Subjective Quality Assessment of User-Generated Content Gaming Videos

Xiangxu Yu, Zhengzhong Tu, Zhenqiang Ying, Alan C. Bovik, Neil Birkbeck, Yilin Wang, Balu Adsumilli; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2022, pp. 74-83

Abstract


Benefited from the rapid development of the digital game industry, the growing popularity of online user-generated content (UGC) videos for games has accelerated the development of perceptual video quality assessment (VQA) models specifically for gaming videos. As a novel UGC type, gaming videos are recorded by gamers and uploaded to major streaming media platforms such as YouTube and Twitch, and have been extremely popular among the audience. However, there is little work on VQA research related to gaming videos and understanding their characteristics. In order to promote the development of the gaming VQA model, we created a new UGC gaming video VQA resource, named LIVE-YouTube Gaming video quality (LIVE-YT-Gaming) database, composed of 600 authentic UGC gaming videos and 18,600 subjective quality ratings collected from an online subjective study. We also compared and analyzed several state-of-the-art (SOTA) VQA models on the new database. To support work in this field, the new database will be publicly available through the link: https://live.ece.utexas.edu/research/LIVE-YT-Gaming/index.html.

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[bibtex]
@InProceedings{Yu_2022_WACV, author = {Yu, Xiangxu and Tu, Zhengzhong and Ying, Zhenqiang and Bovik, Alan C. and Birkbeck, Neil and Wang, Yilin and Adsumilli, Balu}, title = {Subjective Quality Assessment of User-Generated Content Gaming Videos}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops}, month = {January}, year = {2022}, pages = {74-83} }