{"url":"/dataset/classic5","name":"Classic5","full_name":null,"description_markdown":"Five classic grayscale images commonly used for image quality assessment tasks.","description_withheld":null,"homepage":"https://github.com/cszn/DnCNN/tree/master/testsets/classic5","introduced_date":"2006-09-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/pointwise-shape-adaptive-dct-for-high-quality","title":"Pointwise shape-adaptive DCT for high-quality deblocking of compressed color images","first_author":"Alessandro Foi","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"JPEG Artifact Correction","url":"/task/jpeg-artifact-correction","datasets_with_task":"/datasets/task/jpeg-artifact-correction"}],"languages":[],"variants":["Classic5 (Quality 40 Grayscale)","Classic5 (Quality 30 Grayscale)","Classic5 (Quality 20 Grayscale)","Classic5 (Quality 10 Grayscale)","Classic5"],"data_loaders":[{"repo":"https://github.com/cszn/DnCNN","url":"https://github.com/cszn/DnCNN","frameworks":["pytorch"]}],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/jpeg-artifact-correction-on-classic5-quality","task":"JPEG Artifact Correction","dataset_variant":"Classic5 (Quality 10 Grayscale)","rows":7,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"30.38","SSIM":"0.8266"},"code_links":[]},"note":"rows are the archive's own order at snapshot; 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