Tool-IQA: 利用简单工具增强图像质量评估
Tool-IQA: Augmenting Image Quality Assessment with Simple Tools
AI总结：
提出Tool-IQA，通过为视觉语言模型配备放大镜和伽马校正器等简单工具，将被动评分转变为工具增强的工作流程，显著提升图像质量评估性能。
摘要：
视觉语言模型（VLM）越来越多地被用于图像质量评估（IQA）。然而，当前方法通常采用静态的一次性评分范式，而人类通过动态视觉检查（例如，选择性调整视图以验证细节和细微伪影）来评估图像质量。具体来说，仅依赖单次观察存在两个主要限制：首先，仅在全局尺度上感知图像限制了对更精细局部细节的评估；其次，图像的原始强度分布可能压倒可见性，导致对图像质量的检查不足。为了解决这些问题，我们提出了Tool-IQA，将评估机制从被动评分转变为工具增强的工作流程。特别地，我们为VLM配备了简单而有效的视图工具：用于检查局部细节的放大镜，以及用于揭示可见性和隐藏伪影的伽马校正器。评估遵循一个结构化的流程，包括带有评分标准的初始观察、工具增强的深入检查以及最终校准质量分数的量化。此外，为了确保高效且有目的地调用工具，我们引入了一种批量感知的训练策略，以奖励能够产生积极贡献的工具交互，而不仅仅是鼓励使用。在各种IQA基准上的实验表明，通过有效的工具调用和校准评估，我们提出的Tool-IQA显著优于现有最先进的模型，例如，在具有挑战性的CLIVE数据集上实现了0.854的PLCC。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
