IQA-Spider: Unifying Multi-Granularity Image Quality Assessment with Reasoning, Grounding and Referring

We present IQA-Spider, the first image quality assessment (IQA) framework that unifies reasoning, grounding, and referring into a single LMM-based framework for multi-granularity quality understanding. Existing LMM-based IQA methods typically support only partial perception dimensions, such as quality description and question answering~(\textit{i.e.}, reasoning) or pixel-level grounding. This limitation largely stems from the absence of (i) a unified task and data formulation and (ii) effective optimization paradigms for multi-granularity learning. To address these limitations, we formulate a rigorous four-task paradigm covering global and local quality description, pixel-level grounding, and region-level referring. Based on this formulation, we construct a corresponding IQA dataset with a scalable and automatic annotation pipeline, thereby providing a solid foundation for unified multi-granularity learning. To further enable unified perception, we adopt a conflict-free two-stage design that progressively extends text-level multi-granularity understanding to pixel-level grounding: (i) the first stage equips the model with fine-grained text-level reasoning across multiple IQA tasks, and (ii) the second stage introduces a training-free text-to-point grounding paradigm, which bridges textual semantics and pixel-level perception by mapping token logits to spatial coordinates. Based on these efforts, we achieve IQA-Spider with unified multi-granularity explainable image quality assessment. Extensive experiments across multiple benchmarks demonstrate strong performance, validating the effectiveness and versatility of the proposed formulation and framework. Our code and dataset will be released at: \href{https://github.com/Helen1p/IQA-Spider.git}{https://github.com/Helen1p/IQA-Spider.git}.

Images are used everywhere, from social media and photography to medical, industrial, and creative applications. However, automatically judging image quality is still difficult because quality problems can appear at different levels: an image may look poor overall, a specific region may be degraded, or a user may want to know why a certain part looks bad. This paper studies how to build an image quality assessment system that can evaluate images more flexibly and explain its judgments in a way that is easier to understand. Our work connects several related abilities, including giving an overall quality judgment, locating quality-related regions, referring to specific visual areas, and providing reasoning about the result. This makes image quality assessment more useful for real-world scenarios where users need not only a score, but also an explanation of what is wrong and where the problem appears.