Information Flow in Biological Networks for Color Vision

Abstract

Color Appearance Models are biological networks that consist of a cascade of linear+nonlinear layers that modify the linear measurements at the retinal photo-receptors leading to an internal (nonlinear) representation of color that correlates with psychophysical experience. The basic layers of these networks include: (1) chromatic adaptation (normalization of the mean and covariance of the color manifold), (2) change to opponent color channels (PCA-like rotation in the color space), and (3) saturating nonlinearities to get perceptually Euclidean color representations (similar to dimensionwise equalization). The Efficient Coding Hypothesis argues that these transforms should emerge from information-theoretic goals. In case this hypothesis holds in color vision, the question is, what is the coding gain due to the different layers of the color appearance networks? In this work, a representative family of Color Appearance Models is analyzed in terms of how the redundancy among the chromatic components is modified along the network and how much information is transferred from the input data to the noisy response. The proposed analysis is done using data and methods that were not available before: (1) new colorimetrically calibrated scenes in different CIE illuminations for proper evaluation of chromatic adaptation, and (2) new statistical tools to estimate (multivariate) information-theoretic quantities between multidimensional sets based on Gaussianization. Results confirm that the Efficient Coding Hypothesis holds for current color vision models, and identify the psychophysical mechanisms critically responsible for gains in information transference: opponent channels and their nonlinear nature are more important than chromatic adaptation at the retina.