{"url":"/dataset/msu-sr-qa-dataset","name":"MSU SR-QA Dataset","full_name":"MSU Super-Resolution Quality Assessment Dataset","description_markdown":"Our dataset was made of videos from MSU Video Upscalers Benchmark Dataset, MSU Video Super-Resolution Benchmark Dataset and MSU Super-Resolution for Video Compression Benchmark Dataset. Dataset consists of real videos (were filmed with 2 cameras), video games footages, movies, cartoons, dynamic ads. \r\n\r\nHow we brought our dataset closer to completeness?\r\n* The dataset covers a large number of use cases in the field of SR due to the large number of content types\r\n* The dataset contains videos with completely different resolutions, FPS values: 8, 24, 25, 30, 60, as well as high and low spatio-temporal complexity\r\n* Distorted videos were obtained using 46 SR methods, some of them were preprocessed with 5 codecs: aomenc, vvenc, x264, x265, uavs3es with different bitrates and qp values\r\n* The dataset was manually checked for redundancy\r\n\r\nVideos from benchmarks are FullHD video crops, since the subjective comparison was made on crops. Therefore, the resolution of all videos in the received dataset is low.","description_withheld":null,"homepage":"https://videoprocessing.ai/benchmarks/super-resolution-metrics.html#home","introduced_date":"2024-03-04","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Super-Resolution","url":"/task/super-resolution","datasets_with_task":"/datasets/task/super-resolution"},{"name":"Image Super-Resolution","url":"/task/image-super-resolution","datasets_with_task":"/datasets/task/image-super-resolution"},{"name":"Image Quality Assessment","url":"/task/image-quality-assessment","datasets_with_task":"/datasets/task/image-quality-assessment"},{"name":"Video Super-Resolution","url":"/task/video-super-resolution","datasets_with_task":"/datasets/task/video-super-resolution"},{"name":"Video Quality Assessment","url":"/task/video-quality-assessment","datasets_with_task":"/datasets/task/video-quality-assessment"}],"languages":[],"variants":["MSU SR-QA Dataset"],"data_loaders":[],"num_papers_in_archive":26,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-quality-assessment-on-msu-sr-qa-dataset","task":"Video Quality Assessment","dataset_variant":"MSU SR-QA Dataset","rows":60,"metrics":["SROCC","PLCC","KLCC","Type"],"first_row_in_archive_order":{"model":"PieAPP","paper":"/paper/pieapp-perceptual-image-error-assessment","metrics":{"KLCC":"0.61945","PLCC":"0.75743","SROCC":"0.75215","Type":"FR"},"code_links":[{"title":"prashnani/PerceptualImageError","url":"https://github.com/prashnani/PerceptualImageError"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/q-align-teaching-lmms-for-visual-scoring-via","title":"Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels","date":"2023-12-28","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/topiq-a-top-down-approach-from-semantics-to","title":"TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment","date":"2023-08-06","rows_on_this_dataset":8,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":10,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/vila-learning-image-aesthetics-from-user","title":"VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining","date":"2023-03-24","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/shift-tolerant-perceptual-similarity-metric-1","title":"Shift-tolerant Perceptual Similarity Metric","date":"2022-07-27","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":7,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/exploring-clip-for-assessing-the-look-and","title":"Exploring CLIP for Assessing the Look and Feel of Images","date":"2022-07-25","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/maniqa-multi-dimension-attention-network-for","title":"MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment","date":"2022-04-19","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/erqa-edge-restoration-quality-assessment-for","title":"ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution","date":"2021-10-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/locally-adaptive-structure-and-texture","title":"Locally Adaptive Structure and Texture Similarity for Image Quality Assessment","date":"2021-10-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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":3,"code_links":1,"syntology":null},{"paper":"/paper/musiq-multi-scale-image-quality-transformer","title":"MUSIQ: Multi-scale Image Quality Transformer","date":"2021-08-12","rows_on_this_dataset":4,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":5,"samples_unverified":1,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unified-quality-assessment-of-in-the-wild","title":"Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training","date":"2020-11-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/norm-in-norm-loss-with-faster-convergence-and","title":"Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment","date":"2020-08-10","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/image-quality-assessment-unifying-structure","title":"Image Quality Assessment: Unifying Structure and Texture Similarity","date":"2020-04-16","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/from-patches-to-pictures-paq-2-piq-mapping","title":"From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality","date":"2019-12-20","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/quality-assessment-of-in-the-wild-videos","title":"Quality Assessment of In-the-Wild Videos","date":"2019-08-01","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":4,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/the-2018-pirm-challenge-on-perceptual-image","title":"The 2018 PIRM Challenge on Perceptual Image Super-resolution","date":"2018-09-20","rows_on_this_dataset":1,"code_links":8,"syntology":null},{"paper":"/paper/pieapp-perceptual-image-error-assessment","title":"PieAPP: Perceptual Image-Error Assessment through Pairwise Preference","date":"2018-06-06","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":2,"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/nima-neural-image-assessment","title":"NIMA: Neural Image Assessment","date":"2017-09-15","rows_on_this_dataset":1,"code_links":12,"syntology":null},{"paper":"/paper/learning-a-no-reference-quality-metric-for","title":"Learning a No-Reference Quality Metric for Single-Image Super-Resolution","date":"2016-12-18","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"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},{"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":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":4,"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":10,"samples_harvested":68,"samples_ran":30,"samples_unverified":38,"pointer_only_for_licence":21,"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."}