Reduced Reference Stereoscopic Image Quality Assessment Based on Binocular Perceptual Information

In this paper, we propose a novel reduced reference stereoscopic image quality assessment (RR-SIQA) metric by using binocular perceptual information (BPI). BPI is represented by the distribution statistics of visual primitives in left and right views´ images, which are extracted by sparse coding and representation . Specifically, entropy of the left view´s image and entropy of the right view´s image are used to represent monocular cue. Their mutual information is used to represent binocular cue. Constructively, we represent BPI as three numerical indicators . The difference of the original and distorted images´ BPIs is taken as perceptual loss vector. The perceptual loss vector is used to compute the quality score for a stereoscopic image by a prediction function which is trained using support vector regression (SVR). Experimental results show that the proposed metric achieves significantly higher prediction accuracy than the state-of-the-art reduced reference SIQA methods and better than several state-of-the-art full reference SIQA methods on the LIVE phase II asymmetric databases.