Perceptual video quality assessment: Spatiotemporal pooling strategies for different distortions and visual maps

Abstract

In this paper, we investigate the challenge of distortion map feature selection and spatiotemporal pooling in perceptual video quality assessment (PVQA). We analyze three distortion maps representing different visual features spatially and temporally: squared error, local pixel-level SSIM, and absolute difference of optical flow magnitudes. We examine the performance of each of these maps with different spatial and temporal pooling strategies across three databases. We identify the most effective statistical pooling strategies spatially and temporally with respect to PVQA. We also show the most significant spatial and temporal features correlated with perception for every distortion/feature map. Our results show that varying the pooling strategy and distortion maps yields a significant improvement in perceptual quality estimation. We also deduce insights from our results to better understand the sensitivity of human vision to distortions. We aim for these findings to provide perceptual cues and guidelines to researchers during metric design, perceptual feature selection, HVS modeling and pooling selection/optimization. We further show that the same distortions across databases can yield different results in terms of PVQA evaluation and verification.