ML Anthology

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
CVPRW 2024 pp. 5826-5837
doi:10.1109/CVPRW63382.2024.00591 /cvprw/2024/conde2024cvprw-ais/

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 results during the challenge period. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for Video Quality Assessment of user-generated content.

PDF CVPRW Semantic Scholar

Cite

Text

Conde et al. "AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00591

Markdown

[Conde et al. "AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/conde2024cvprw-ais/) doi:10.1109/CVPRW63382.2024.00591

BibTeX

@inproceedings{conde2024cvprw-ais,
  title     = {{AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results}},
  author    = {Conde, Marcos V. and Zadtootaghaj, Saman and Barman, Nabajeet and Timofte, Radu and He, Chenlong and Zheng, Qi and Zhu, Ruoxi and Tu, Zhengzhong and Wang, Haiqiang and Chen, Xiangguang and Meng, Wenhui and Pan, Xiang and Shi, Huiying and Zhu, Han and Xu, Xiaozhong and Sun, Lei and Chen, Zhenzhong and Liu, Shan and Zhang, Zicheng and Wu, Haoning and Zhou, Yingjie and Li, Chunyi and Liu, Xiaohong and Lin, Weisi and Zhai, Guangtao and Sun, Wei and Cao, Yuqin and Jiang, Yanwei and Jia, Jun and Zhang, Zhichao and Chen, Zijian and Zhang, Weixia and Min, Xiongkuo and Göring, Steve and Qi, Zihao and Feng, Chen},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2024},
  pages     = {5826-5837},
  doi       = {10.1109/CVPRW63382.2024.00591},
  url       = {https://mlanthology.org/cvprw/2024/conde2024cvprw-ais/}
}