{"url":"/dataset/msu-video-quality-metrics-dataset","name":"MSU FR VQA Database","full_name":"MSU Full-Reference Video Quality Assessment Database","description_markdown":"The dataset was created for video quality assessment problem. It was formed with 36 clips from Vimeo, which were selected from 18,000+ open-source clips with high bitrate (license CCBY or CC0).\r\n\r\nThe clips include videos recorded by both professionals and amateurs. Almost half of the videos contain scene changes and high dynamism. Moreover, the synthetic to natural lightning ratio is approximately 1 to 3.\r\n\r\nContent type: nature, sport, humans close up, gameplays, music videos, water stream or steam, CGI\r\nEffects and distortions: shaking, slow-motion, grain/noisy, too dark/bright regions, macro shooting, captions (text), extraneous objects on the camera lens or just close to it\r\nResolution: 1920x1080 as the most popular modern video resolution (more in the future)\r\nFormat: yuv420p\r\nFPS: 24, 25, 30, 39, 50, 60\r\nVideos duration: mainly 10 seconds\r\nSuch content diversity helps simulate near-realistic conditions. The choice of videos collected for the benchmark dataset employed clustering in terms of space-time complexity to obtain a representative distribution.\r\n\r\nFor compression we used 40 codecs of 10 compression standards (H.264, AV1, H.265, VVC, etc.). Each video was compressed with 3 target bitrates: 1,000 Kbps, 2,000 Kbps, and 4,000 Kbps, and different real-life encoding modes: constant quality (CRF) and variable bitrate (VBR). The choice of bitrate range simplifies the subjective comparison procedure since the video quality is more difficult to distinguish visually at higher bitrates.\r\n\r\nThe subjective assessment involved pairwise comparisons using crowdsourcing service Subjectify.us. To increase the relevance of the results, each pair of videos received at least 10 responses from participants. In total, 766362 valid answers were collected from more than 10800 unique participants.","description_withheld":null,"homepage":"https://videoprocessing.ai/benchmarks/video-quality-metrics.html","introduced_date":"2022-11-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/video-compression-dataset-and-benchmark-of","title":"Video compression dataset and benchmark of learning-based video-quality metrics","first_author":"Anastasia Antsiferova","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Image Quality Assessment","url":"/task/image-quality-assessment","datasets_with_task":"/datasets/task/image-quality-assessment"},{"name":"Video Quality Assessment","url":"/task/video-quality-assessment","datasets_with_task":"/datasets/task/video-quality-assessment"}],"languages":[],"variants":["MSU FR VQA 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/video-quality-assessment-on-msu-video-quality-1","task":"Video Quality Assessment","dataset_variant":"MSU FR VQA Database","rows":20,"metrics":["SRCC","PLCC","KLCC"],"first_row_in_archive_order":{"model":"VMAF Y (v061)","paper":"/paper/toward-a-practical-perceptual-video-quality","metrics":{"KLCC":"0.809","PLCC":"0.952","SRCC":"0.942"},"code_links":[{"title":"Netflix/vmaf","url":"https://github.com/Netflix/vmaf"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-quality-assessment-on-msu-fr-vqa","task":"Image Quality Assessment","dataset_variant":"MSU FR VQA Database","rows":6,"metrics":["SRCC"],"first_row_in_archive_order":{"model":"AHIQ","paper":"/paper/attentions-help-cnns-see-better-attention","metrics":{"SRCC":"0.937"},"code_links":[{"title":"iigroup/maniqa","url":"https://github.com/iigroup/maniqa"},{"title":"iigroup/ahiq","url":"https://github.com/iigroup/ahiq"},{"title":"MindSpore-scientific/code-9","url":"https://github.com/MindSpore-scientific/code-9/tree/main/Zero-DCE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/attentions-help-cnns-see-better-attention","title":"Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network","date":"2022-04-22","rows_on_this_dataset":2,"code_links":3,"syntology":null},{"paper":"/paper/deep-learning-based-full-reference-and-no","title":"Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos","date":"2021-06-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fovvideovdp-a-visible-difference-predictor","title":"FovVideoVDP: A visible difference predictor for wide field-of-view video","date":"2021-04-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/st-greed-space-time-generalized-entropic","title":"ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction","date":"2020-10-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/the-unreasonable-effectiveness-of-deep","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","date":"2018-01-11","rows_on_this_dataset":1,"code_links":24,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gradient-magnitude-similarity-deviation-on","title":"Gradient magnitude similarity deviation on multiple scales for color image quality assessment","date":"2017-06-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mean-deviation-similarity-index-efficient-and","title":"Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator","date":"2016-08-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/a-haar-wavelet-based-perceptual-similarity","title":"A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment","date":"2016-07-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/toward-a-practical-perceptual-video-quality","title":"Toward A Practical Perceptual Video Quality Metric","date":"2016-06-06","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/image-quality-assessment-based-on-dct-subband","title":"Image quality assessment based on DCT subband similarity","date":"2015-12-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/vsi-a-visual-saliency-induced-index-for","title":"VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment","date":"2014-08-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/gradient-magnitude-similarity-deviation-a","title":"Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index","date":"2013-08-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/sr-sim-a-fast-and-high-performance-iqa-index","title":"SR-SIM: A fast and high performance IQA index based on spectral residual","date":"2012-09-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fsim-a-feature-similarity-index-for-image","title":"FSIM: A Feature Similarity Index for Image Quality Assessment","date":"2011-01-31","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/image-information-and-visual-quality","title":"Image information and visual quality","date":"2006-02-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/multiscale-structural-similarity-for-image","title":"Multiscale structural similarity for image quality assessment","date":"2004-05-04","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/image-quality-assessment-from-error","title":"Image quality assessment: from error visibility to structural similarity","date":"2004-04-13","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":2,"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."}