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.
@article{arxiv.2509.06413,
title = {VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results},
author = {Yixiao Li and Xin Li and Chris Wei Zhou and Shuo Xing and Hadi Amirpour and Xiaoshuai Hao and Guanghui Yue and Baoquan Zhao and Weide Liu and Xiaoyuan Yang and Zhengzhong Tu and Xinyu Li and Chuanbiao Song and Chenqi Zhang and Jun Lan and Huijia Zhu and Weiqiang Wang and Xiaoyan Sun and Shishun Tian and Dongyang Yan and Weixia Zhang and Junlin Chen and Wei Sun and Zhihua Wang and Zhuohang Shi and Zhizun Luo and Hang Ouyang and Tianxin Xiao and Fan Yang and Zhaowang Wu and Kaixin Deng},
journal= {arXiv preprint arXiv:2509.06413},
year = {2025}
}