No-reference video quality estimation based on machine learning for passive gaming video streaming applications

Barman, Nabajeet, Jammeh, Emmanuel, Ghorashi, Seyed Ali and Martini, Maria G. (2019) No-reference video quality estimation based on machine learning for passive gaming video streaming applications. IEEE Access, 7, pp. 74511-74527. ISSN (online) 2169-3536

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Abstract

Recent years have seen increasing growth and popularity of gaming services, both interactive and passive. While interactive gaming video streaming applications have received much attention, passive gaming video streaming, in-spite of its huge success and growth in recent years, has seen much less interest from the research community. For the continued growth of such services in the future, it is imperative that the end user gaming quality of experience (QoE) is estimated so that it can be controlled and maximized to ensure user acceptance. Previous quality assessment studies have shown not so satisfactory performance of existing No-reference (NR) video quality assessment (VQA) metrics. Also, due to the inherent nature and different requirements of gaming video streaming applications, as well as the fact that gaming videos are perceived differently from non-gaming content (as they are usually computer generated and contain artificial/synthetic content), there is a need for application specific light-weight, no-reference gaming video quality prediction models. In this paper, we present two NR machine learning based quality estimation models for gaming video streaming, NR-GVSQI and NR-GVSQE, using NR features such as bitrate, resolution, blockiness, etc. We evaluate their performance on different gaming video datasets and show that the proposed models outperform the current state-of-the-art no-reference metrics, while also reaching a prediction accuracy comparable to the best known full reference metric.

Item Type: Article
Additional Information: This work was supported by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 643072 and Kingston University's ISC Fund.
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Faculty of Science, Engineering and Computing > School of Computer Science and Mathematics
Depositing User: Nabajeet Barman
Date Deposited: 12 Jun 2019 07:59
Last Modified: 18 Jul 2019 15:46
DOI: https://doi.org/10.1109/ACCESS.2019.2920477
URI: http://eprints.kingston.ac.uk/id/eprint/43406

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