Refine-IQA: Multi-Stage Reinforcement Finetuning for Perceptual Image Quality Assessment

• URL: http://arxiv.org/abs/2508.03763v1
• Date: Mon, 04 Aug 2025 22:46:10 GMT
• Title: Refine-IQA: Multi-Stage Reinforcement Finetuning for Perceptual Image Quality Assessment
• Authors: Ziheng Jia, Jiaying Qian, Zicheng Zhang, Zijian Chen, Xiongkuo Min,
• Abstract summary: Reinforcement fine-tuning (RFT) is a proliferating paradigm for LMM training.<n>We propose a multi-stage RFT IQA framework (-IQA)<n>The resulting Refine-IQA Series Models achieve outstanding performance on both perception and scoring tasks.
• Score: 22.184690568393126
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement fine-tuning (RFT) is a proliferating paradigm for LMM training. Analogous to high-level reasoning tasks, RFT is similarly applicable to low-level vision domains, including image quality assessment (IQA). Existing RFT-based IQA methods typically use rule-based output rewards to verify the model's rollouts but provide no reward supervision for the "think" process, leaving its correctness and efficacy uncontrolled. Furthermore, these methods typically fine-tune directly on downstream IQA tasks without explicitly enhancing the model's native low-level visual quality perception, which may constrain its performance upper bound. In response to these gaps, we propose the multi-stage RFT IQA framework (Refine-IQA). In Stage-1, we build the Refine-Perception-20K dataset (with 12 main distortions, 20,907 locally-distorted images, and over 55K RFT samples) and design multi-task reward functions to strengthen the model's visual quality perception. In Stage-2, targeting the quality scoring task, we introduce a probability difference reward involved strategy for "think" process supervision. The resulting Refine-IQA Series Models achieve outstanding performance on both perception and scoring tasks-and, notably, our paradigm activates a robust "think" (quality interpreting) capability that also attains exceptional results on the corresponding quality interpreting benchmark.

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