{"url":"/task/image-quality-assessment","name":"Image Quality Assessment","slug":"image-quality-assessment","description_markdown":null,"categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":732,"papers_with_code":318,"benchmarks":3,"benchmark_tables_in_archive":6,"benchmark_tables_shown":6,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":16,"subtasks":7,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/image-quality-assessment-on-msu-nr-vqa","slug":"image-quality-assessment-on-msu-nr-vqa","dataset":"MSU NR VQA Database","dataset_url":"/dataset/msu-video-quality-metrics-benchmark","rows_in_archive":10,"metrics":["SRCC","PLCC","KLCC"],"first_row_in_archive_order":{"model":"UNIQUE","paper_title":"UNIQUE: Unsupervised Image Quality Estimation","paper_url":"/paper/unique-unsupervised-image-quality-estimation","paper_date":"2018-10-15","arxiv_id":"1810.06631","code_links":[{"title":"olivesgatech/UNIQUE-Unsupervised-Image-Quality-Estimation","url":"https://github.com/olivesgatech/UNIQUE-Unsupervised-Image-Quality-Estimation"},{"title":"olivesgatech/UNIQUE-Project-Repository","url":"https://github.com/olivesgatech/UNIQUE-Project-Repository"}],"syntology":null}},{"leaderboard":"/sota/image-quality-assessment-on-msu-fr-vqa","slug":"image-quality-assessment-on-msu-fr-vqa","dataset":"MSU FR VQA Database","dataset_url":"/dataset/msu-video-quality-metrics-dataset","rows_in_archive":6,"metrics":["SRCC"],"first_row_in_archive_order":{"model":"AHIQ","paper_title":"Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network","paper_url":"/paper/attentions-help-cnns-see-better-attention","paper_date":"2022-04-22","arxiv_id":"2204.10485","code_links":[{"title":"iigroup/maniqa","url":"https://github.com/iigroup/maniqa"},{"title":"iigroup/ahiq","url":"https://github.com/iigroup/ahiq"},{"title":"MindSpore-scientific/code-9","url":"https://github.com/MindSpore-scientific/code-9/tree/main/Zero-DCE"}],"syntology":null}},{"leaderboard":"/sota/image-quality-assessment-on-koniq-10k","slug":"image-quality-assessment-on-koniq-10k","dataset":"KonIQ-10k","dataset_url":"/dataset/koniq-10k","rows_in_archive":4,"metrics":["SRCC","PLCC"],"first_row_in_archive_order":{"model":"RealQA","paper_title":"Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model","paper_url":"/paper/next-token-is-enough-realistic-image-quality","paper_date":"2025-03-08","arxiv_id":"2503.06141","code_links":[{"title":"AMAP-ML/RealQA","url":"https://github.com/AMAP-ML/RealQA"}],"syntology":null}},{"leaderboard":null,"slug":"image-quality-assessment-on-kadid-10k","dataset":"KADID-10k","dataset_url":"/dataset/kadid-10k","rows_in_archive":0,"metrics":["PLCC","SRCC"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-quality-assessment-on-kadid10k","dataset":"KADID10K","dataset_url":null,"rows_in_archive":0,"metrics":["SRCC"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"image-quality-assessment-on-spaq","dataset":"SPAQ","dataset_url":"/dataset/spaq","rows_in_archive":0,"metrics":["PLCC","SRCC"],"first_row_in_archive_order":null}],"datasets":[{"url":"/dataset/csiq","name":"CSIQ","full_name":"Categorical Subjective Image Quality","num_papers_in_archive":115},{"url":"/dataset/koniq-10k","name":"KonIQ-10k","full_name":"Konstanz Image Quality 10k Database","num_papers_in_archive":113},{"url":"/dataset/spaq","name":"SPAQ","full_name":"Smartphone Photography Attribute and Quality","num_papers_in_archive":86},{"url":"/dataset/kadid-10k","name":"KADID-10k","full_name":"","num_papers_in_archive":29},{"url":"/dataset/eyeq","name":"EyeQ","full_name":"","num_papers_in_archive":28},{"url":"/dataset/msu-sr-qa-dataset","name":"MSU SR-QA Dataset","full_name":"MSU Super-Resolution Quality Assessment Dataset","num_papers_in_archive":26},{"url":"/dataset/msu-video-quality-metrics-benchmark","name":"MSU NR VQA Database","full_name":"MSU No-Reference Video Quality Assessment Database","num_papers_in_archive":20},{"url":"/dataset/msu-video-quality-metrics-dataset","name":"MSU FR VQA Database","full_name":"MSU Full-Reference Video Quality Assessment Database","num_papers_in_archive":18},{"url":"/dataset/tid2013","name":"TID2013","full_name":"TID2013","num_papers_in_archive":17},{"url":"/dataset/cure-tsr","name":"CURE-TSR","full_name":"CURE