A discrete magno-parvo additive model in early vision for explaining brightness perception in varying contrastive contexts

A varying contrastive context filter (VCCF)-based model of brightness perception has been proposed. It is motivated first by a recently proposed difference of difference-of-Gaussian (DDOG) filter. Alongside, it is also inspired from the fact that the nature evolves various discrete systems and mechanisms to carry out many of its complex tasks. A weight factor, used for the linear combination of two filters representing the magnocellular and parvocellular channels in the central visual pathway, has been defined and termed as the factor of contrastive context (FOCC) in the present model. This is a binary variable that lends a property of discretization to the DDOG filter. By analyzing important brightness contrast as well as brightness assimilation illusions, we arrive at the minimal set of values (only two) for FOCC, using which one is able to successfully predict the direction of brightness shift in both situations of brightness contrast, claimed and categorized here as low contrastive context, and those of brightness assimilation, claimed and categorized here as high contrastive context perception, depending upon whether the initial M-channel-filtered stimulus is above or below a threshold of the contrastive context. As distinct from Michelson/Weber/RMS contrast, high or low, the contrastive context claimed is dependent on the edge information in the stimulus determined by the Laplacian operator, also used in the DDOG model. We compared the proposed model against the already well-established oriented difference-of-Gaussian (ODOG) model of brightness perception. Extensive simulations suggest that though for most illusions both ODOG and VCCF produce correct output, for certain intricate cases in which the ODOG filter fails to correctly predict the illusory effect, our proposed VCCF model continues to remain effective.