Awesome Image Quality Assessment (IQA)

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

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• Papers
  • Task-Oriented IQA
  • Agentic IQA
  • Spatial-Aware IQA
  • Unified IQA
  • Explainable IQA
  • AIGC IQA
  • Image Aesthetic Assessment
  • No Reference (NR)
  • Full Reference (FR)
  • 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.
• Image Quality Assessment Benchmark. Benchmark datasets and evaluation code for IQA.
• Recent Image Quality Related Papers. A collection of recent papers related to image quality.
• Awesome Evaluation of Visual Generation. A curated list of resources for evaluation of visual generation.

Papers

Task-Oriented IQA

Image quality assessment based on downstream task goals and utility

• [Arxiv 2026] Task-Utility IQA: Rethinking Image Quality Assessment in Embodied Tasks, Zha et al. Bibtex
• [Arxiv 2026] Q-CamTuning: A Multimodal Large Language Model for Fine-Grained Quality Reasoning in Livestreaming Camera Tuning, Bi et al. Bibtex

Agentic IQA

Agentic methods that use visual evidence and specialist tools for image and video quality assessment

• [Arxiv 2026] PACE: An Evolutionary Agentic Approach for Open-ended Image Quality Perception, Tang et al. Github | Bibtex
• [CVPR 2026] RL-ScanIQA: Reinforcement-Learned Scanpaths for Blind 360° Image Quality Assessment, Wang et al. Github (coming soon) | Bibtex
• [Arxiv 2025] Q-Router: Agentic Video Quality Assessment with Expert Model Routing and Artifact Localization, Xing et al. | Bibtex
• [ICLR 2026] AgenticIQA: An Agentic Framework for Adaptive and Interpretable Image Quality Assessment, Zhu et al. Project | Bibtex
• [NeurIPS 2026] Q-Probe: Scaling Image Quality Assessment to High Resolution via Context-Aware Agentic Probing, Li et al. | Bibtex
• [NeurIPS 2026] Tool-IQA: Augmenting Image Quality Assessment with Simple Tools, Qin et al. Github (inference code and weights; training pending) | Bibtex
• [ECCV 2026] IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment, Wu et al. Github | Bibtex
• [ECCV 2026] Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning, Liang et al. Github | Bibtex

Spatial-Aware IQA

Quality assessment with spatial context and local structures

• [Arxiv 2026] Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback, Zhang et al. Github | Bibtex
• [ICLR 2026] Grounding-IQA: Grounding Multimodal Language Model for Image Quality Assessment, Chen et al. Github (partial release) | Bibtex
• [Eurographics 2024] Enhancing image quality prediction with self-supervised visual masking, Uğur et al. Github | Project | Bibtex
• [Arxiv 2024] SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning, Chen et al. Github | Bibtex
• [ACM MM 2024 (Oral)] Q-Ground: Image Quality Grounding with Large Multi-modality Models, Chen et al. Bibtex | Github

Unified IQA

All IQA types unified in a single model

• [Arxiv 2025] UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture, et al. Github | Bibtex
• [ECCV 2024] PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts, Chen et al. Github | Bibtex
• [ICML 2024] Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels, Wu et al. Github | Bibtex

Explainable IQA

Human readable IQA, mostly with large language models

• [Arxiv 2026] Of Chains and Gains: What Reinforcement Learning changes on Reasoning-based Image Quality Assessment, Li et al. Bibtex
• ✨[CVPR 2026] Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videos, Saini et al. Github | Bibtex
• ✨[ICLR 2026] Panoptic Pairwise Distortion Graph, Janjua et al. Github | Bibtex
• [Arxiv 2026] MR-IQA: A Unified Margin View of Regression and Ranking for Blind Image Quality Assessment, Li et al. Github | Bibtex
• [Arxiv 2026] Q-Hawkeye: Reliable Visual Policy Optimization for Image Quality Assessment, Xie et al. Github (training code; inference pending) | Bibtex
• [Arxiv 2026] QualiRAG: Retrieval-Augmented Generation for Visual Quality Understanding, Cao et al. Github | Bibtex
• ✨[ICLR 2026 (Oral)] Reasoning as Representation: Rethinking Visual Reinforcement Learning in Image Quality Assessment, Zhao et al. Github | Bibtex
• ✨[NeurIPS 2025 (Spotlight)] VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank, Wu et al. Github | Bibtex
• [ICLR 2026] Self-Evolving Vision-Language Models for Image Quality Assessment via Voting and Ranking, Wen et al. Github | Bibtex
• [Arxiv 2025] Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model, Li et al. Github | Bibtex
• [Arxiv 2025] Q-Insight: Understanding Image Quality via Visual Reinforcement Learning, Li et al. Github | Bibtex
• [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
• [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. Github | Project | Bibtex
• [TIP 2025] Enhancing Descriptive Image Quality Assessment with A Large-scale Multi-modal Dataset, You et al. Github | Project | Bibtex
• [CVPR 2024] Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models, Wu et al. Github | Bibtex

