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Quality Enhancement of Gaming Content using Generative Adversarial Networks
Citation key avanaki2020a
Author Avanaki, Nasim Jamshidi and Zadtootaghaj, Saman and Barman, Nabajeet and Schmidt, Steven and Martini, Maria G. and Möller, Sebastian
Title of Book 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX)
Pages 1–6
Year 2020
ISBN 978-1-7281-5965-2
DOI 10.1109/QoMEX48832.2020.9123074
Location Athlone, Ireland
Month may
Publisher IEEE
Series QoMEX ’20
How Published Fullpaper
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.
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