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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.06413 |
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| _version_ | 1866909775665561600 |
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| author | Li, Yixiao Li, Xin Zhou, Chris Wei Xing, Shuo Amirpour, Hadi Hao, Xiaoshuai Yue, Guanghui Zhao, Baoquan Liu, Weide Yang, Xiaoyuan Tu, Zhengzhong Li, Xinyu Song, Chuanbiao Zhang, Chenqi Lan, Jun Zhu, Huijia Wang, Weiqiang Sun, Xiaoyan Tian, Shishun Yan, Dongyang Zhang, Weixia Chen, Junlin Sun, Wei Wang, Zhihua Shi, Zhuohang Luo, Zhizun Ouyang, Hang Xiao, Tianxin Yang, Fan Wu, Zhaowang Deng, Kaixin |
| author_facet | Li, Yixiao Li, Xin Zhou, Chris Wei Xing, Shuo Amirpour, Hadi Hao, Xiaoshuai Yue, Guanghui Zhao, Baoquan Liu, Weide Yang, Xiaoyuan Tu, Zhengzhong Li, Xinyu Song, Chuanbiao Zhang, Chenqi Lan, Jun Zhu, Huijia Wang, Weiqiang Sun, Xiaoyan Tian, Shishun Yan, Dongyang Zhang, Weixia Chen, Junlin Sun, Wei Wang, Zhihua Shi, Zhuohang Luo, Zhizun Ouyang, Hang Xiao, Tianxin Yang, Fan Wu, Zhaowang Deng, Kaixin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06413 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results Li, Yixiao Li, Xin Zhou, Chris Wei Xing, Shuo Amirpour, Hadi Hao, Xiaoshuai Yue, Guanghui Zhao, Baoquan Liu, Weide Yang, Xiaoyuan Tu, Zhengzhong Li, Xinyu Song, Chuanbiao Zhang, Chenqi Lan, Jun Zhu, Huijia Wang, Weiqiang Sun, Xiaoyan Tian, Shishun Yan, Dongyang Zhang, Weixia Chen, Junlin Sun, Wei Wang, Zhihua Shi, Zhuohang Luo, Zhizun Ouyang, Hang Xiao, Tianxin Yang, Fan Wu, Zhaowang Deng, Kaixin Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2509.06413 |