{"url":"/dataset/uhd-iqa","name":"UHD-IQA","full_name":null,"description_markdown":"We introduce a novel Image Quality Assessment (IQA) dataset comprising 6073 UHD-1 (4K) images, annotated at a fixed width of 3840 pixels. Contrary to existing No-Reference (NR) IQA datasets, ours focuses on highly aesthetic photos of high technical quality, filling a gap in the literature. The images, carefully curated to exclude synthetic content, are sufficiently diverse to train general NR-IQA models. Importantly, the dataset is annotated with perceptual quality ratings obtained through a crowdsourcing study. Ten expert raters, comprising photographers and graphics artists, assessed each image at least twice in multiple sessions spanning several days, resulting in highly reliable labels. Annotators were rigorously selected based on several metrics, including self-consistency, to ensure their reliability. The dataset includes rich metadata with user and machine-generated tags from over 5,000 categories and popularity indicators such as favorites, likes, downloads, and views. With its unique characteristics, such as its focus on high-quality images, reliable crowdsourced annotations, and high annotation resolution, our dataset opens up new opportunities for advancing perceptual image quality assessment research and developing practical NRIQA models that apply to modern photos.","description_withheld":null,"homepage":"https://database.mmsp-kn.de/uhd-iqa-benchmark-database.html","introduced_date":"2024-06-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/uhd-iqa-benchmark-database-pushing-the","title":"UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment","first_author":"Vlad Hosu","url":null},"license":{"name":"CC0","url":"https://creativecommons.org/public-domain/cc0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"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"},{"name":"NR-IQA","url":"/task/nr-iqa","datasets_with_task":"/datasets/task/nr-iqa"},{"name":"Blind Image Quality Assessment","url":"/task/blind-image-quality-assessment","datasets_with_task":"/datasets/task/blind-image-quality-assessment"},{"name":"4k","url":"/task/4k","datasets_with_task":"/datasets/task/4k"}],"languages":[],"variants":["UHD-IQA"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/no-reference-image-quality-assessment-on-uhd","task":"No-Reference Image Quality Assessment","dataset_variant":"UHD-IQA","rows":7,"metrics":["SRCC","PLCC"],"first_row_in_archive_order":{"model":"LAR-IQA (KAN head)","paper":"/paper/lar-iqa-a-lightweight-accurate-and-robust-no","metrics":{"PLCC":"0.786","SRCC":"0.836"},"code_links":[{"title":"nasimjamshidi/lar-iqa","url":"https://github.com/nasimjamshidi/lar-iqa"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/lar-iqa-a-lightweight-accurate-and-robust-no","title":"LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment Model","date":"2024-08-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/quality-aware-image-text-alignment-for-real","title":"Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment","date":"2024-03-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/konx-cross-resolution-image-quality","title":"KonX: Cross-Resolution Image Quality Assessment","date":"2022-12-12","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/exploring-clip-for-assessing-the-look-and","title":"Exploring CLIP for Assessing the Look and Feel of Images","date":"2022-07-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/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}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":13,"samples_ran":10,"samples_unverified":3,"pointer_only_for_licence":8,"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."}