A comprehensive collection of IQA papers, datasets and codes. We also provide PyTorch implementations of mainstream metrics in IQA-PyTorch

• Awesome Image Quality Assessment (IQA)
  • Papers
    • Explainable IQA
    • AIGC IQA
    • No Reference (NR)
    • Full Reference (FR)
    • Image Aesthetic Assessment
    • Others
  • Datasets
    • IQA datasets
    • Perceptual similarity datasets

Related Resources:

• Awesome Image Aesthetic Assessment and Cropping. A curated list of resources including papers, datasets, and relevant links to aesthetic evaluation and cropping.

All IQA types unified in a single model

• [ECCV 2024] PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts, Chen et al. Bibtex
• [ICML 2024] Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels, Wu et al. Github | Bibtex

Human readable IQA, mostly with large language models

• [TPAMI 2024] Q-Bench+: A Benchmark for Multi-modal Foundation Models on Low-level Vision from Single Images to Pairs, Zhang et al. Github | Bibtex
• [ACM MM2024] Q-Ground: Image Quality Grounding with Large Multi-modality Models, Chen et al. Bibtex | Github
• [Arxiv 2024] VisualCritic: Making LMMs Perceive Visual Quality Like Humans, Huang et al. Bibtex
• [ECCV 2024] A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment, Wu et al. Github | Bibtex
• [ECCV 2024] Towards Open-ended Visual Quality Comparison, Wu et al. Github | Bibtex
• [ECCV 2024] Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models, You et al. Project | Bibtex
• [CVPR 2024] Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models, Wu et al. Github | Bibtex

• [ICLR 2024] Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision, Wu et al. Github | Bibtex
• [ICCV 2023] TIFA: Text-to-Image Faithfulness Evaluation with Question Answering, Hu et al. Github | Bibtex | Project
• [NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences for Text-to-image Generation, Xu et al. Github | Bibtex
• [ICCV2023] Better Aligning Text-to-Image Models with Human Preference, Wu et al. Github | Github(HPSv2) | Bibtex
• [NeurIPS 2023] Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation, Yuval et al. Github | Bibtex
• [TCSVT2023] A Fine-grained Subjective Perception & Alignment Database for AI Generated Image Quality Assessment, Li et al. Github | Bibtex

• [Arxiv 2024] Q-Mamba: On First Exploration of Vision Mamba for Image Quality Assessment, Guan et al. Bibtex

• [Arxiv 2024] Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare, Zhe et al. Bibtex

• [Arxiv 2024] Quality-aware Image-Text Alignment for Real-World Image Quality Assessment, Agnolucci et al. Github | Bibtex

• [CVPR 2024] Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment, Li et al. Bibtex

• [CVPR 2024] Boosting Image Quality Assessment through Efficient Transformer Adaptation with Local Feature Enhancement , Xu et al. | Bibtex

• [WACV2024] ARNIQA: Learning Distortion Manifold for Image Quality Assessment, Agnolucci et al. Github | Bibtex

• [TIP2023] TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment, Chen et al. Github | Bibtex

• [ICCV2023] Test Time Adaptation for Blind Image Quality Assessment, Roy et al. Github | Bibtex

• [CVPR2023] Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild, Saha et al. Bibtex | Github

• [CVPR2023] Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective, Zhang et al. Github | Bibtex

• [CVPR2023] Quality-aware Pre-trained Models for Blind Image Quality Assessment, Zhao et al. Bibtex

• [AAAI2023] Exploring CLIP for Assessing the Look and Feel of Images, Wang et al. Github | Bibtex

• [AAAI2023] Data-Efficient Image Quality Assessment with Attention-Panel Decoder, Qin et al. Github | Bibtex

• [TPAMI2022] Continual Learning for Blind Image Quality Assessment , Zhang et al. Github | Bibtex

• [TIP2022] No-Reference Image Quality Assessment by Hallucinating Pristine Features, Chen et al. Github | Bibtex

• [TIP2022] VCRNet: Visual Compensation Restoration Network for No-Reference Image Quality Assessment, Pan et al. Github | Bibtex

• [TMM2022] GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment, Sun et al. Github | Bibtex

• [CVPR2021] Troubleshooting Blind Image Quality Models in the Wild, Wang et al. Github | Bibtex

• [ECCV2022] Shift-tolerant Perceptual Similarity Metric, Ghildyal et al. Github | Bibtex
• [BMVC2022] Content-Diverse Comparisons improve IQA, Thong et al. Bibtex
• [ACM MM2022] Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity, Zhou et al. Github | Bibtex

• [ACMMM 2024] AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception, Huang et al. Project | Github | Bibtex
• [Arxiv 2024] AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception, Huang et al. Github | Bibtex
• [ECCV2024] Scaling Up Personalized Aesthetic Assessment via Task Vector Arithmetic, Yun et al. Bibtex | Project
• [CVPR2023] VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining, Ke et al. Bibtex
• [CVPR2023] Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method, Yi et al. Github | Bibtex

• [ECCV 2024] Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures, He et al. Github | Bibtex
• [CVPR 2023] Learning a Deep Color Difference Metric for Photographic Images, Chen et al. Github | Bibtex

• [CVPR2024] DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer, Chen et al. Bibtex | Project

• [NeurIPS 2023] Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment , Wu et al. Bibtex | Github

• [Arxiv 2024] ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images, Zhu et al. Bibtex | Github

• [Arxiv 2024] Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics, Gushchin et al. Bibtex | Github | Project
• [NeurIPS 2022] Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop, Zhang et al. Bibtex | Github