Enhancing Image Quality Assessment Ability of LMMs via Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2601.08311v1
• Date: Tue, 13 Jan 2026 08:00:02 GMT
• Title: Enhancing Image Quality Assessment Ability of LMMs via Retrieval-Augmented Generation
• Authors: Kang Fu, Huiyu Duan, Zicheng Zhang, Yucheng Zhu, Jun Zhao, Xiongkuo Min, Jia Wang, Guangtao Zhai,
• Abstract summary: Large Multimodal Models (LMMs) have recently shown remarkable promise in low-level visual perception tasks.<n>We introduce IQARAG, a training-free framework that enhances LMMs' Image Quality Assessment (IQA) ability.<n>IQARAG leverages Retrieval-Augmented Generation (RAG) to retrieve some semantically similar but quality-variant reference images with corresponding Mean Opinion Scores (MOSs) for input image.
• Score: 102.10193318526137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Multimodal Models (LMMs) have recently shown remarkable promise in low-level visual perception tasks, particularly in Image Quality Assessment (IQA), demonstrating strong zero-shot capability. However, achieving state-of-the-art performance often requires computationally expensive fine-tuning methods, which aim to align the distribution of quality-related token in output with image quality levels. Inspired by recent training-free works for LMM, we introduce IQARAG, a novel, training-free framework that enhances LMMs' IQA ability. IQARAG leverages Retrieval-Augmented Generation (RAG) to retrieve some semantically similar but quality-variant reference images with corresponding Mean Opinion Scores (MOSs) for input image. These retrieved images and input image are integrated into a specific prompt. Retrieved images provide the LMM with a visual perception anchor for IQA task. IQARAG contains three key phases: Retrieval Feature Extraction, Image Retrieval, and Integration & Quality Score Generation. Extensive experiments across multiple diverse IQA datasets, including KADID, KonIQ, LIVE Challenge, and SPAQ, demonstrate that the proposed IQARAG effectively boosts the IQA performance of LMMs, offering a resource-efficient alternative to fine-tuning for quality assessment.

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