Psychophysically Tuned Divisive Normalization Approximately
Factorizes the PDF of Natural Images

V. Laparra and J. Malo



Abstract

 

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.


Related Papers


  Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images.
V. Laparra and J. Malo.
Neural Computation,22(12):3179-206. 2010  pdf

Divisive Normalization Image Quality Metric Revisited
V. Laparra, J. Muñoz and J.Malo
Journal of the Optical Society Am. A. 27(4): 852-864 (2010).
Also selected by OSA for the Virtual Journal for Biomedical Optics 5(8), 2010  d mat



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