AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results
Abstract
Video quality assessment (VQA) is a crucial task in the development of video compression standards, as it directly impacts the viewer experience. This paper presents the results of the Compressed Video Quality Assessment challenge, held in conjunction with the Advances in Image Manipulation (AIM) workshop at ECCV 2024. The challenge aimed to evaluate the performance of VQA methods on a diverse dataset of 459 videos, encoded with 14 codecs of various compression standards (AVC/H.264, HEVC/H.265, AV1, and VVC/H.266) and containing a comprehensive collection of compression artifacts. To measure the methods performance, we employed traditional correlation coefficients between their predictions and subjective scores, which were collected via large-scale crowdsourced pairwise human comparisons. For training purposes, participants were provided with the Compressed Video Quality Assessment Dataset (CVQAD), a previously developed dataset of 1022 videos. Up to 30 participating teams registered for the challenge, while we report the results of 6 teams, which submitted valid final solutions and code for reproducing the results. Moreover, we calculated and present the performance of state-of-the-art VQA methods on the developed dataset, providing a comprehensive benchmark for future research. The dataset, results, and online leaderboard are publicly available at https://challenges.videoprocessing.ai/challenges/compressed-video-quality-assessment.html .
Cite
Text
Smirnov et al. "AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results." European Conference on Computer Vision Workshops, 2024. doi:10.1007/978-3-031-91856-8_13Markdown
[Smirnov et al. "AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results." European Conference on Computer Vision Workshops, 2024.](https://mlanthology.org/eccvw/2024/smirnov2024eccvw-aim/) doi:10.1007/978-3-031-91856-8_13BibTeX
@inproceedings{smirnov2024eccvw-aim,
title = {{AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results}},
author = {Smirnov, Maksim and Gushchin, Aleksandr and Antsiferova, Anastasia and Vatolin, Dmitriy S. and Timofte, Radu and Jia, Ziheng and Zhang, Zicheng and Sun, Wei and Qian, Jiaying and Cao, Yuqin and Sun, Yinan and Zhu, Yuxin and Min, Xiongkuo and Zhai, Guangtao and De, Kanjar and Luo, Qing and Zhang, Ao-Xiang and Zhang, Peng and Lei, Haibo and Jiang, Linyan and Li, Yaqing and Meng, Wenhui and Tan, Xiaoheng and Wang, Haiqiang and Xu, Xiaozhong and Liu, Shan and Chen, Zhenzhong and Cheng, Zhengxue and Xiao, Jiahao and Xu, Jun and He, Chenlong and Zheng, Qi and Zhu, Ruoxi and Li, Min and Fan, Yibo and Tu, Zhengzhong},
booktitle = {European Conference on Computer Vision Workshops},
year = {2024},
pages = {212-228},
doi = {10.1007/978-3-031-91856-8_13},
url = {https://mlanthology.org/eccvw/2024/smirnov2024eccvw-aim/}
}