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

• URL: http://arxiv.org/abs/2404.16205v1
• Date: Wed, 24 Apr 2024 21:02:14 GMT
• Title: AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
• Authors: 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,
• Abstract summary: 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 perceptual quality of videos. The user-generated videos from the YouTube dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions.
• Score: 140.47245070508353
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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