No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics

Dendi, Sathya Veera Reddy and Channappayya, Sumohana (2020) No-Reference Video Quality Assessment Using Natural Spatiotemporal Scene Statistics. IEEE Transactions on Image Processing. pp. 5612-5624. ISSN 1941-0042

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Abstract

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 .

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IITH Creators:
IITH CreatorsORCiD
Channappayya, SumohanaUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Natural scene statistics of videos,spatiotemporal Gabor filters,human visual system (HVS), SVR and 3D-MSCN
Subjects: Electrical Engineering
Electrical Engineering > Automation & Control Systems
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 27 Apr 2020 06:45
Last Modified: 31 Aug 2021 07:03
URI: http://raiith.iith.ac.in/id/eprint/7602
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