25支团队挑战无参考用户生成视频增强，8000人评分验证效果。
NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results
- 构建150段真实用户视频，含噪声、模糊等多重退化。
- 超8000人次众包打分，7个团队通过代码验证完成挑战。
- 数据与结果开源，助力视频增强技术研究与应用。
本文概述了NTIRE 2025年用户生成视频增强挑战赛。挑战赛构建了150段无参考真值的用户生成内容视频，包含真实世界中的噪声、模糊、色彩褪色、压缩伪影等退化问题。参赛者需开发算法以提升此类视频的视觉质量。鉴于用户生成内容在短视频平台的广泛应用，该任务具有重要实际意义。评估基于众包主观质量评分，获得超过8000名评估者的投票。挑战吸引了25支以上团队提交方案，其中7支通过最终阶段的源代码验证。研究成果可为当前用户生成视频增强技术提供洞见，并揭示该领域新兴趋势与有效策略。所有数据，包括处理后的视频、主观对比投票及评分，均已公开发布于https://github.com/msu-video-group/NTIRE25_UGC_Video_Enhancement。
原文摘要 · Abstract (English)
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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