frugivory in primates,” Proc. R. Soc. Lond. Ser. B 263, 593–599 (1996).
[CrossRef]

X. Zhang and B. A. Wandell, “A spatial extension of CIELAB for digital color image reproduction,” J. Soc. Inf. Disp. (1996).

1995 (1)

B. W. Kolpatzik and C. A. Bouman, “Optimized universal color palette design for error diffusion,” J. Electron. Imaging 4, 131–143 (1995).
[CrossRef]

1994 (1)

M. A. Webster and J. D. Mollon, “The influence of contrast adaptation on color appearance,” Vis. Res. 34, 1993–2020 (1994).
[CrossRef]

1993 (6)

Q. Zaidi and A. G. Shapiro, “Adaptive orthogonalization of opponent-color signals,” Biol. Cybernet. 69, 415–428 (1993).
[CrossRef]

A. Chaparro, C. F. Stromeyer, E. P. Huang, R. E. Kronauer, and R. T. Eskew, “Colour is what the eye sees best,” Nature 361, 348–350 (1993).
[CrossRef]

J. J. Atick, Z. Li, and A. N. Redlich, “What does post-adaptation color appearance reveal about cortical color representation?” Vis. Res. 33, 123–129 (1993).
[CrossRef]

J. H. van Hateren, “Spatial, temporal and spectral pre-processing for colour vision,” Proc. R. Soc. Lond. Ser. B 251, 61–68 (1993).
[CrossRef]

G. R. Cole, T. Hine, and W. McIlhagga, “Detection mechanisms in L-, M-, and S-cone contrast space,” J. Opt. Soc. Am. A 10, 38–51 (1993).
[CrossRef]

J. S. Werner and B. E. Schefrin, “Loci of achromatic points throughout the life span,” J. Opt. Soc. Am. A 10, 1509–1516 (1993).
[CrossRef]

1992 (2)

J. Krauskopf and K. Gegenfurtner, “Color discrimination and adaptation,” Vis. Res. 32, 2165–2175 (1992).
[CrossRef]

J. J. Atick, Z. Li, and A. N. Redlich, “Understanding retinal color coding from first principles,” Neural Comput. 4, 559–572 (1992).
[CrossRef]

1990 (2)

J. J. Atick, “Could information-theory provide an ecological theory of sensory processing,” Network Comp. Neural Syst. 3, 213–251 (1990).

A. L. Nagy and R. R. Sanchez, “Critical color differences determined with a visual search task,” J. Opt. Soc. Am. A 7, 1209–1217 (1990).
[CrossRef]

1988 (1)

1986 (1)

R. M. Boynton, A. L. Nagy, and R. T. Eskew, “Similarity of normalized discrimination ellipses in the constant-luminance chromaticity plane,” Perception 15, 755–763 (1986).
[CrossRef]

1985 (1)

R. S. Berns and F. W. J. Billmeyer, “Development of the 1929 Munsell Book of Color: A historical review,” Color Res. Appl. 10, 246–250 (1985).
[CrossRef]

1984 (1)

A. M. Derrington, J. Krauskopf, and P. Lennie, “Chromatic mechanisms in lateral geniculate nucleus of macaque,” J. Physiol. 357, 241–265 (1984).

1983 (1)

G. Buchsbaum, and A. Gottschalk, “Trichromacy, opponent colours coding and optimum colour information transmission in the retina,” Proc. R. Soc. Lond. Ser. B 220, 89–113 (1983).
[CrossRef]

1982 (1)

J. Krauskopf, D. R. Williams, and D. W. Heeley, “Cardinal directions of color space,” Vis. Res. 22, 1123–1131 (1982).
[CrossRef]

1981 (3)

A. Hard and L. Sivik, “NCS-Natural Color System: A Swedish standard for color notation,” Color Res. Appl. 6, 129–138 (1981).
[CrossRef]

M. R. Pointer, “A comparison of the CIE 1976 colour spaces,” Color Res. Appl. 6, 108–118 (1981).
[CrossRef]

S. Laughlin, “A simple coding procedure enhances a neuron’s information capacity,” Z. Naturforsch. C 36, 910–912 (1981).

1980 (1)

R. M. Boynton and N. Kambe, “Chromatic difference steps of moderate size measured along theoretically critical axes,” Color Res. Appl. 5, 13–23 (1980).
[CrossRef]

1979 (1)

1977 (1)

A. R. Roberston, “The CIE 1976 color-difference fomulae,” Color Res. Appl. 2, 7–11 (1977).

1949 (1)

1942 (1)

Aldaba, M. A.

M. A. Aldaba, J. M. Linhares, P. D. Pinto, S. M. Nascimento, K. Amano, and D. H. Foster, “Visual sensitivity to color errors in images of natural scenes,” Vis. Neurosci. 23, 555–559(2006).

Amano, K.

M. A. Aldaba, J. M. Linhares, P. D. Pinto, S. M. Nascimento, K. Amano, and D. H. Foster, “Visual sensitivity to color errors in images of natural scenes,” Vis. Neurosci. 23, 555–559(2006).

Atick, J. J.

J. J. Atick, Z. Li, and A. N. Redlich, “What does post-adaptation color appearance reveal about cortical color representation?” Vis. Res. 33, 123–129 (1993).
[CrossRef]

J. J. Atick, Z. Li, and A. N. Redlich, “Understanding retinal color coding from first principles,” Neural Comput. 4, 559–572 (1992).
[CrossRef]

J. J. Atick, “Could information-theory provide an ecological theory of sensory processing,” Network Comp. Neural Syst. 3, 213–251 (1990).

Attewell, D.

R. Baddeley, and D. Attewell, “The relationship between language and the environment: information theory shows why we have only three lightness terms,” Psychol. Sci. 20, 1100–1107 (2009).

