{"url":"/dataset/pipal-perceptual-iqa-dataset","name":"PIPAL","full_name":"Perceptual Image Processing ALgorithms IQA Dataset","description_markdown":"PIPAL training set contains 200 reference images, 40 distortion types, 23k distortion images, and more than one million human ratings. Especially, we include GAN-based algorithms’ outputs as a new GAN-based distortion type. We employ the Elo rating system to assign the Mean Opinion Scores (MOS).","description_withheld":null,"homepage":"https://www.jasongt.com/projectpages/pipal.html","introduced_date":"2020-07-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/pipal-a-large-scale-image-quality-assessment","title":"PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration","first_author":"Jinjin Gu","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[],"languages":[],"variants":["PIPAL","PIPAL NTIRE2021 Validation Testset"],"data_loaders":[],"num_papers_in_archive":37,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}