This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted code and models. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
The paper reviews the AIS 2024 Video Quality Assessment Challenge focusing on user-generated content, evaluating deep learning-based methods for efficient video quality assessment under time constraints.
- Year
- 2024
- Venue
- arXiv 2024
- Authors
- 36
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2404.16205ARXIV-DEFAULT
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Authors
36Guangtao ZhaiXiaohong LiuZhengzhong TuRadu TimofteHaoNing WuWeisi LinHan ZhuLei SunZiCheng ZhangWeixia ZhangChunyi LiXiongkuo MinMarcos V. CondeQi ZhengWei SunChen FengYuqin CaoZijian ChenXiang PanShan LiuYingjie ZhouJun JiaYanwei JiangSaman ZadtootaghajNabajeet BarmanChenlong HeRuoxi ZhuHaiqiang WangXiangguang ChenWenhui MengHuiying ShiXiaozhong XuZhenzhong ChenZhichao ZhangSteve GöringZihao Qi