AIGC IQA

• [Website 2026] ImageBench V1: Text-to-Image Benchmark with VLM Judges, imagebench.ai. Project | Methodology
• [Arxiv 2026] ELIQ: A Label-Free Framework for Quality Assessment of Evolving AI-Generated Images, Li et al. Bibtex
• [Arxiv 2026] EduVQA: Benchmarking AI-Generated Video Quality Assessment for Education, Chen et al. Bibtex
• ✨[CVPR 2024 (Best Paper)] Rich Human Feedback for Text-to-Image Generation, Liang et al. Github | Bibtex
• [ICLR 2024 (Spotlight)] Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision, Wu et al. Github | Bibtex
• [ICCV 2023] TIFA: Accurate and Interpretable 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
• [ICCV 2023] 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
• [TCSVT 2024] AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment, Li et al. Github | Bibtex

Image Aesthetic Assessment

• [Arxiv 2026] CropReward: A Human-Preference Benchmark and Reward Model for Aesthetic Image Cropping, Sheng et al. Bibtex
• [Arxiv 2026] Moonworks Lunara: Modeling Artistic Intelligence, Wang et al. Dataset | Bibtex
• [CVPR 2026] Venus: Benchmarking and Empowering Multimodal Large Language Models for Aesthetic Guidance and Cropping, Du et al. Github | Bibtex
• [CVPR 2026 (Oral)] Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks, Yang et al. Github | Bibtex
• [AAAI 2026] Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment, Liu et al. Github | Bibtex
• [Arxiv 2026] VideoAesBench: Benchmarking the Video Aesthetics Perception Capabilities of Large Multimodal Models, Li et al. Github | Bibtex
• [Arxiv 2026] Aesthetic Camera Viewpoint Suggestion with 3D Aesthetic Field, Tang et al. Bibtex
• [CVPR 2026] ArtiMuse: Fine-Grained Image Aesthetics Assessment with Joint Scoring and Expert-Level Understanding, Cao et al. Project | Github | Bibtex
• [CVPR 2025] Charm: The Missing Piece in ViT Fine-Tuning for Image Aesthetic Assessment, Behrad et al. Github | Bibtex
• [ACM MM 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
• [ECCV 2024] Scaling Up Personalized Image Aesthetic Assessment via Task Vector Customization, Yun et al. Bibtex | Project
• [CVPR 2023] VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining, Ke et al. Bibtex
• [CVPR 2023] Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method, Yi et al. Github | Bibtex

No Reference (NR)

