Kavli Affiliate: Li Xin Li
| First 5 Authors: Yixiao Li, Yixiao Li, , ,
| Summary:
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.
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