A computational texture masking model for natural images based on adjacent visual channel inhibition

Abstract

Masking is a perceptual effect in which contents of the image reduce the ability of the observer to see the target signals hidden in the image. Characterization of masking effects plays an important role in modern image quality assessment (IQA) algorithms. In this work, we attribute the reduced sensitivity to the inhibition imposed by adjacent visual channels. In our model, each visual channel is excited by the contrast difference between the reference and distorted image in the corresponding channel and suppressed by the activities of the mask in adjacent channels. The model parameters are fitted to the results of a psychophysical experiment conducted with a set of different natural texture masks. Cross-validation is performed to demonstrate the model's performance in predicting the target detection threshold. The results of this work could be applied to improve the performance of current HVS-based IQA algorithms.