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Color Appearance Models are biological networks that consist of a cascade oflinear+nonlinear layers that modify the linear measurements at the retinalphoto-receptors leading to an internal (nonlinear) representation of color thatcorrelates with psychophysical experience. The basic layers of these networksinclude: (1) chromatic adaptation (normalization of the mean and covariance ofthe color manifold), (2) change to opponent color channels (PCA-like rotationin the color space), and (3) saturating nonlinearities to get perceptuallyEuclidean color representations (similar to dimensionwise equalization). TheEfficient Coding Hypothesis argues that these transforms should emerge frominformation-theoretic goals. In case this hypothesis holds in color vision, thequestion is, what is the coding gain due to the different layers of the colorappearance networks?  In this work, a representative family of Color Appearance Models is analyzedin terms of how the redundancy among the chromatic components is modified alongthe network and how much information is transferred from the input data to thenoisy response. The proposed analysis is done using data and methods that werenot available before: (1) new colorimetrically calibrated scenes in differentCIE illuminations for proper evaluation of chromatic adaptation, and (2) newstatistical tools to estimate (multivariate) information-theoretic quantitiesbetween multidimensional sets based on Gaussianization. Results confirm thatthe Efficient Coding Hypothesis holds for current color vision models, andidentify the psychophysical mechanisms critically responsible for gains ininformation transference: opponent channels and their nonlinear nature are moreimportant than chromatic adaptation at the retina.

Information Flow in Biological Networks for Color Vision