• [Arxiv 2026] MetaRefine: Score Refinement with Multi-Level Features for Image Assessment, Xu et al. | Original-resolution geometry and multi-level feature score refinement | Bibtex
• [AAAI 2026] Fine-grained Image Quality Assessment for Perceptual Image Restoration, Sheng et al. Bibtex | HomePage | Github
• [Arxiv 2026] Decoupling Perception and Calibration: Label-Efficient Image Quality Assessment Framework, Li et al. Bibtex
• [Arxiv 2026] SIQA: Toward Reliable Scientific Image Quality Assessment, Li et al. Bibtex
• [ICML 2025] QMamba: On First Exploration of Vision Mamba for Image Quality Assessment, Guan et al. Github | Bibtex
• [NeurIPS 2024] Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare, Zhu et al. Project | Bibtex
• [Arxiv 2024] Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment, Agnolucci et al. Github | Bibtex
• [ACCV 2024] ATTIQA: Generalizable Image Quality Feature Extractor using Attribute-aware Pretraining, Kwon et al. Bibtex
• [CVPR 2024] Blind Image Quality Assessment Based On Geometric Order Learning, Shin 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. Github | Bibtex
• [WACV 2024 (Oral)] ARNIQA: Learning Distortion Manifold for Image Quality Assessment, Agnolucci et al. Github | Bibtex
• [TIP 2023] TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment, Chen et al. Github | Bibtex
• [ICCV 2023] Test Time Adaptation for Blind Image Quality Assessment, Roy et al. Github | Bibtex
• [CVPR 2023] Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild, Saha et al. Bibtex | Github
• [CVPR 2023] Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective, Zhang et al. Github | Bibtex
• [CVPR 2023] 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
• [AAAI 2023] 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
• [CVPRW 2022] MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment, NR. Github | Transformer, multi-dimension attention, dual branch
• [WACV 2022] No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency, TReS, NR. Github | Transformer, relative ranking, self-consistency
• [BMVC 2021] KonIQ++: Boosting No-Reference Image Quality Assessment in the Wild by Jointly Predicting Image Quality and Defects, NR. Github | Multi-task with distortion prediction
• [ICCV 2021] MUSIQ: Multi-scale Image Quality Transformer, NR. Github / Github | Multi-scale, transformer, Aspect Ratio Preserved (ARP) resizing
• [ICCV 2021] Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment, CKDN, NR. Github | Degraded reference, Conditional knowledge distillation (related to HIQA)
• [CVPR 2020] Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network, HyperIQA, NR. Github | Content-aware hyper network
• [CVPR 2020] MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment, NR. Github | Meta-learning
• [ECCV 2020] GIQA: Generated Image Quality Assessment, NR. Github | Generated image
• [PIRM Challenge 2018] The 2018 PIRM Challenge on Perceptual Image Super-resolution, PI, NR. Github | 1/2 * (NIQE + (10 - NRQM)).
• [CVPR 2018] Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning, HIQA, NR. Project | Hallucinated reference
• [CVPR 2018] Blind Predicting Similar Quality Map for Image Quality Assessment, BPSQM, NR. | Pixel-wise quality map
• [ICCV 2017] RankIQA: Learning from Rankings for No-reference Image Quality Assessment, NR. Github | Pretrain on synthetically ranked data
• [CVPR 2014] Convolutional Neural Networks for No-Reference Image Quality Assessment, CNNIQA, NR. Github | First CNN-based NR-IQA
• [TIP 2021] Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild, UNIQUE, NR. Github | Combine synthetic and authentic image pairs
• [TCSVT 2020] Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network, DBCNN, NR. Github | Two branches for synthetic and authentic distortions
• [TMM 2019] Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?, SFA, NR. Github | Aggregate ResNet50 features of multiple cropped patches
• [TIP 2019] A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction, PQR, NR/Aesthetic. Github/Github | Unify different type of aesthetic labels
• [TIP 2018] Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment, WaDIQaM, NR/FR. Github | Weighted average of patch qualities, shared FR/NR models
• [TIP 2018] NIMA: Neural Image Assessment, NR. Github/Github | Squared EMD loss
• [TIP 2017] End-to-End Blind Image Quality Assessment Using Deep Neural Networks, MEON, NR. | Multi-task: distortion learning and quality prediction
• [TIP 2017] dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs, NR. Download | Similar to RankIQA
• [CVIU 2017] Learning a No-Reference Quality Metric for Single-Image Super-Resolution (NRQM), NR. Project | Traditional, Super resolution
• [JoV 2017] Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach, FRIQUEE, NR. Github | Authentically Distorted, Bag of Features
• [TIP 2016] Blind Image Quality Assessment Based on High Order Statistics Aggregation, HOSA, NR. Matlab download | Traditional
• [TIP 2015] A Feature-Enriched Completely Blind Image Quality Evaluator, ILNIQE, NR. Project | Traditional
• [TIP 2012] No-reference Image Quality Assessment in the Spatial Domain, BRISQUE, NR. Github | Traditional
• [TIP 2012] Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain, BLIINDS-II, NR. Github
• [CVPR 2012] Unsupervised Feature Learning Framework for No-reference Image Quality Assessment, CORNIA, NR. Github | Codebook Representation
• [SPL 2012] Making a “Completely Blind” Image Quality Analyzer, NIQE, NR. Github | Traditional
• [TIP 2011] Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality, DIIVINE, NR. Github

Full Reference (FR)

• [Arxiv 2026] JLD: Perceptual Distance Through A Jacobian Lens, Saini et al., FR. Frozen vision encoder with a Jacobian lens. Github | Project | 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
• [CVPR 2022 NTIRE workshop] Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network, AHIQ, FR. Github | Attention, Transformer
• [CVPR 2022] Incorporating Semi-Supervised and Positive-Unlabeled Learning for Boosting Full Reference Image Quality Assessment, JSPL, FR. Github | semi-supervised and positive-unlabeled (PU) learning
• [AAAI 2022] Content-Variant Reference Image Quality Assessment via Knowledge Distillation, CVRKD, NAR. Github | Non-Aligned content reference, knowledge distillation
• [CVPRW 2021] Perceptual Image Quality Assessment with Transformers, IQT, FR. Github | Transformer
• [ACMM 2021] Locally Adaptive Structure and Texture Similarity for Image Quality Assessment, A-DISTS, FR. Github
• [TPAMI 2021] Image Quality Assessment: Unifying Structure and Texture Similarity, DISTS, FR. Github
• [CVPR 2018] The Unreasonable Effectiveness of Deep Features as a Perceptual Metric, LPIPS, FR. Project | Perceptual similarity, Pairwise Preference
• [CVPR 2018] PieAPP: Perceptual Image-Error Assessment through Pairwise Preference, FR. Project | Perceptual similarity, Pairwise Preference
• [TIP 2018] Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment, WaDIQaM, NR/FR. Github
• [TCSVT 2020] A Novel Just-Noticeable-Difference-based Saliency-Channel Attention Residual Network for Full-Reference Image Quality Predictions, JND-SalCAR, FR. | JND (Just-Noticeable-Difference)
• [TIP 2019] Visual Quality Assessment for Super-resolved Images: Database and Method, QADS, FR. Project | Super-resolution
• [TIP 2011] FSIM: A Feature Similarity Index for Image Quality Assessment, FR. Project | Traditional
• [TIP 2005] An information fidelity criterion for image quality assessment using natural scene statistics (IFC), FR. | Traditional
• [TIP 2006] Image Information and Visual Quality (VIF), FR. Project | Traditional
• [FR] Multiscale Structural Similarity for Image Quality Assessment (MS-SSIM) Project | Traditional
• [TIP 2004] Image Quality Assessment: From Error Visibility to Structural Similarity, SSIM, FR. Project | Traditional

