{"url":"/dataset/live-fb-lsvq","name":"LIVE-FB LSVQ","full_name":"LIVE-FB Large-Scale Social Video Quality (LSVQ) Database","description_markdown":"No-reference (NR) perceptual video quality assessment (VQA) is a complex, unsolved, and important problem to social and streaming media applications. Efficient and accurate video quality predictors are needed to monitor and guide the processing of billions of shared, often imperfect, user-generated content (UGC). Unfortunately, current NR models are limited in their prediction capabilities on real-world, \"in-the-wild\" UGC video data. To advance progress on this problem, we created the largest (by far) subjective video quality dataset, containing 39, 000 real-world distorted videos and 117, 000 space-time localized video patches (\"v-patches\"), and 5.5M human perceptual quality annotations. Using this, we created two unique NR-VQA models: (a) a local-to-global region-based NR VQA architecture (called PVQ) that learns to predict global video quality and achieves state-of-the-art performance on 3 UGC datasets, and (b) a first-of-a-kind space-time video quality mapping engine (called PVQ Mapper) that helps localize and visualize perceptual distortions in space and time. We will make the new database and prediction models available immediately following the review process.","description_withheld":null,"homepage":"https://github.com/baidut/PatchVQ","introduced_date":"2020-11-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/patch-vq-patching-up-the-video-quality","title":"Patch-VQ: 'Patching Up' the Video Quality Problem","first_author":"Zhenqiang Ying","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Video Quality Assessment","url":"/task/video-quality-assessment","datasets_with_task":"/datasets/task/video-quality-assessment"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LIVE-FB LSVQ"],"data_loaders":[{"repo":"https://github.com/Yapwei/Dataset","url":"https://github.com/Yapwei/Dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-quality-assessment-on-live-fb-lsvq","task":"Video Quality Assessment","dataset_variant":"LIVE-FB LSVQ","rows":13,"metrics":["PLCC"],"first_row_in_archive_order":{"model":"OneAlign + FAST-VQA","paper":"/paper/q-align-teaching-lmms-for-visual-scoring-via","metrics":{"PLCC":"0.900"},"code_links":[{"title":"q-future/q-align","url":"https://github.com/q-future/q-align"}]},"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":2,"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/disentangling-aesthetic-and-technical-effects","title":"Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives","date":"2022-11-09","rows_on_this_dataset":1,"code_links":3,"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/neighbourhood-representative-sampling-for","title":"Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment","date":"2022-10-11","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/hvs-revisited-a-comprehensive-video-quality","title":"HVS Revisited: A Comprehensive Video Quality Assessment Framework","date":"2022-10-09","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fast-vqa-efficient-end-to-end-video-quality","title":"FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling","date":"2022-07-06","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/conviqt-contrastive-video-quality-estimator","title":"CONVIQT: Contrastive Video Quality Estimator","date":"2022-06-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/discovqa-temporal-distortion-content","title":"DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment","date":"2022-06-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-deep-learning-based-no-reference-quality","title":"A Deep Learning based No-reference Quality Assessment Model for UGC Videos","date":"2022-04-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/image-quality-assessment-using-contrastive","title":"Image Quality Assessment using Contrastive Learning","date":"2021-10-25","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":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/blindly-assess-quality-of-in-the-wild-videos","title":"Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception","date":"2021-08-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/patch-vq-patching-up-the-video-quality","title":"Patch-VQ: 'Patching Up' the Video Quality Problem","date":"2020-11-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ugc-vqa-benchmarking-blind-video-quality","title":"UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content","date":"2020-05-29","rows_on_this_dataset":1,"code_links":5,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":13,"samples_ran":9,"samples_unverified":4,"pointer_only_for_licence":13,"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."}