Using Saliency Weighted Disparity Statistics for Objective Visual Comfort Assessment of Stereoscopic Images
Visual comfort assessment (VCA) for stereoscopic images is a particularly significant yet challenging task in 3D quality of experience research field. Although the subjective assessment given by human observers is known as the most reliable way to evaluate the experienced visual discomfort, it is time consuming and non systematic. Therefore, it is of great importance to develop objective VCA approaches that can faithfully predict the degree of visual discomfort as human beings do. In this paper, a novel two stage objective VCA framework is proposed. The main contribution of this study is that the important visual attention mechanism of human visual system is incorporated for visual comfort aware feature extraction. Specifically, in the first stage, we first construct an adaptive 3D visual saliency detection model to derive saliency map of a stereoscopic image, and then a set of saliency weighted disparity statistics are computed and combined to form a single feature vector to represent a stereoscopic image in terms of visual comfort. In the second stage, a high dimensional feature vector is fused into a single visual comfort score by performing random forest algorithm. Experimental results on two benchmark databases confirm the superior performance of the proposed approach.