Perceptual Full-Reference Quality Assessment of Stereoscopic Images by Considering Binocular Visual Characteristics

Perceptual quality assessment is a challenging issue in 3D signal processing research. It is important to study 3D signal directly instead of studying simple extension of the 2D metrics directly to the 3D case as in some previous studies. In this paper, we propose a new perceptual full-reference quality assessment metric of stereoscopic images by considering the binocular visual characteristics. The major technical contribution of this paper is that the binocular perception and combination properties are considered in quality assessment. To be more specific, we first perform left-right consistency checks and compare matching error between the corresponding pixels in binocular disparity calculation, and classify the stereoscopic images into non-corresponding, binocular fusion, and binocular suppression regions. Also, local phase and local amplitude maps are extracted from the original and distorted stereoscopic images as features in quality assessment. Then, each region is evaluated independently by considering its binocular perception property, and all evaluation results are integrated into an overall score. Besides, a binocular just noticeable difference model is used to reflect the visual sensitivity for the binocular fusion and suppression regions. Experimental results show that compared with the relevant existing metrics, the proposed metric can achieve higher consistency with subjective assessment of stereoscopic images.