Full-Reference Stereo Image Quality Assessment Using Natural Stereo Scene Statistics

Empirical studies of the joint statistics of luminance and disparity images (or wavelet coefficients) of natural stereoscopic scenes have resulted in two important findings: the marginal statistics are modelled well by the generalized Gaussian distribution (GGD) and there exists significant correlation between them. Inspired by these findings, we propose a full-reference image quality assessment algorithm dubbed STeReoscopic Image Quality Evaluator (STRIQE). We show that the parameters of the GGD fits of luminance wavelet coefficients along with correlation values form excellent features. Importantly, we demonstrate that the use of disparity information (via correlation) results in a consistent improvement in the performance of the algorithm. The performance of our algorithm is evaluated over popular datasets and shown to be competitive with the state-of-the-art full-reference algorithms. The efficacy of the algorithm is further highlighted by its near-linear relation with subjective scores, low root mean squared error (RMSE), and consistently good performance over both symmetric and asymmetric distortions.