# GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment

arXiv: https://arxiv.org/abs/2103.07666 · HTML: https://mlsanity.com/paper/2103.07666/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2021.

## Built on by (most cited first, 30 of 43)

- [Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distribution](https://mlsanity.com/paper/2501.11561/) (2025)
- [Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models](https://mlsanity.com/paper/2312.08962/) (2023)
- [Q-Insight: Understanding Image Quality via Visual Reinforcement Learning](https://mlsanity.com/paper/2503.22679/) (2025)
- [KVQ: Kwai Video Quality Assessment for Short-form Videos](https://mlsanity.com/paper/2402.07220/) (2024)
- [Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Dataset](https://mlsanity.com/paper/2405.18842/) (2024)
- [Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model](https://mlsanity.com/paper/2207.04904/) (2022)
- [DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer](https://mlsanity.com/paper/2406.09622/) (2024)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild](https://mlsanity.com/paper/2405.19996/) (2024)
- [VisualCritic: Making LMMs Perceive Visual Quality Like Humans](https://mlsanity.com/paper/2403.12806/) (2024)
- [Exploring Rich Subjective Quality Information for Image Quality Assessment in the Wild](https://mlsanity.com/paper/2409.05540/) (2024)
- [Grounding-IQA: Grounding Multimodal Language Model for Image Quality Assessment](https://mlsanity.com/paper/2411.17237/) (2024)
- [Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics](https://mlsanity.com/paper/2405.18790/) (2024)
- [Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment](https://mlsanity.com/paper/2105.09143/) (2021)
- [StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment](https://mlsanity.com/paper/2207.14489/) (2022)
- [Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection](https://mlsanity.com/paper/2301.07681/) (2023)
- [Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling](https://mlsanity.com/paper/2208.14603/) (2022)
- [REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effect](https://mlsanity.com/paper/2209.01760/) (2022)
- [No-Reference Point Cloud Quality Assessment via Graph Convolutional Network](https://mlsanity.com/paper/2411.07728/) (2024)
- [Perceptual Visual Quality Assessment: Principles, Methods, and Future Directions](https://mlsanity.com/paper/2503.00625/) (2025)
- [UARE: A Unified Vision-Language Model for Image Quality Assessment, Restoration, and Enhancement](https://mlsanity.com/paper/2512.06750/) (2025)
- [Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling](https://mlsanity.com/paper/2501.07087/) (2025)
- [Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment](https://mlsanity.com/paper/2209.05321/) (2022)
- [MLlm-DR: Towards Explainable Depression Recognition with MultiModal Large Language Models](https://mlsanity.com/paper/2507.05591/) (2025)
- [Regression-free Blind Image Quality Assessment with Content-Distortion Consistency](https://mlsanity.com/paper/2307.09279/) (2023)
- [M3-AGIQA: Multimodal, Multi-Round, Multi-Aspect AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2502.15167/) (2025)
- [DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality Assessment](https://mlsanity.com/paper/2505.24002/) (2025)
- [Plug In, Grade Right: Psychology-Inspired AGIQA](https://mlsanity.com/paper/2512.22780/) (2025)
- [How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practices](https://mlsanity.com/paper/2603.05010/) (2026)
- [Automated Assessment of Transthoracic Echocardiogram Image Quality Using Deep Neural Networks](https://mlsanity.com/paper/2209.00976/) (2022)

## Abstract (arXiv)

A good distortion representation is crucial for the success of deep blind image quality assessment (BIQA). However, most previous methods do not effectively model the relationship between distortions or the distribution of samples with the same distortion type but different distortion levels. In this work, we start from the analysis of the relationship between perceptual image quality and distortion-related factors, such as distortion types and levels. Then, we propose a Distortion Graph Representation (DGR) learning framework for IQA, named GraphIQA, in which each distortion is represented as a graph, i.e., DGR. One can distinguish distortion types by learning the contrast relationship between these different DGRs, and infer the ranking distribution of samples from different levels in a DGR. Specifically, we develop two sub-networks to learn the DGRs: a) Type Discrimination Network (TDN) that aims to embed DGR into a compact code for better discriminating distortion types and learning the relationship between types; b) Fuzzy Prediction Network (FPN) that aims to extract the distributional characteristics of the samples in a DGR and predicts fuzzy degrees based on a Gaussian prior. Experiments show that our GraphIQA achieves the state-of-the-art performance on many benchmark datasets of both synthetic and authentic distortions.