No-Reference Stereoscopic Image Quality Assessment Considering Binocular Disparity and Fusion Compensation

Abstract

In this paper, we propose an optimized dual stream convolutional neural network (CNN) considering binocular disparity and fusion compensation for no-reference stereoscopic image quality assessment (SIQA). Different from previous methods, we extract both disparity and fusion features from multiple levels to simulate hierarchical processing of the stereoscopic images in human brain. Given that the ocular dominance plays an important role in quality evaluation, the fusion weights assignment module (FWAM) is proposed to assign weight to guide the fusion of the left and the right features respectively. Experimental results on four public stereoscopic image databases show that the proposed method is superior to the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.