{"url":"/dataset/kadid-10k","name":"KADID-10k","full_name":null,"description_markdown":"Konstanz artificially distorted image quality database (KADID-10k) contains 81 pristine images, each degraded by 25 distortions in 5 levels.","description_withheld":null,"homepage":"http://database.mmsp-kn.de/kadid-10k-database.html","introduced_date":"2020-01-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/deepfl-iqa-weak-supervision-for-deep-iqa","title":"DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning","first_author":"Hanhe Lin","url":null},"license":null,"modalities":[],"tasks":[{"name":"Image Quality Assessment","url":"/task/image-quality-assessment","datasets_with_task":"/datasets/task/image-quality-assessment"},{"name":"No-Reference Image Quality Assessment","url":"/task/no-reference-image-quality-assessment","datasets_with_task":"/datasets/task/no-reference-image-quality-assessment"}],"languages":[],"variants":["KADID-10k"],"data_loaders":[],"num_papers_in_archive":29,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/no-reference-image-quality-assessment-on-1","task":"No-Reference Image Quality Assessment","dataset_variant":"KADID-10k","rows":9,"metrics":["SRCC","PLCC"],"first_row_in_archive_order":{"model":"RvTC (image-only)","paper":"/paper/language-integration-in-fine-tuning","metrics":{"PLCC":"0.98","SRCC":"0.98"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-quality-assessment-on-kadid-10k","task":"Image Quality Assessment","dataset_variant":"KADID-10k","rows":0,"metrics":["PLCC","SRCC"],"first_row_in_archive_order":null,"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/language-integration-in-fine-tuning","title":"Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression","date":"2025-07-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/arniqa-learning-distortion-manifold-for-image","title":"ARNIQA: Learning Distortion Manifold for Image Quality Assessment","date":"2023-10-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":5,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/uniqa-a-unified-framework-for-both-full","title":"You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment","date":"2023-10-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/re-iqa-unsupervised-learning-for-image","title":"Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild","date":"2023-04-02","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/image-quality-assessment-using-contrastive","title":"Image Quality Assessment using Contrastive Learning","date":"2021-10-25","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/no-reference-image-quality-assessment-via-1","title":"No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency","date":"2021-08-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/blindly-assess-image-quality-in-the-wild","title":"Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network","date":"2020-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/blind-image-quality-assessment-using-a-deep","title":"Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network","date":"2019-07-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":4,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/no-reference-image-quality-assessment-in-the","title":"No-Reference Image Quality Assessment in the Spatial Domain","date":"2012-08-17","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":19,"samples_ran":11,"samples_unverified":8,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}