The development of multimodal large language models (MLLMs) enables the evaluation of image quality through natural language descriptions. This advancement allows for more detailed assessments. However, these MLLM-based IQA methods primarily rely on general contextual descriptions, sometimes limiting fine-grained quality assessment. To address this limitation, we introduce a new image quality assessment (IQA) task paradigm, **grounding-IQA**. This paradigm integrates multimodal referring and grounding with IQA to realize more fine-grained quality perception, thereby extending existing IQA. Specifically, grounding-IQA comprises two subtasks: grounding-IQA-description (GIQA-DES) and visual question answering (GIQA-VQA). GIQA-DES involves detailed descriptions with precise locations (e.g., bounding boxes), while GIQA-VQA focuses on quality QA for local regions. To realize grounding-IQA, we construct a corresponding dataset, GIQA-160K, through our proposed automated annotation pipeline. Furthermore, we develop a well-designed benchmark, GIQA-Bench. The benchmark evaluates the grounding-IQA performance from three perspectives: description quality, VQA accuracy, and grounding precision. Experiments demonstrate that our proposed method facilitates the more fine-grained IQA application. Code: https://github.com/zhengchen1999/Grounding-IQA.
@article{arxiv.2411.17237,
title = {Grounding-IQA: Grounding Multimodal Language Model for Image Quality Assessment},
author = {Zheng Chen and Xun Zhang and Wenbo Li and Renjing Pei and Fenglong Song and Xiongkuo Min and Xiaohong Liu and Xin Yuan and Yong Guo and Yulun Zhang},
journal= {arXiv preprint arXiv:2411.17237},
year = {2026}
}
Comments
Accepted to ICLR 2026. Code is available at: https://github.com/zhengchen1999/Grounding-IQA