Investigate the Low-level Visual Perception in Vision-Language based Image Quality Assessment

• URL: http://arxiv.org/abs/2512.09573v1
• Date: Wed, 10 Dec 2025 12:06:47 GMT
• Title: Investigate the Low-level Visual Perception in Vision-Language based Image Quality Assessment
• Authors: Yuan Li, Zitang Sun, Yen-Ju Chen, Shin'ya Nishida,
• Abstract summary: We introduce a low-level distortion perception task that requires models to classify specific distortion types.<n>Our analysis shows that although MLLMs are structurally capable of representing such distortions, they tend to overfit training templates.<n>We show that improving the alignment of the vision encoder dramatically enhances distortion recognition accuracy, increasing it from 14.92% to 84.43%.
• Score: 7.969076042774561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Image Quality Assessment (IQA) have leveraged Multi-modal Large Language Models (MLLMs) to generate descriptive explanations. However, despite their strong visual perception modules, these models often fail to reliably detect basic low-level distortions such as blur, noise, and compression, and may produce inconsistent evaluations across repeated inferences. This raises an essential question: do MLLM-based IQA systems truly perceive the visual features that matter? To examine this issue, we introduce a low-level distortion perception task that requires models to classify specific distortion types. Our component-wise analysis shows that although MLLMs are structurally capable of representing such distortions, they tend to overfit training templates, leading to biases in quality scoring. As a result, critical low-level features are weakened or lost during the vision-language alignment transfer stage. Furthermore, by computing the semantic distance between visual features and corresponding semantic tokens before and after component-wise fine-tuning, we show that improving the alignment of the vision encoder dramatically enhances distortion recognition accuracy, increasing it from 14.92% to 84.43%. Overall, these findings indicate that incorporating dedicated constraints on the vision encoder can strengthen text-explainable visual representations and enable MLLM-based pipelines to produce more coherent and interpretable reasoning in vision-centric tasks.

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