Masking in chrominance channels of natural images — Data, analysis, and prediction

Abstract

This paper addresses the visual masking that occurs in the chrominance channels of natural images. We present results from a psychophysical experiment designed to obtain local thresholds of just noticeable log-Gabor distortion in the Cr and Cb channels of natural images. We analyzed the data and investigated the correlation between several low-level image features and the collected thresholds. As expected, features like variance, entropy, or edge density were correlated relatively high with the thresholds. We evaluated the performance of linear and non-linear regression (using neural networks and support vector machines) for thresholds prediction from multiple global image features; we also fitted a modified Watson-Solomon's computational model (based on log-Gabor features) for thresholds prediction. The evaluation showed that neural networks and support vector machines are most suitable for thresholds prediction. The computational model performed reasonably well, with further prospects of its improvement.