Relations between the statistics of natural images and the response properties of cortical cells

TLDR

The efficiency of an image‑coding scheme depends on the class of images it encounters, so studying natural image statistics (e.g., trees, rocks, bushes) helps understand how the mammalian visual system represents images, although many image classes are not efficiently coded by such schemes. In this study, various coding schemes are compared in relation to how they represent the information in such natural images. The study uses Gabor‑like transforms that respond to local spatial regions, frequencies, and orientations to represent code coefficients. The results show that mammalian simple cells, tuned to orientation and spatial frequency, efficiently encode natural images by reducing higher‑order redundancy to first‑order redundancy, yielding high signal‑to‑noise ratios and enabling transmission with fewer cells, thereby supporting Barlow’s minimal‑redundancy theory.

Abstract

The relative efficiency of any particular image-coding scheme should be defined only in relation to the class of images that the code is likely to encounter. To understand the representation of images by the mammalian visual system, it might therefore be useful to consider the statistics of images from the natural environment (i.e., images with trees, rocks, bushes, etc). In this study, various coding schemes are compared in relation to how they represent the information in such natural images. The coefficients of such codes are represented by arrays of mechanisms that respond to local regions of space, spatial frequency, and orientation (Gabor-like transforms). For many classes of image, such codes will not be an efficient means of representing information. However, the results obtained with six natural images suggest that the orientation and the spatial-frequency tuning of mammalian simple cells are well suited for coding the information in such images if the goal of the code is to convert higher-order redundancy (e.g., correlation between the intensities of neighboring pixels) into first-order redundancy (i.e., the response distribution of the coefficients). Such coding produces a relatively high signal-to-noise ratio and permits information to be transmitted with only a subset of the total number of cells. These results support Barlow's theory that the goal of natural vision is to represent the information in the natural environment with minimal redundancy.