Guiding Perception-Reasoning Closer to Human in Blind Image Quality Assessment

• URL: http://arxiv.org/abs/2512.16484v1
• Date: Thu, 18 Dec 2025 12:52:37 GMT
• Title: Guiding Perception-Reasoning Closer to Human in Blind Image Quality Assessment
• Authors: Yuan Li, Yahan Yu, Youyuan Lin, Yong-Hao Yang, Chenhui Chu, Shin'ya Nishida,
• Abstract summary: We investigate how a model can acquire both human-like and self-consistent reasoning capability for blind image quality assessment (BIQA)<n>We first collect human evaluation data that capture several aspects of human perception-reasoning pipeline.<n>We adopt reinforcement learning, using human annotations as reward signals to guide the model toward human-like perception and reasoning.
• Score: 24.713568842749222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans assess image quality through a perception-reasoning cascade, integrating sensory cues with implicit reasoning to form self-consistent judgments. In this work, we investigate how a model can acquire both human-like and self-consistent reasoning capability for blind image quality assessment (BIQA). We first collect human evaluation data that capture several aspects of human perception-reasoning pipeline. Then, we adopt reinforcement learning, using human annotations as reward signals to guide the model toward human-like perception and reasoning. To enable the model to internalize self-consistent reasoning capability, we design a reward that drives the model to infer the image quality purely from self-generated descriptions. Empirically, our approach achieves score prediction performance comparable to state-of-the-art BIQA systems under general metrics, including Pearson and Spearman correlation coefficients. In addition to the rating score, we assess human-model alignment using ROUGE-1 to measure the similarity between model-generated and human perception-reasoning chains. On over 1,000 human-annotated samples, our model reaches a ROUGE-1 score of 0.512 (cf. 0.443 for baseline), indicating substantial coverage of human explanations and marking a step toward human-like interpretable reasoning in BIQA.

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