AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

Marcos V. Conde,Saman Zadtootaghaj,Nabajeet Barman,Radu Timofte, Chenlong He,Qi Zheng, Ruoxi Zhu,Zhengzhong Tu,Haiqiang Wang, Xiangguang Chen,Wenhui Meng,Xiang Pan, Huiying Shi,Han Zhu,Xiaozhong Xu,Lei Sun,Zhenzhong Chen,Shan Liu,Zicheng Zhang,Haoning Wu,Yingjie Zhou,Chunyi Li,Xiaohong Liu,Weisi Lin,Guangtao Zhai,Wei Sun,Yuqin Cao, Yanwei Jiang,Jun Jia, Zhichao Zhang,Zijian Chen,Weixia Zhang,Xiongkuo Min,Steve Göring,Zihao Qi,Chen Feng

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)（2024）

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Abstract
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge,focused on User-Generated Content (UGC). The aim of this challenge is to gatherdeep learning-based methods capable of estimating the perceptual quality of UGCvideos. The user-generated videos from the YouTube UGC Dataset include diversecontent (sports, games, lyrics, anime, etc.), quality and resolutions. Theproposed methods must process 30 FHD frames under 1 second. In the challenge, atotal of 102 participants registered, and 15 submitted code and models. Theperformance of the top-5 submissions is reviewed and provided here as a surveyof diverse deep models for efficient video quality assessment of user-generatedcontent.
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Key words
VQA,video,AIS,video quality assessment,Image Quality Assessment,IQA
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