{"url":"/dataset/live1","name":"LIVE (Public-Domain Subjective Image Quality Database)","full_name":null,"description_markdown":"The **LIVE Public-Domain Subjective Image Quality Database** is a resource developed by the Laboratory for Image and Video Engineering at the University of Texas at Austin. It contains a set of images and videos whose quality has been ranked by human subjects. This database is used in Quality Assessment (QA) research, which aims to make quality predictions that align with the subjective opinions of human observers.\r\n\r\nThe database was created through an extensive experiment conducted in collaboration with the Department of Psychology at the University of Texas at Austin. The experiment involved obtaining scores from human subjects for many images distorted with different distortion types. The QA algorithm may be trained on part of this data set, and tested on the rest.\r\n\r\nThe database is available to the research community free of charge. If you use these images in your research, the creators kindly ask that you reference their website and their papers. There are two releases of the database. Release 2 includes more distortion types and more subjects than Release 1. The distortions include JPEG-compressed images, JPEG2000-compressed images, Gaussian blur, and white noise.","description_withheld":null,"homepage":"https://live.ece.utexas.edu/research/quality/subjective.htm","introduced_date":null,"introduced_date_note":null,"introduced_by":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":["LIVE1 (Quality 40 Grayscale)","LIVE1 (Quality 30 Grayscale)","LIVE1 (Quality 30 Color)","LIVE1 (Quality 20 Grayscale)","LIVE1 (Quality 20 Color)","Live1 (Quality 10 Grayscale)","LIVE1 (Quality 10 Color)","LIVE (Public-Domain Subjective Image Quality Database)"],"data_loaders":[],"num_papers_in_archive":18,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-10-1","task":"JPEG Artifact Correction","dataset_variant":"Live1 (Quality 10 Grayscale)","rows":13,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"29.94","SSIM":"0.8359"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-20-1","task":"JPEG Artifact Correction","dataset_variant":"LIVE1 (Quality 20 Grayscale)","rows":12,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"32.31","SSIM":"0.8938"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-10","task":"JPEG Artifact Correction","dataset_variant":"LIVE1 (Quality 10 Color)","rows":9,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"28.36","SSIM":"0.818"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-20","task":"JPEG Artifact Correction","dataset_variant":"LIVE1 (Quality 20 Color)","rows":9,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"30.66","SSIM":"0.8797"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-30-1","task":"JPEG Artifact Correction","dataset_variant":"LIVE1 (Quality 30 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":"33.73","SSIM":"0.9223"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-40","task":"JPEG Artifact Correction","dataset_variant":"LIVE1 (Quality 40 Grayscale)","rows":5,"metrics":["PSNR","SSIM"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"34.71","SSIM":"0.9347"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/jpeg-artifact-correction-on-live1-quality-30","task":"JPEG Artifact Correction","dataset_variant":"LIVE1 (Quality 30 Color)","rows":3,"metrics":["PSNR","PSNR-B","SSIM"],"first_row_in_archive_order":{"model":"Hi-IR","paper":"/paper/hierarchical-information-flow-for-generalized","metrics":{"PSNR":"32.02","SSIM":"0.9063"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hierarchical-information-flow-for-generalized","title":"Hierarchical Information Flow for Generalized Efficient Image Restoration","date":"2024-11-27","rows_on_this_dataset":7,"code_links":0,"syntology":null},{"paper":"/paper/one-size-fits-all-hypernetwork-for-tunable","title":"Hypernetwork-Based Adaptive Image Restoration","date":"2022-06-13","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-flexible-blind-jpeg-artifacts-removal","title":"Towards Flexible Blind JPEG Artifacts Removal","date":"2021-09-29","rows_on_this_dataset":7,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":9,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/quantization-guided-jpeg-artifact-correction","title":"Quantization Guided JPEG Artifact Correction","date":"2020-04-17","rows_on_this_dataset":6,"code_links":1,"syntology":null},{"paper":"/paper/residual-dense-network-for-image-restoration","title":"Residual Dense Network for Image Restoration","date":"2018-12-25","rows_on_this_dataset":4,"code_links":3,"syntology":null},{"paper":"/paper/s-net-a-scalable-convolutional-neural-network","title":"S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction","date":"2018-10-18","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/implicit-dual-domain-convolutional-network","title":"Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction","date":"2018-10-18","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/dpw-sdnet-dual-pixel-wavelet-domain-deep-cnns","title":"DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images","date":"2018-05-27","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/multi-level-wavelet-cnn-for-image-restoration","title":"Multi-level Wavelet-CNN for Image Restoration","date":"2018-05-18","rows_on_this_dataset":6,"code_links":5,"syntology":null},{"paper":"/paper/memnet-a-persistent-memory-network-for-image","title":"MemNet: A Persistent Memory Network for Image Restoration","date":"2017-08-07","rows_on_this_dataset":4,"code_links":2,"syntology":null},{"paper":"/paper/beyond-a-gaussian-denoiser-residual-learning","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","date":"2016-08-13","rows_on_this_dataset":4,"code_links":22,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":1,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/image-restoration-using-convolutional-auto","title":"Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections","date":"2016-06-29","rows_on_this_dataset":2,"code_links":17,"syntology":null},{"paper":"/paper/compression-artifacts-reduction-by-a-deep","title":"Compression Artifacts Reduction by a Deep Convolutional Network","date":"2015-04-27","rows_on_this_dataset":4,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":30,"samples_ran":11,"samples_unverified":19,"pointer_only_for_licence":1,"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."}