No-reference stereoscopic image quality evaluator with segmented monocular features and perceptual binocular features

Abstract

No-reference stereoscopic image quality assessment (NR SIQA) has aroused much attention in the multimedia application, but most current works cannot achieve reliable accuracy due to the non-visual meaning image patches and the weak biological visual models. Inspired by this, we propose a novel NR SIQA model by the introduction of the meaningful superpixel visual patches and the significant binocular perception models. Following the visual perceptual posterior probability theory, local monocular superpixel spatial entropy and the natural scene statistics (NSS) features are firstly utilized to present the high level semantic perceptual mechanism and image naturalness. Then the binocular biological models are applied to extract the binocular features to simulate the inner binocular interaction information, which well complement with the monocular features. Finally, a support vector regression (SVR) model is utilized to predict the objective quality score. Experiments are conducted on four popular stereoscopic image Databases, and the results demonstrate that the proposed model obtains a good consistency with the subjective scores, and has more reliable performance than previous works in terms of prediction accuracy and generalization capability.