The conventional approach in Computational Neuroscience in
favor of the efficient coding hypothesis goes from image statistics to
perception. It has been argued that the behavior of the early stages of
biological visual processing (e.g. spatial frequency analyzers and their
non-linearities) may be obtained from image samples and the efficient coding
hypothesis using no psychophysical or physiological information.
In this work we address the same issue in the opposite
direction, from perception to image statistics: we show that
psychophysically fitted image representation in V1 has appealing statistical
properties, e.g. approximate PDF factorization and substantial mutual
information reduction, even though no statistical information is used to fit
the V1 model. These results are a complementary evidence in favor of the
efficient coding hypothesis.
