No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics.

Robust spatiotemporal representations of natural videos have several applications including quality assessment, action recognition, object tracking etc. In this paper, we propose a video representation that is based on a parameterized statistical model for the spatiotemporal statistics of mean subtracted and contrast normalized (MSCN) coefficients of natural videos. Specifically, we propose an asymmetric generalized Gaussian distribution (AGGD) to model the statistics of MSCN coefficients of natural videos and their spatiotemporal Gabor bandpass filtered outputs. We then demonstrate that the AGGD model parameters serve as good representative features for distortion discrimination. Based on this observation, we propose a supervised learning approach using support vector regression (SVR) to address the no-reference video quality assessment (NRVQA) problem. The performance of the proposed algorithm is evaluated on publicly available video quality assessment (VQA) datasets with both traditional and in-capture/authentic distortions. We show that the proposed algorithm delivers competitive performance on traditional (synthetic) distortions and acceptable performance on authentic distortions. The code for our algorithm will be released at https://www.iith.ac.in/~lfovia/downloads.html.