Toward a Quality Predictor for Stereoscopic Images via Analysis of Human Binocular Visual Perception

Abstract

Perceptual stereoscopic image quality assessment (SIQA) has become a challenge research problem due to the poor understanding of human binocular visual characteristics. For the task of SIQA, an intuitive idea is to develop effective models on the basis of the image content and depth perception. In this paper, we propose a full-reference objective quality evaluator for stereoscopic images by simulating binocular behaviors of the human visual system (HVS): Binocular interaction and depth perception. This model is based on a cyclopean image from a novel binocular combination model as image content quality description and a depth binocular combination model from a depth synthesized procedure as depth perception description. The final quality score of the distorted stereoscopic images is calculated by integrating the above two perception indicators. The experimental results on two stereoscopic image quality databases demonstrate that our proposed metric works efficiently for both symmetric and asymmetric distortions and achieves high consistent alignment with subjective observations.