AIM 2025 Challenge on Robust Offline Video Super-Resolution: Dataset, Methods and Results

Nikolai Karetin, Ivan Molodetskikh, Dmitry Vatolin, Radu Timofte, Yixin Yang, Junyang Chen, Jiangxin Dong, Jinshan Pan, Zhihao Liu, Lishen Qu, Shihao Zhou, Jufeng Yang, Yuxuan Jiang, Siyue Teng, Chengxi Zeng, Fan Zhang, David Bull, Qi Tang, Jie Liu, Jie Tang, Gangshan Wu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 5674-5682

Abstract

This paper presents the AIM 2025 Challenge on Robust Offline Video Super-Resolution, the first challenge focusing on 4x upscaling of heavily degraded 270p videos to high-quality 1080p sequences. The challenge addresses the practical problem of enhancing low-quality video content while suppressing noise, blur, and compression artifacts under realistic hardware constraints. We introduce a comprehensive benchmark consisting of 30 diverse video clips spanning camera-shot and animated content, along with a novel synthetic degradation pipeline that ensures reproducible results. Our evaluation methodology employs subjective pairwise comparisons conducted through crowdsourcing. The challenge attracted significant participation and established new baselines for robust video super-resolution in challenging real-world scenarios.

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[bibtex]
@InProceedings{Karetin_2025_ICCV, author = {Karetin, Nikolai and Molodetskikh, Ivan and Vatolin, Dmitry and Timofte, Radu and Yang, Yixin and Chen, Junyang and Dong, Jiangxin and Pan, Jinshan and Liu, Zhihao and Qu, Lishen and Zhou, Shihao and Yang, Jufeng and Jiang, Yuxuan and Teng, Siyue and Zeng, Chengxi and Zhang, Fan and Bull, David and Tang, Qi and Liu, Jie and Tang, Jie and Wu, Gangshan}, title = {AIM 2025 Challenge on Robust Offline Video Super-Resolution: Dataset, Methods and Results}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2025}, pages = {5674-5682} }