NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results

• URL: http://arxiv.org/abs/2505.03007v1
• Date: Mon, 05 May 2025 20:06:11 GMT
• Title: NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results
• Authors: Nikolay Safonov, Alexey Bryncev, Andrey Moskalenko, Dmitry Kulikov, Dmitry Vatolin, Radu Timofte, Haibo Lei, Qifan Gao, Qing Luo, Yaqing Li, Jie Song, Shaozhe Hao, Meisong Zheng, Jingyi Xu, Chengbin Wu, Jiahui Liu, Ying Chen, Xin Deng, Mai Xu, Peipei Liang, Jie Ma, Junjie Jin, Yingxue Pang, Fangzhou Luo, Kai Chen, Shijie Zhao, Mingyang Wu, Renjie Li, Yushen Zuo, Shengyun Zhong, Zhengzhong Tu,
• Abstract summary: This paper presents an overview of the NTIRE 2025 Challenge on Video Enhancement.<n>The challenge constructed a set of 150 user-generated content videos without reference ground truth.<n>The goal of the participants was to develop an algorithm capable of improving the visual quality of such videos.
• Score: 73.23764765210825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an overview of the NTIRE 2025 Challenge on UGC Video Enhancement. The challenge constructed a set of 150 user-generated content videos without reference ground truth, which suffer from real-world degradations such as noise, blur, faded colors, compression artifacts, etc. The goal of the participants was to develop an algorithm capable of improving the visual quality of such videos. Given the widespread use of UGC on short-form video platforms, this task holds substantial practical importance. The evaluation was based on subjective quality assessment in crowdsourcing, obtaining votes from over 8000 assessors. The challenge attracted more than 25 teams submitting solutions, 7 of which passed the final phase with source code verification. The outcomes may provide insights into the state-of-the-art in UGC video enhancement and highlight emerging trends and effective strategies in this evolving research area. All data, including the processed videos and subjective comparison votes and scores, is made publicly available at https://github.com/msu-video-group/NTIRE25_UGC_Video_Enhancement.

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