{"url":"/dataset/csiq","name":"CSIQ","full_name":"Categorical Subjective Image Quality","description_markdown":"The CSIQ database consists of 30 original images, each is distorted using six different types of distortions at four to five different levels of distortion. CSIQ images are subjectively rated base on a linear displacement of the images across four calibrated LCD monitors placed side by side with equal viewing distance to the observer. The database contains 5000 subjective ratings from 35 different observers, and ratings are reported in the form of DMOS.","description_withheld":null,"homepage":"https://s2.smu.edu/~eclarson/csiq.html","introduced_date":"2010-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/most-apparent-distortion-full-reference-image","title":"Most Apparent Distortion: Full-Reference Image Quality Assessment and the Role of Strategy","first_author":"Eric C. Larson","url":null},"license":null,"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"}],"languages":[],"variants":["CSIQ"],"data_loaders":[],"num_papers_in_archive":115,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/no-reference-image-quality-assessment-on-csiq","task":"No-Reference Image Quality Assessment","dataset_variant":"CSIQ","rows":8,"metrics":["SRCC","PLCC"],"first_row_in_archive_order":{"model":"UNIQA","paper":"/paper/uniqa-a-unified-framework-for-both-full","metrics":{"PLCC":"0.970","SRCC":"0.964"},"code_links":[{"title":"barcodereader/yoto","url":"https://github.com/barcodereader/yoto"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"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":"2026-09-25T09:33:49+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":"2026-09-25T09:33:49+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":"2026-09-25T09:33:49+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":"2026-09-25T09:33:49+00:00","samples_harvested":10,"samples_ran":5,"samples_unverified":5,"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":"2026-09-25T09:33:49+00:00","papers_with_samples":4,"samples_harvested":19,"samples_ran":12,"samples_unverified":7,"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."}