Tool-IQA: Augmenting Image Quality Assessment with Simple Tools

2026-06-14

AI Summary

This work addresses the limitations of existing image quality assessment (IQA) methods, which rely on static, single-pass scoring and fail to capture the dynamic, localized nature of human visual inspection. To overcome this, the authors propose a tool-augmented active IQA framework that reformulates the assessment process into three stages: structured observation, tool-assisted scrutiny, and calibrated scoring. For the first time, interactive viewing tools—namely a magnifier and a gamma corrector—are introduced to enhance the visual language model’s sensitivity to local artifacts and fine details. A batch-aware training strategy is further devised to improve tool invocation efficiency. The proposed method achieves state-of-the-art performance across multiple IQA benchmarks, attaining a PLCC of 0.854 on the CLIVE dataset.

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.

Problem

• Image Quality Assessment
• Vision-Language Models
• Local Details
• Visibility
• Artifacts

Innovation

• Tool-augmented IQA
• Vision-Language Models
• Interactive Image Assessment
• Gamma Correction
• Magnifier Tool