Tool-IQA: Augmenting Image Quality Assessment with Simple Tools

• URL: http://arxiv.org/abs/2606.16082v1
• Date: Mon, 15 Jun 2026 00:47:05 GMT
• Title: Tool-IQA: Augmenting Image Quality Assessment with Simple Tools
• Abstract summary: Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA)<n>We propose Tool-IQA, shifting the assessment mechanism from passive scoring to a tool-augmented workflow.<n>Our proposed Tool-IQA significantly outperforms existing state-of-the-art models.
• Score: 17.03478604082667
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Vision-Language Models (VLMs) have been increasingly adopted for Image Quality Assessment (IQA). However, current methods typically employ a static one-shot scoring paradigm, despite the fact that humans assess image quality through dynamic visual inspection, e.g., selectively adjusting views to verify details and subtle artifacts. Specifically, relying solely on a single-pass observation introduces two primary limitations: first, perceiving the image only at a global scale restricts the assessment of finer local details; second, the original intensity distribution of the image may overwhelm the visibility, leading to insufficient inspection of image quality. To address these issues, we propose Tool-IQA, shifting the assessment mechanism from passive scoring to a tool-augmented workflow. In particular, we equip VLMs with simple yet effective view tools: a Magnifier to inspect local details, and a Gamma Corrector to uncover visibility and hidden artifacts. The assessment follows a structured pipeline that consists of an initial observation with rubric notes, a tool-augmented in-depth inspection, and a final quantification for calibrated quality score. Furthermore, to ensure efficient and purposeful tool callings, we introduce a batch-aware training strategy to reward tool interactions that can yield positive contributions rather than simply encouraging usage. Experiments on a variety of IQA benchmarks demonstrate that, with effective tool calling and calibrated assessment, our proposed Tool-IQA significantly outperforms existing state-of-the-art models, e.g., it achieves a PLCC of 0.854 on the challenging CLIVE dataset.

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