A simple principled approach for modeling and understanding uniform color metrics.

An important goal in characterizing human color vision is to order color percepts in a way that captures their similarities and differences. This has resulted in the continuing evolution of "uniform color spaces," in which the distances within the space represent the perceptual differences between the stimuli. While these metrics are now very successful in predicting how color percepts are scaled, they do so in largely empirical, ad hoc ways, with limited reference to actual mechanisms of color vision. In this article our aim is to instead begin with general and plausible assumptions about color coding, and then develop a model of color appearance that explicitly incorporates them. We show that many of the features of empirically defined color order systems (those of Munsell, Pantone, NCS, and others) as well as many of the basic phenomena of color perception, emerge naturally from fairly simple principles of color information encoding in the visual system and how it can be optimized for the spectral characteristics of the environment.

[1]  Journal of the Optical Society of America , 1950, Nature.

[2]  P. Sterling,et al.  How Much the Eye Tells the Brain , 2006, Current Biology.

[3]  Frederick A.A. Kingdom,et al.  Lightness, brightness and transparency: A quarter century of new ideas, captivating demonstrations and unrelenting controversy , 2011, Vision Research.

[4]  Zhaoping Li,et al.  What does post-adaptation color appearance reveal about cortical color representation? , 1993, Vision Research.

[5]  Barry B. Lee,et al.  The 'blue-on' opponent pathway in primate retina originates from a distinct bistratified ganglion cell type , 1994, Nature.

[6]  A. Hendrickson,et al.  Human photoreceptor topography , 1990, The Journal of comparative neurology.

[7]  Rhea T. Eskew,et al.  Higher order color mechanisms: A critical review , 2009, Vision Research.

[8]  D. Foster,et al.  Relational colour constancy from invariant cone-excitation ratios , 1994, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[9]  G. Buchsbaum,et al.  Trichromacy, opponent colours coding and optimum colour information transmission in the retina , 1983, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[10]  Michael A Webster,et al.  Uniform color spaces and natural image statistics. , 2012, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  D. Tranchina,et al.  Light adaptation in the turtle retina: embedding a parametric family of linear models in a single nonlinear model , 1988, Visual Neuroscience.

[12]  A. Fairhall,et al.  Timescales of Inference in Visual Adaptation , 2009, Neuron.

[13]  Rolf G. Kuehni,et al.  Color ordered : a survey of color order systems from antiquity to the present , 2008 .

[14]  K. Naka,et al.  S‐potentials from colour units in the retina of fish (Cyprinidae) , 1966, The Journal of physiology.

[15]  Mark S. Cembrowski,et al.  A Synaptic Mechanism for Retinal Adaptation to Luminance and Contrast , 2011, The Journal of Neuroscience.

[16]  Mahdi Nezamabadi,et al.  Color Appearance Models , 2014, J. Electronic Imaging.

[17]  Fred Rieke,et al.  Review the Challenges Natural Images Pose for Visual Adaptation , 2022 .

[18]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[19]  G. Horwitz,et al.  Nonlinear analysis of macaque V1 color tuning reveals cardinal directions for cortical color processing , 2012, Nature Neuroscience.

[20]  M. Webster,et al.  The influence of contrast adaptation on color appearance , 1994, Vision Research.

[21]  Yoko Mizokami,et al.  Seasonal variations in the color statistics of natural images , 2007, Network.

[22]  D. Macleod,et al.  Optimal nonlinear codes for the perception of natural colours , 2001, Network.

[23]  H. Akaike A new look at the statistical model identification , 1974 .

[24]  Angela M. Brown,et al.  Higher order color mechanisms , 1986, Vision Research.

[25]  On Mach’s Contributions to the Analysis of Sensations , 1970 .

[26]  Fuhui Long,et al.  Spectral statistics in natural scenes predict hue, saturation, and brightness. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[27]  A. Romney,et al.  A quantitative model for transforming reflectance spectra into the Munsell color space using cone sensitivity functions and opponent process weights , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[28]  J. Mollon "Tho' she kneel'd in that place where they grew..." The uses and origins of primate colour vision. , 1989, The Journal of experimental biology.

[29]  P. Walraven Fundamental chromaticity diagram with physiological axes , 1999 .

[30]  D. Ruderman,et al.  Statistics of cone responses to natural images: implications for visual coding , 1998 .

[31]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[32]  David Williams,et al.  Color Perception Is Mediated by a Plastic Neural Mechanism that Is Adjustable in Adults , 2002, Neuron.

[33]  Paul Centore,et al.  An open‐source inversion algorithm for the Munsell renotation , 2012 .

[34]  E. Schrödinger Grundlinien einer Theorie der Farbenmetrik im Tagessehen , 1920 .

[35]  M. Luo,et al.  Uniform colour spaces based on CIECAM02 colour appearance model , 2006 .

[36]  M. Meister,et al.  Fast and Slow Contrast Adaptation in Retinal Circuitry , 2002, Neuron.

[37]  J. Jonas,et al.  Human optic nerve fiber count and optic disc size. , 1992, Investigative ophthalmology & visual science.

[38]  Joseph J Atick,et al.  Could information theory provide an ecological theory of sensory processing? , 2011, Network.

[39]  Minchen Wei,et al.  Development of the IES method for evaluating the color rendition of light sources. , 2015, Optics express.

[40]  Li Zhaoping,et al.  Understanding Vision: Theory, Models, and Data , 2014 .

[41]  M. Webster Visual Adaptation. , 2015, Annual review of vision science.

[42]  S. Laughlin,et al.  Predictive coding: a fresh view of inhibition in the retina , 1982, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[43]  S. Laughlin A Simple Coding Procedure Enhances a Neuron's Information Capacity , 1981, Zeitschrift fur Naturforschung. Section C, Biosciences.

[44]  R. M. Boynton,et al.  Chromaticity diagram showing cone excitation by stimuli of equal luminance. , 1979, Journal of the Optical Society of America.

[45]  Barry B. Lee,et al.  Color coding in the primate visual pathway: a historical view. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[46]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .