Full-Reference 3-D Video Quality Assessment Using Scene Component Statistical Dependencies

Abstract

In this letter, we present a full-reference (FR) quality assessment algorithm to assess the perceptual quality of natural stereoscopic three-dimensional (S3-D) videos. Toward the end, we rely on an empirical model for the joint statistics of motion and depth subband coefficients of an S3-D video frame. Specifically, we use the recently proposed bivariate generalized Gaussian distribution (BGGD) model for the joint statistics. In this letter, we show that the coherence of the covariance matrix of the BGGD varies in proportion with the perceptual video quality. We compute the coherence scores from the eigenvalues of the covariance matrix to estimate the amount of directional dependency between the motion and depth components. To estimate the overall spatial quality score, we apply off-the-shelf 2-D FR image quality assessment metrics on a frame-by-frame basis on both the views and average the frame wise scores. Finally, we pool the coherence and spatial quality scores to derive the overall quality for the S3-D video. An evaluation of the proposed algorithm on the IRCCYN, WaterlooIVC Phase I, and LFOVIA S3D video databases demonstrates its robust performance. The proposed algorithm is called depth- and motion-based 3-D video quality evaluator.