Amnon D. Silverstein

Development of an image/threshold database for designing and testing human vision models

Models that predict human performance on narrow classes of visual stimuli abound in the vision science literature. However, the vision and the applied imaging communities need robust general-purpose, rather than narrow, computational human visual system models to evaluate image fidelity and quality and ultimately improve imaging algorithms. Psychophysical measure of image imaging algorithms. Psychophysical measures of image quality are too costly and time consuming to gather to evaluate the impact each algorithm modification might have on image quality.

Modeling Contrast Thresholds

The application of the linear systems approach to the analysis of human spatial vision [1] began in earnest in the 1960’s, with estimates of the human contrast sensitivity function (CSF)[2]. Human visual system (HVS) models fall into two broad categories: single resolution and multi-resolution. Single resolution models typically use a low-pass (or a band-pass) filter as the first stage. These models have the advantage of computational simplicity but ignore much of what we have learned from neurophysiological and psychophysical studies. The CSF is also often used to scale mechanism sensitivity in standard multi-resolution HVS models [3].