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