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[bibtex]@InProceedings{Huang_2025_ICCV, author = {Huang, Fan and Min, Xiongkuo and Ma, Zhichao and Liu, Xiaohong and Zhou, Chris Wei and Zhai, Guangtao and Gao, Junjie and Liu, Runze and Peng, Yingzhe and Yang, Shujian and Zhang, Jin and Yang, Kai and You, Zhiyuan and Ao, Zitian and Wu, Yicheng and Zhang, Weixia and Chen, Junlin and Sun, Wei and Wang, Zhihua and Zhang, Zhe and Yang, Yang and Bai, Mingying and Du, Jiawang and Lu, Zilong and Jiang, Zhenyu and Cui, Ziguan and Gan, Zongliang and Tang, Guijin and Yang, Fan and Ouyang, Hang and Shi, Zhuohang and Xiao, Tianxin and Luo, Zhizun and Wu, Zhaowang and Deng, Kaixin and Zhang, Ruikun and Yang, Hao and Pan, Liyuan}, title = {VQualA 2025 Document Image Quality Assessment Challenge}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2025}, pages = {3344-3353} }
VQualA 2025 Document Image Quality Assessment Challenge
Abstract
This paper reports on the VQualA 2025 Document Image Quality Assessment Challenge, which will be held in conjunction with the Visual Quality Assessment Competition Workshop (VQualA) at ICCV 2025. This challenge is to address a major challenge in the field of image processing, namely, image quality assessment (IQA) for enhanced document images. The challenge uses the IQA Dataset for enhanced document images (DIQA-5000), which has a total of 5000 enhanced document images with human-annotated Mean Opinion Scores (MOS), including diverse combinations of document enhancement algorithms. The challenge has a total of 120 registered participants. 16 participating teams submitted their prediction results during the development phase, with a total of 183 submissions. A total of 97 submissions were submitted by 16 participating teams during the final testing phase. Finally, 7 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance.
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