Multi-Granularity Reasoning for Image Quality Assessment via Attribute-Aware Reinforcement Learning to Rank

2026-04-07

AI Summary

This work addresses the limitation of existing image quality assessment (IQA) methods, which typically predict only a single scalar score while neglecting the multidimensional attributes—such as sharpness, color fidelity, noise, and composition—that underpin human perception. To overcome this, the authors propose MG-IQA, a novel framework that jointly predicts overall quality and fine-grained perceptual attributes in a single inference pass. MG-IQA integrates vision-language models with reinforcement learning–based ranking, leveraging attribute-aware prompts, a multidimensional Thurstone reward model, and a cross-domain alignment mechanism to enable interpretable, multi-granularity evaluation without requiring perceptual scale realignment. Experiments demonstrate that MG-IQA consistently outperforms state-of-the-art methods across eight IQA benchmarks, achieving an average 2.1% improvement in SRCC for overall quality prediction and generating explanations highly aligned with human judgments.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Recent advances in reasoning-induced image quality assessment (IQA) have demonstrated the power of reinforcement learning to rank (RL2R) for training vision-language models (VLMs) to assess perceptual quality. However, existing approaches operate at a single granularity, predicting only an overall quality score, while overlooking the multi-dimensional nature of human quality perception, which encompasses attributes such as sharpness, color fidelity, noise level, and compositional aesthetics. In this paper, we propose MG-IQA (Multi-Granularity IQA), a multi-granularity reasoning framework that extends RL2R to jointly assess overall image quality and fine-grained quality attributes within a single inference pass. Our approach introduces three key innovations: (1) an attribute-aware prompting strategy that elicits structured multi-attribute reasoning from VLMs; (2) a multi-dimensional Thurstone reward model that computes attribute-specific fidelity rewards for group relative policy optimization; and (3) a cross-domain alignment mechanism that enables stable joint training across synthetic distortion, authentic distortion, and AI-generated image datasets without perceptual scale re-alignment. Extensive experiments on eight IQA benchmarks demonstrate that MG-IQA consistently outperforms state-of-the-art methods in both overall quality prediction (average SRCC improvement of 2.1\%) and attribute-level assessment, while generating interpretable, human-aligned quality descriptions.

Problem

• Image Quality Assessment
• Multi-Granularity Reasoning
• Quality Attributes
• Vision-Language Models
• Perceptual Quality

Innovation

• multi-granularity reasoning
• attribute-aware reinforcement learning
• vision-language models
• Thurstone reward model
• cross-domain alignment