SSIM-based binocular perceptual model for quality assessment of stereoscopic images
Jian Ma, Ping An, Liquan Shen, Kai Li, Jialu Yang · 2017
In this paper, we propose a novel full reference stereoscopic image quality assessment (FR-SIQA) metric by utilizing SSIM-based binocular perceptual model. The goal is to predict the perceptual quality of a stereoscopic image via jointly considering the qualities of cyclopean image and the difference image. Specifically, we first apply the contrast sensitivity filtering to both the reference and distorted stereo pairs. Constructively, a new cyclopean image is generated by considering binocular perceptual model and binocular rivalry simultaneously. Finally, the overall quality score of a testing stereoscopic image is predicted by combining the qualities of its cyclopean image and difference image. Experimental results show that the proposed metric achieves high consistency with human subjective assessment and outperforms several the state-of-the-art FR-SIQA methods.