Traffic Sign Recognition","num_papers_in_archive":16},{"url":"/dataset/uhd-iqa","name":"UHD-IQA","full_name":"","num_papers_in_archive":9},{"url":"/dataset/piq23","name":"PIQ23","full_name":"","num_papers_in_archive":5},{"url":"/dataset/hephaestus","name":"Hephaestus","full_name":"Hephaestus: A large scale multitask dataset towards InSAR understanding","num_papers_in_archive":3},{"url":"/dataset/cross","name":"CROSS","full_name":"Cross-Reference Omnidirectional Stitching IQA","num_papers_in_archive":1},{"url":"/dataset/fraunhofer-portugal-aicos-edof-dataset","name":"Fraunhofer Portugal AICOS EDoF Dataset","full_name":"Fraunhofer Portugal AICOS EDoF Dataset","num_papers_in_archive":1},{"url":"/dataset/sacid","name":"SACID","full_name":"Saliency Aware Compressed Images Dataset","num_papers_in_archive":1}],"subtasks":[{"url":"/task/aesthetics-quality-assessment","name":"Aesthetics Quality Assessment"},{"url":"/task/document-image-quality-assessment","name":"Document Image Quality Assessment"},{"url":"/task/full-reference-image-quality-assessment","name":"Full reference image quality assessment"},{"url":"/task/full-reference-image-quality-assessment-2","name":"Full-Reference Image Quality Assessment"},{"url":"/task/image-quality-estimation","name":"Image Quality Estimation"},{"url":"/task/no-reference-image-quality-assessment","name":"No-Reference Image Quality Assessment"},{"url":"/task/stereoscopic-image-quality-assessment","name":"Stereoscopic image quality assessment"}],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":318,"tagged_in_all":732,"items":[{"url":"/paper/conditional-image-synthesis-with-auxiliary","title":"Conditional Image Synthesis With Auxiliary Classifier GANs","date":"2016-10-30","arxiv_id":"1610.09585","repositories_listed":37,"syntology":{"n":5,"n_ran":5,"n_unverified":0,"n_pointer_only":5}},{"url":"/paper/the-unreasonable-effectiveness-of-deep","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","date":"2018-01-11","arxiv_id":"1801.03924","repositories_listed":24,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/nima-neural-image-assessment","title":"NIMA: Neural Image Assessment","date":"2017-09-15","arxiv_id":"1709.05424","repositories_listed":12,"syntology":null},{"url":"/paper/ser-fiq-unsupervised-estimation-of-face-image","title":"SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness","date":"2020-03-20","arxiv_id":"2003.09373","repositories_listed":5,"syntology":null},{"url":"/paper/pytorch-image-quality-metrics-for-image","title":"PyTorch Image Quality: Metrics for Image Quality Assessment","date":"2022-08-31","arxiv_id":"2208.14818","repositories_listed":4,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/evaluation-of-retinal-image-quality","title":"Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces","date":"2019-07-10","arxiv_id":"1907.05345","repositories_listed":4,"syntology":null},{"url":"/paper/fetmrqc-an-open-source-machine-learning","title":"FetMRQC: a robust quality control system for multi-centric fetal brain MRI","date":"2023-11-08","arxiv_id":"2311.04780","repositories_listed":3,"syntology":null},{"url":"/paper/fetmrqc-automated-quality-control-for-fetal","title":"FetMRQC: Automated Quality Control for fetal brain MRI","date":"2023-04-12","arxiv_id":"2304.05879","repositories_listed":3,"syntology":null},{"url":"/paper/attentions-help-cnns-see-better-attention","title":"Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network","date":"2022-04-22","arxiv_id":"2204.10485","repositories_listed":3,"syntology":null},{"url":"/paper/an-introduction-to-neural-data-compression","title":"An Introduction to Neural Data Compression","date":"2022-02-14","arxiv_id":"2202.06533","repositories_listed":3,"syntology":{"n":16,"n_ran":0,"n_unverified":16,"n_pointer_only":0}},{"url":"/paper/region-adaptive-deformable-network-for-image","title":"Region-Adaptive Deformable Network for Image Quality Assessment","date":"2021-04-23","arxiv_id":"2104.11599","repositories_listed":3,"syntology":null},{"url":"/paper/learning-to-resize-images-for-computer-vision","title":"Learning to Resize Images for Computer Vision