No-Reference Quality Assessment of Stereoscopic Images Based on Binocular Combination of Local Features Statistics

Abstract

No-reference (NR) stereoscopic 3D (S3D) image quality assessment (SIQA) is still challenging due to the poor understanding of how the human visual system (HVS) judges image quality based on binocular vision. In this paper, we propose an efficient opinion-aware NR Stereoscopic Quality predictor based on local contrast statistics combination (SQSC). Specifically, for left and right views, we first extract statistical features of the gradient magnitude (GM) and Laplacian of Gaussian (LoG) responses, describing the image local structures from different perspectives. The HVS is insensitive to low-order statistical redundancies that can be removed by LoG filtering. Hence, the monocular statistical features are then fused to derive the binocular features based on a linear combination model using LoG responses-based weightings. These weightings can efficiently simulate the binocular rivalry (BR) phenomenon. Finally, the binocular features and the subjective scores were jointly employed to construct a learned regression model obtained by the support vector regression (SVR) algorithm. Experimental results on three widely used 3D IQA databases demonstrate the high prediction performance of the proposed method when compared to recent well performing SIQA methods.