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From Glance to Scrutiny: Progressive Distortion Reasoning for Fine-Grained Image Quality Assessment

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Multi-modal large language models (MLLMs) have demonstrated significant potential in image quality assessment (IQA) by bridging visual perception with descriptive evaluations. However, existing approaches mainly focus on holistic quality prediction, often functioning as black boxes that provide limited insight into where distortions occur and how they affect perceived quality, hindering fine-grained analysis of localized and heterogeneous degradations. We propose GS-IQA, a framework that reformulates IQA as a progressive Where--What--How diagnosis, emulating the human perceptual process from a

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