NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915275214946304 |
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| author | Safonov, Nikolay Bryncev, Alexey Moskalenko, Andrey Kulikov, Dmitry Vatolin, Dmitry Timofte, Radu Lei, Haibo Gao, Qifan Luo, Qing Li, Yaqing Song, Jie Hao, Shaozhe Zheng, Meisong Xu, Jingyi Wu, Chengbin Liu, Jiahui Chen, Ying Deng, Xin Xu, Mai Liang, Peipei Ma, Jie Jin, Junjie Pang, Yingxue Luo, Fangzhou Chen, Kai Zhao, Shijie Wu, Mingyang Li, Renjie Zuo, Yushen Zhong, Shengyun Tu, Zhengzhong |
| author_facet | Safonov, Nikolay Bryncev, Alexey Moskalenko, Andrey Kulikov, Dmitry Vatolin, Dmitry Timofte, Radu Lei, Haibo Gao, Qifan Luo, Qing Li, Yaqing Song, Jie Hao, Shaozhe Zheng, Meisong Xu, Jingyi Wu, Chengbin Liu, Jiahui Chen, Ying Deng, Xin Xu, Mai Liang, Peipei Ma, Jie Jin, Junjie Pang, Yingxue Luo, Fangzhou Chen, Kai Zhao, Shijie Wu, Mingyang Li, Renjie Zuo, Yushen Zhong, Shengyun Tu, Zhengzhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results Safonov, Nikolay Bryncev, Alexey Moskalenko, Andrey Kulikov, Dmitry Vatolin, Dmitry Timofte, Radu Lei, Haibo Gao, Qifan Luo, Qing Li, Yaqing Song, Jie Hao, Shaozhe Zheng, Meisong Xu, Jingyi Wu, Chengbin Liu, Jiahui Chen, Ying Deng, Xin Xu, Mai Liang, Peipei Ma, Jie Jin, Junjie Pang, Yingxue Luo, Fangzhou Chen, Kai Zhao, Shijie Wu, Mingyang Li, Renjie Zuo, Yushen Zhong, Shengyun Tu, Zhengzhong Computer Vision and Pattern Recognition 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. |
| title | NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.03007 |