评测生成式超分图像质量，发现新伪影并验证顶尖评估方法
VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results
- 基于最新生成模型的超分图像数据集，聚焦真实生成内容质量
- 108人参赛4队提交有效方案，在新数据集上达当前最优性能
- 适合关注生成图像质量评估与视觉感知的研究者
本文介绍了基于图像超分辨率生成内容质量评估（ISRGen-QA）数据集的ISRGC-Q挑战赛，作为ICCV 2025研讨会视觉质量评估（VQualA）竞赛的一部分。与现有超分辨率图像质量评估数据集不同，ISRGen-QA更侧重于最新生成式方法（包括生成对抗网络GAN和扩散模型）生成的超分图像。该挑战旨在分析现代超分技术引入的独特伪影，并有效评估其感知质量。共有108名参与者注册，4支团队提交了有效的解决方案和结果报告，其在ISRGen-QA数据集上表现出当前最优（SOTA）性能。项目已公开：https://github.com/Lighting-YXLI/ISRGen-QA。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。