IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment
For IQA researchers, it addresses the insensitivity of MLLMs to low-level perceptual degradations by augmenting reasoning with explicit perceptual observations.
IQA-T1 improves image quality assessment by integrating tool-generated visual evidence (noise maps, gradient stats, frequency spectra) into MLLM reasoning, achieving best overall performance across seven benchmarks.
Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability. Recent approaches based on multimodal large language models (MLLMs) introduce textual reasoning for quality prediction, yet their judgments rely heavily on semantically biased internal representations, making them insensitive to low-level perceptual degradations. We propose IQA-T1, a tool-based visual evidence reasoning framework that augments MLLM reasoning with explicit perceptual observations. During inference, the model autonomously invokes specialized analysis tools to generate structured visual evidence, such as noise residual maps, gradient statistics, and frequency spectra, which are progressively integrated into the reasoning process. To support this paradigm, we construct Q-Tool, a dataset containing 11k multimodal reasoning chains grounded in tool-generated evidence. Extensive experiments on seven IQA benchmarks show that IQA-T1 achieves the best overall performance across datasets while producing interpretable and evidence-grounded quality assessments. Code and dataset are available at https://github.com/zibuyu-02/IQA-T1.