NR-GVQM : a no reference gaming video quality metric

Zadtootaghaj, Saman, Barman, Nabajeet, Schmidt, Steven, Martini, Maria and Möller, Sebastian (2018) NR-GVQM : a no reference gaming video quality metric. In: 2018 IEEE International Symposium on Multimedia (ISM); 10 - 12 Dec 2018, Taichung City, Taiwan.

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Abstract

Gaming as a popular system has recently expanded the associated services, by stepping into live streaming services. Live gaming video streaming is not only limited to cloud gaming services, such as Geforce Now, but also include passive streaming, where the players' gameplay is streamed both live and ondemand over services such as Twitch.tv and YouTubeGaming. So far, in terms of gaming video quality assessment, typical video quality assessment methods have been used. However, their performance remains quite unsatisfactory. In this paper, we present a new No Reference (NR) gaming video quality metric called NR-GVQM with performance comparable to state-of-the-art Full Reference (FR) metrics. NR-GVQM is designed by training a Support Vector Regression (SVR) with the Gaussian kernel using nine frame-level indexes such as naturalness and blockiness as input features and Video Multimethod Assessment Fusion (VMAF) scores as the ground truth. Our results based on a publicly available dataset of gaming videos are shown to have a correlation score of 0.98 with VMAF and 0.89 with MOS scores. We further present two approaches to reduce computational complexity.

Item Type: Conference or Workshop Item (Paper)
Event Title: 2018 IEEE International Symposium on Multimedia (ISM)
Organising Body: The Institute of Electrical and Electronics Engineers
Additional Information: Published in: 2018 IEEE International Symposium on Multimedia (ISM). Piscataway, NJ : IEEE. ISBN 9781538668580.
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: 21 Feb 2020 13:44
Last Modified: 21 Feb 2020 13:44
URI: http://eprints.kingston.ac.uk/id/eprint/42732

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