Quality enhancement of gaming content using generative adversarial networks

Avanaki, Nasim Jamshidi, Zadtootaghaj, Saman, Barman, Nabajeet, Schmidt, Steven, Martini, Maria G. and Möller, Sebastian (2020) Quality enhancement of gaming content using generative adversarial networks. In: 12th International Conference on Quality of Multimedia Experience; 26 - 28 May 2020, Athlone, Ireland (presented online).

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Abstract

Recently, streaming of gameplay scenes has gained much attention, as evident with the rise of platforms such as Twitch.tv and Facebook Gaming. These streaming services have to deal with many challenges due to the low quality of source materials caused by client devices, network limitations such as bandwidth and packet loss, as well as low delay requirements. Spatial video artifact such as blockiness and blurriness as a result of as video compression or up-scaling algorithms can significantly impact the Quality of Experience of end-users of passive gaming video streaming applications. In this paper, we investigate solutions to enhance the video quality of compressed gaming content. Recently, several super-resolution enhancement techniques using Generative Adversarial Network (e.g., SRGAN) have been proposed, which are shown to work with high accuracy on non-gaming content. Towards this end, we improved the SRGAN by adding a modified loss function as well as changing the generator network such as layer levels and skip connections to improve the flow of information in the network, which is shown to improve the perceived quality significantly. In addition, we present a performance evaluation of improved SRGAN for the enhancement of frame quality caused by compression and rescaling artifacts for gaming content encoded in multiple resolution-bitrate pairs.

Item Type: Conference or Workshop Item (Paper)
Event Title: 12th International Conference on Quality of Multimedia Experience
Organising Body: Institute of Electrical and Electronics Engineers
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: 29 May 2020 06:44
Last Modified: 29 May 2020 06:44
URI: http://eprints.kingston.ac.uk/id/eprint/45172

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