Tasks","date":"2021-03-17","arxiv_id":"2103.09950","repositories_listed":3,"syntology":null},{"url":"/paper/contrastive-explanations-in-neural-networks","title":"Contrastive Explanations in Neural Networks","date":"2020-08-01","arxiv_id":"2008.00178","repositories_listed":3,"syntology":null},{"url":"/paper/image-quality-assessment-guided-deep-neural","title":"Image Quality Assessment Guided Deep Neural Networks Training","date":"2017-08-13","arxiv_id":"1708.03880","repositories_listed":3,"syntology":null},{"url":"/paper/deep-learning-based-compression-detection-for","title":"Deep Learning-based Compression Detection for explainable Face Image Quality Assessment","date":"2025-01-07","arxiv_id":"2501.03619","repositories_listed":2,"syntology":null},{"url":"/paper/a-study-on-the-adequacy-of-common-iqa","title":"A study on the adequacy of common IQA measures for medical images","date":"2024-05-29","arxiv_id":"2405.19224","repositories_listed":2,"syntology":null},{"url":"/paper/a-comprehensive-study-of-multimodal-large","title":"A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment","date":"2024-03-16","arxiv_id":"2403.10854","repositories_listed":2,"syntology":{"n":7,"n_ran":6,"n_unverified":1,"n_pointer_only":7}},{"url":"/paper/depicting-beyond-scores-advancing-image","title":"Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models","date":"2023-12-14","arxiv_id":"2312.08962","repositories_listed":2,"syntology":null},{"url":"/paper/pku-i2iqa-an-image-to-image-quality","title":"PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images","date":"2023-11-27","arxiv_id":"2311.15556","repositories_listed":2,"syntology":null},{"url":"/paper/test-time-adaptation-for-blind-image-quality","title":"Test Time Adaptation for Blind Image Quality Assessment","date":"2023-07-27","arxiv_id":"2307.14735","repositories_listed":2,"syntology":null},{"url":"/paper/deep-learning-techniques-for-blind-image","title":"Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluation","date":"2023-06-15","arxiv_id":"2306.09426","repositories_listed":2,"syntology":null},{"url":"/paper/re-iqa-unsupervised-learning-for-image","title":"Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild","date":"2023-04-02","arxiv_id":"2304.00451","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/qmrnet-quality-metric-regression-for-eo-image","title":"QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution","date":"2022-10-12","arxiv_id":"2210.06618","repositories_listed":2,"syntology":null},{"url":"/paper/empirical-study-of-quality-image-assessment","title":"Empirical Study of Quality Image Assessment for Synthesis of Fetal Head Ultrasound Imaging with DCGANs","date":"2022-06-01","arxiv_id":"2206.01731","repositories_listed":2,"syntology":null},{"url":"/paper/maniqa-multi-dimension-attention-network-for","title":"MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment","date":"2022-04-19","arxiv_id":"2204.08958","repositories_listed":2,"syntology":{"n":9,"n_ran":2,"n_unverified":7,"n_pointer_only":0}},{"url":"/paper/which-generative-adversarial-network-yields","title":"Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalization","date":"2022-03-25","arxiv_id":"2203.13856","repositories_listed":2,"syntology":null},{"url":"/paper/image-quality-assessment-using-contrastive","title":"Image Quality Assessment using Contrastive Learning","date":"2021-10-25","arxiv_id":"2110.13266","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_unverified":1,"n_pointer_only":3}},{"url":"/paper/illumination-aware-image-quality-assessment","title":"Illumination-Aware Image Quality Assessment for Enhanced Low-light Image","date":"2021-10-22","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/blindly-assess-quality-of-in-the-wild-videos","title":"Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception","date":"2021-08-19","arxiv_id":"2108.08505","repositories_listed":2,"syntology":null},{"url":"/paper/musiq-multi-scale-image-quality-transformer","title":"MUSIQ: Multi-scale Image Quality Transformer","date":"2021-08-12","arxiv_id":"2108.05997","repositories_listed":2,"syntology":{"n":6,"n_ran":5,"n_unverified":1,"n_pointer_only":6}}],"syntology_records":9,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}