Baddeley, R.

R. Baddeley, and D. Attewell, “The relationship between language and the environment: information theory shows why we have only three lightness terms,” Psychol. Sci. 20, 1100–1107 (2009).

Balasubramanian, V.

G. Tkacik, P. Garrigan, C. Ratliff, G. Milcinski, J. M. Klein, L. H. Seyfarth, P. Sterling, D. H. Brainard, and V. Balasubramanian, “Natural images from the birthplace of the human eye,” PLoS One 6, e20409 (2011).
[CrossRef]

Barlow, H. B.

H. B. Barlow, “A theory about the functional role and synaptic mechanism of visual aftereffects,” in Visual Coding and Efficiency, C. Blakemore, ed. (Cambridge University Press, 1990), pp. 363–375.

Berns, R. S.

R. S. Berns and F. W. J. Billmeyer, “Development of the 1929 Munsell Book of Color: A historical review,” Color Res. Appl. 10, 246–250 (1985).
[CrossRef]

Billmeyer, F. W. J.

R. S. Berns and F. W. J. Billmeyer, “Development of the 1929 Munsell Book of Color: A historical review,” Color Res. Appl. 10, 246–250 (1985).
[CrossRef]

Bouman, C. A.

B. W. Kolpatzik and C. A. Bouman, “Optimized universal color palette design for error diffusion,” J. Electron. Imaging 4, 131–143 (1995).
[CrossRef]

Boynton, R. M.

R. M. Boynton, A. L. Nagy, and R. T. Eskew, “Similarity of normalized discrimination ellipses in the constant-luminance chromaticity plane,” Perception 15, 755–763 (1986).
[CrossRef]

R. M. Boynton and N. Kambe, “Chromatic difference steps of moderate size measured along theoretically critical axes,” Color Res. Appl. 5, 13–23 (1980).
[CrossRef]

D. I. MacLeod and R. M. Boynton, “Chromaticity diagram showing cone excitation by stimuli of equal luminance,” J. Opt. Soc. Am. 69, 1183–1186 (1979).
[CrossRef]

Bradley, A.

Brainard, D. H.

G. Tkacik, P. Garrigan, C. Ratliff, G. Milcinski, J. M. Klein, L. H. Seyfarth, P. Sterling, D. H. Brainard, and V. Balasubramanian, “Natural images from the birthplace of the human eye,” PLoS One 6, e20409 (2011).
[CrossRef]

D. H. Brainard, “Color appearance and color difference specification,” in The Science of Color, 2nd ed., S. K. Shevell, ed. (Optical Society of America, 2003), pp. 191–216.

Brown, W. R. J.

Buchsbaum, G.

G. Buchsbaum, and A. Gottschalk, “Trichromacy, opponent colours coding and optimum colour information transmission in the retina,” Proc. R. Soc. Lond. Ser. B 220, 89–113 (1983).
[CrossRef]

Cavonius, C. R.

C. R. Cavonius and J. D. Mollon, “Reaction time as a measure of the discriminability of large colour differences,” in Colour Coded vs Monochrome Electronic Displays, C. P. Gibson, ed. (HMSO, 1984).

Chaparro, A.

A. Chaparro, C. F. Stromeyer, E. P. Huang, R. E. Kronauer, and R. T. Eskew, “Colour is what the eye sees best,” Nature 361, 348–350 (1993).
[CrossRef]

Charles-Dominique, P.

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M. A. Aldaba, J. M. Linhares, P. D. Pinto, S. M. Nascimento, K. Amano, and D. H. Foster, “Visual sensitivity to color errors in images of natural scenes,” Vis. Neurosci. 23, 555–559(2006).

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G. Tkacik, P. Garrigan, C. Ratliff, G. Milcinski, J. M. Klein, L. H. Seyfarth, P. Sterling, D. H. Brainard, and V. Balasubramanian, “Natural images from the birthplace of the human eye,” PLoS One 6, e20409 (2011).
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W. S. Geisler, “Visual perception and the statistical properties of natural scenes,” Annu. Rev. Psychol. 59, 167–192 (2008).
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J. Imaging Sci. Technol. (1)

S. N. Yendrikhovskij, “Computing color categories from the statistics of natural images,” J. Imaging Sci. Technol. 45, 409–417(2001).

J. Opt. Soc. Am. (3)

J. Opt. Soc. Am. A (10)

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M. A. Webster, “Adaptation and visual coding,” J. Vision 11(5), 3 (2011).
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Figures (2)

Fig. 1.
Fig. 1.

Spherical distributions in the Munsell (left), CIE L*u*v* (middle) or CIE L*a*b* (right) spaces projected into the cone opponent space. Each row plots the distributions along different pairs of the cardinal axes; top: the S versus LM isoluminant plane; middle: luminance and LM plane; bottom: luminance versus S plane. Lines show the axes of the first (solid) or second (dashed) principal components of the distributions in each of the projected planes.

Fig. 2.
Fig. 2.

Natural color distributions measured from two outdoor locations (top: Sierra Nevadas, USA; bottom: Western Ghats, India) and during two seasons (lush and arid) [31]. Plots show the pooled distributions of the chromaticities from individual scenes sampled in each environment. Lines show the axes of the first (solid) or second (dashed) principal components of the distributions in each of the projected planes.

Tables (2)

Tables Icon

Table 1. Principal Axes and Cone-Opponent Contrasts of Spherical Distributions in the Uniform Color Spaces

Tables Icon

Table 2. Ratio of Variance in Signals along the Principal and Orthogonal Chromatic Axes of the Natural Distributions from the Four Environments, where Values Closer to 1 Represent More Circular or Unbiased Distributions

Equations (1)

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LM=1953*(rmb0.6568),S(L+M)=5533*(bmb0.01825),

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