No-Reference Stereoscopic Video Quality Assessment Algorithm Using Joint Motion and Depth Statistics

Abstract

We propose a supervised no-reference (NR) quality assessment algorithm for assessing the perceptual quality of natural stereoscopic (S3D) videos. We empirically model the joint statistics of motion and depth subband coefficients of an S3D video frame using a Bivaraite Generalized Gaussian Distribution (BGGD). We compute the BGGD model parameters (α, β) to estimate the statistical dependency strength and show the features are quality discriminative. We compute the popular 2D NR image quality assessment (IQA) model NIQE on a frame-by-frame basis for both views to estimate the spatial quality. The frame-level BGGD features and spatial features are consolidated and used with the corresponding S3D videos difference mean opinion score (DMOS) labels for supervised learning using support vector regression (SVR). The overall quality of an S3D video is computed by averaging the frame-level quality predictions of the constituent video frames. The proposed algorithm, dubbed Video QUality Evaluation using MOtion and DEpth Statistics (VQUEMODES) is shown to outperform the state-of-the-art methods when evaluated over the IRCCYN and LFOVIA S3D subjective quality assessment databases.