BLIND STEREOPAIR QUALITY ASSESSMENT USING STATISTICS OF MONOCULAR AND BINOCULAR IMAGE STRUCTURES

In this paper, we present a no-reference (NR) quality predictor for stereoscopic/3D images based on statistics aggregation of monocular and binocular local contrast features. In particular, for left and right views, we first extract statistical features of the image gradient magnitude (GM) and the Laplacian of Gaussian (LoG), describing the image local structures from different perspectives. The monocular statistical features are then combined to derive the binocular features based on a linear summation model using weightings based on LoG-response and image local-entropy, independently. These weights can effectively simulate the strength of the views dominance on binocular rivalry (BR) behavior of the human visual system. Subsequently, we further compute the GM features of the difference map between left and right views reflecting the distortion on disparity/depth information. Finally, the BR-inspired combined monocular and disparityrelated binocular features associated with subjective quality scores are jointly used to construct a learned regression model relying on support vector machine regressor. Experimental results on three 3D-IQA benchmark databases demonstrate that our method achieves high quality prediction accuracy and competitive performance compared to state-of-the-art methods.