Prediction of Chromatic Visual Masking with Deep Learning

Visual masking is a well-studied phenomenon that has been exploited for signal compression, computer graphics and data hiding. Among the different types of visual masking, chromatic masking has received very little attention despite its importance and proven potential for the aforementioned applications. In this paper, we ask whether a deep neural network can learn to predict the detection thresholds in a chromatic masking paradigm. For that, a CNN model was trained and evaluated using a dataset made of 480 image patches for which chromatic thresholds were registered in terms of log-Gabor targets, as well as Root Mean Square (RMS) error. Experimental results show the superiority of the proposed approach.