The Wilson-Cowan Model Describes Contrast Response and Subjective Distortion
|Title||The Wilson-Cowan Model Describes Contrast Response and Subjective Distortion|
|Year of Publication||2017|
|Authors||Bertalmío M, Cyriac P, Batard T, Martinez-García M, Malo J|
|Image Source Program||Vision Sciences Society Annual Meeting|
The Wilson-Cowan equations were originally proposed to describe the low-level dynamics of neural populations (Wilson&Cowan 1972). These equations have been extensively used in modelling the oscillations of cortical activity (Cowan et al. 2016). However, due to their low-level nature, very few works have attempted connections to higher level psychophysics (Herzog et al. 2003, Hermens et al. 2005) and, to the best of our knowledge, they have not been used to predict contrast response curves or subjective image quality. Interestingly (Bertalmío&Cowan 2009) showed that Wilson-Cowan models may lead to (high level) color constancy. Moreover, these models may have positive statistical effects similarly to Divisive Normalization, which is the canonical choice to understand contrast response (Watson&Solomon 1997, Carandini&Heeger 2012): while Divisive Normalization reduces redundancy due to predictive coding (Malo&Laparra 2010), Wilson-Cowan leads to local histogram equalization (Bertalmío 2014), another route to
increase channel capacity.
Here we show that the functional (statistical) similarities between Wilson-Cowan and Divisive Normalization actually hold and may be extended to contrast perception. Specifically, first we fitted the Wilson-Cowan model using a procedure reported for Divisive Normalization: following (Watson&Malo 2002, Laparra&Malo 2010), we maximized the correlation with human opinion in quality assessment. Secondly, we used the resulting model to predict the visibility of textured patterns on top of backgrounds of different frequencies and contrasts as in classical masking experiments. Finally, we checked the redundancy reduction of Wilson-Cowan and Divisive Normalization in the same way (as in Malo&Laparra 2010). Results show that (1) Wilson-Cowan is as good as Divisive Normalization in reproducing image distortion psychophysics, (2) Wilson-Cowan dynamics induces saturating responses that attenuate with the contrast of the background, particularly when the background resembles the test; and (3) mutual information between V1-like responses after the Wilson-Cowan interaction decreases similarly as in Divisive Normalization.