AI RESEARCH
Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement
arXiv CS.CV
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ArXi:2604.16858v1 Announce Type: new Image Quality Assessment (IQA) models are increasingly deployed as perceptual critics to guide generative models and image restoration. This role demands not only accurate scores but also actionable, localized feedback. However, current MLLM-based methods adopt a single-look, language-only paradigm, which departs from human evidence-seeking judgment and yields weakly grounded rationales, limiting their reliability for in-the-loop refinement. We propose Q-DeepSight, a think-with-image framework that emulates this human-like process.