Others

Image Intrinsic Scale Assessment (IISA)

Aims to predict the Image Intrinsic Scale, i.e. the scale at which an image shows the best quality

• [ICCV 2025] Image Intrinsic Scale Assessment: Bridging the Gap Between Quality and Resolution, Hosu et al. Bibtex | Github

Color IQA

• [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

Face IQA

• [CVPR 2024] DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer, Chen et al. Bibtex | Project

360° Image (Omnidirectional Image) IQA

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

Egocentric Spatial Images (Apple Vision Pro)

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

Adversarial Attack on IQA

• [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

IQA Losses

• [ACMM2020] Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment Github | Norm-in-Norm Loss

Datasets

IQA datasets

Paper Link	Dataset Name	Type	Published	Website	Images	Annotations
arXiv / Bibtex	Moonworks Lunara Aesthetic II	Aesthetic	arXiv2026	Dataset	2,854 anchor–variation pairs	—
arXiv / Bibtex	Moonworks Lunara Aesthetic I	Aesthetic	arXiv2026	Dataset	2,000	Human-refined prompts and structured annotations
arXiv	FGRestore	NR	AAAI2026	Project	18,408(30,886 pairs)	45,318
arXiv	Beyond8Bits	NR	CVPR2026	Project	44k videos from 6.5k sources	1.5M+ ratings
arXiv	GameScope	NR	ICIP2026	Project	4,048 gaming videos (H.264/H.265/AV1, UGC + PGC)	~37 MOS per video + quality attributes
arXiv	UHD-IQA	NR	ECCVW2024	Project	6k (~3840x2160)	20 ratings per image
arXiv	PaQ-2-PiQ	NR	CVPR2020	Official github	40k, 120k patches	4M
CVF	SPAQ	NR	CVPR2020	Offical github	11k (smartphone)
arXiv	KonIQ-10k	NR	TIP2020	Project	10k from YFCC100M	1.2M
arXiv	AADB	NR/Aesthentic	ECCV2016	Official github	10k images (8500/500/1000), 11 attributes
arXiv	CLIVE	NR	TIP2016	Project	1200	350k
pdf	AVA	NR / Aesthentic	CVPR2012	Github/Project	250k (60 categories)
arXiv	PIPAL	FR	ECCV2020	Project	250	1.13M
arXiv	KADIS-700k	FR	arXiv	Project	140k pristine / 700k distorted	30 ratings (DCRs) per image.
IEEE	KADID-10k	FR	QoMEX2019	Project	81	10k distortions
pdf	Waterloo-Exp	FR	TIP2017	Project	4744	94k distortions
pdf	MDID	FR	PR2017	---	20	1600 distortions
pdf	TID2013	FR	SP2015	Project	25	3000 distortions
pdf	LIVEMD	FR	ACSSC2012	Project	15 pristine images	two successive distortions
pdf	CSIQ	FR	Journal of Electronic Imaging 2010	---	30	866 distortions
pdf	TID2008	FR	2009	Project	25	1700 distortions
pdf	LIVE IQA	FR	TIP2006	Project	29 images, 780 synthetic distortions
link	IVC	FR	2005	---	10	185 distortions

Perceptual similarity datasets

Paper Title	Dataset Name	Type	Published	Website	Images	Annotations
arXiv	BAPPS(LPIPS)	FR	CVPR2018	Project	187.7k	484k
arXiv	PieAPP	FR	CVPR2018	Project	200 images	2.3M
arXiv	Panda	FR	ICLR2026	Project	2200 images	>500k