학술논문
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
Document Type
Working Paper
Author
Conde, Marcos V.; Zadtootaghaj, Saman; Barman, Nabajeet; Timofte, Radu; He, Chenlong; Zheng, Qi; Zhu, Ruoxi; Tu, Zhengzhong; Wang, Haiqiang; Chen, Xiangguang; Meng, Wenhui; Pan, Xiang; Shi, Huiying; Zhu, Han; Xu, Xiaozhong; Sun, Lei; Chen, Zhenzhong; Liu, Shan; Zhang, Zicheng; Wu, Haoning; Zhou, Yingjie; Li, Chunyi; Liu, Xiaohong; Lin, Weisi; Zhai, Guangtao; Sun, Wei; Cao, Yuqin; Jiang, Yanwei; Jia, Jun; Zhang, Zhichao; Chen, Zijian; Zhang, Weixia; Min, Xiongkuo; Göring, Steve; Qi, Zihao; Feng, Chen
Source
Subject
Language
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.
Comment: CVPR 2024 Workshop -- AI for Streaming (AIS) Video Quality Assessment Challenge
Comment: CVPR 2024 Workshop -- AI for Streaming (AIS) Video Quality Assessment Challenge