Local Contrast in Natural Images: Normalisation and Coding Efficiency

Abstract

The visual system employs a gain control mechanism in the cortical coding of contrast whereby the response of each cell is normalised by the integrated activity of neighbouring cells. While restricted in space, the normalisation pool is broadly tuned for spatial frequency and orientation, so that a cell's response is adapted by stimuli which fall outside its ‘classical’ receptive field. Various functions have been attributed to divisive gain control: in this paper we consider whether this output nonlinearity serves to increase the information carrying capacity of the neural code. 46 natural scenes were analysed with the use of oriented, frequency-tuned filters whose bandwidths were chosen to match those of mammalian striate cortical cells. The images were logarithmically transformed so that the filters responded to a luminance ratio or contrast. In the first study, the response of each filter was calibrated relative to its response to a grating stimulus, and local image contrast was expressed in terms of the familiar Michelson metric. We found that the distribution of contrasts in natural images is highly kurtotic, peaking at low values and having a long exponential tail. There is considerable variability in local contrast, both within and between images. In the second study we compared the distribution of response activity before and after implementing contrast normalisation, and noted two major changes. Response variability, both within and between scenes, is reduced by normalisation, and the entropy of the response distribution is increased after normalisation, indicating a more efficient transfer of information.