{"url":"/dataset/koniq-10k","name":"KonIQ-10k","full_name":"Konstanz Image Quality 10k Database","description_markdown":"**KonIQ-10k** is a large-scale IQA dataset consisting of 10,073 quality scored images. This is the first in-the-wild database aiming for ecological validity, with regard to the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models.","description_withheld":null,"homepage":"http://database.mmsp-kn.de/koniq-10k-database.html","introduced_date":"2019-10-14","introduced_date_note":null,"introduced_by":{"paper":"/paper/koniq-10k-an-ecologically-valid-database-for","title":"KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment","first_author":"Vlad Hosu","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":["KonIQ-10k"],"data_loaders":[],"num_papers_in_archive":113,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-quality-assessment-on-koniq-10k","task":"Image Quality Assessment","dataset_variant":"KonIQ-10k","rows":4,"metrics":["SRCC","PLCC"],"first_row_in_archive_order":{"model":"RealQA","paper":"/paper/next-token-is-enough-realistic-image-quality","metrics":{"PLCC":"0.959","SRCC":"0.948"},"code_links":[{"title":"AMAP-ML/RealQA","url":"https://github.com/AMAP-ML/RealQA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/no-reference-image-quality-assessment-on-2","task":"No-Reference Image Quality Assessment","dataset_variant":"KonIQ-10k","rows":1,"metrics":["PLCC","SRCC"],"first_row_in_archive_order":{"model":"RvTC (image-only)","paper":"/paper/language-integration-in-fine-tuning","metrics":{"PLCC":"0.95","SRCC":"0.94"},"code_links":[]},"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/next-token-is-enough-realistic-image-quality","title":"Next Token Is Enough: Realistic Image Quality and Aesthetic Scoring with Multimodal Large Language Model","date":"2025-03-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/q-align-teaching-lmms-for-visual-scoring-via","title":"Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels","date":"2023-12-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"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/koniq-10k-towards-an-ecologically-valid-and","title":"KonIQ-10k: Towards an ecologically valid and large-scale IQA database","date":"2018-03-22","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":0,"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."}