-
[pdf]
[arXiv]
[bibtex]@InProceedings{Li_2025_ICCV, author = {Li, Yixiao and Li, Xin and Zhou, Chris Wei and Xing, Shuo and Amirpour, Hadi and Hao, Xiaoshuai and Yue, Guanghui and Zhao, Baoquan and Liu, Weide and Yang, Xiaoyuan and Tu, Zhengzhong and Li, Xinyu and Song, Chuanbiao and Zhang, Chenqi and Lan, Jun and Zhu, Huijia and Wang, Weiqiang and Sun, Xiaoyan and Tian, Shishun and Yan, Dongyang and Zhang, Weixia and Chen, Junlin and Sun, Wei and Wang, Zhihua and Shi, Zhuohang and Luo, Zhizun and Ouyang, Hang and Xiao, Tianxin and Yang, Fan and Wu, Zhaowang and Deng, Kaixin}, title = {VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2025}, pages = {3372-3382} }
VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results
Abstract
This paper presents the ISRGC-Q Challenge, built upon the Image Super-Resolution Generated Content Quality Assessment (ISRGen-QA) dataset, and organized as part of the Visual Quality Assessment (VQualA) Competition at the ICCV 2025 Workshops. Unlike existing Super-Resolution Image Quality Assessment (SR-IQA) datasets, ISRGen-QA places a greater emphasis on SR images generated by the latest generative approaches, including Generative Adversarial Networks (GANs) and diffusion models. The primary goal of this challenge is to analyze the unique artifacts introduced by modern super-resolution techniques and to evaluate their perceptual quality effectively. A total of 108 participants registered for the challenge, with 4 teams submitting valid solutions and fact sheets for the final testing phase. These submissions demonstrated state-of-the-art (SOTA) performance on the ISRGen-QA dataset. The project is publicly available at: https://github.com/Lighting-YXLI/ISRGen-QA.
Related Material