{"url":"/dataset/live-vqc","name":"LIVE-VQC","full_name":"LIVE Video Quality Challenge (VQC) Database","description_markdown":"The great variations of videographic skills in videography, camera designs, compression and processing protocols, communication and bandwidth environments, and displays leads to an enormous variety of video impairments. Current no-reference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain very limited content, fixed resolutions, were captured using a small number of camera devices by a few videographers and have been subjected to a modest number of distortions. As such, these databases fail to adequately represent real world videos, which contain very different kinds of content obtained under highly diverse imaging conditions and are subject to authentic, complex and often commingled distortions that are difficult or impossible to simulate. As a result, NR video quality predictors tested on real-world video data often perform poorly. Towards advancing NR video quality prediction, we have constructed a large-scale video quality assessment database containing 585 videos of unique content , captured using 101 different devices (43 device models) by 80 different users with wide ranges of levels of complex, authentic distortions. We collected a large number of subjective video quality scores via crowdsourcing. A total of 4776 unique participants took part in the study, yielding more than 205000 opinion scores , resulting in an average of 240 recorded human opinions per video . This study is the largest video quality assessment study ever conducted along several key dimensions: number of unique contents, capture devices, distortion types and combinations of distortions, study participants, and recorded subjective scores.","description_withheld":null,"homepage":"https://live.ece.utexas.edu/research/LIVEVQC/index.html","introduced_date":"2018-03-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/large-scale-study-of-perceptual-video-quality","title":"Large-Scale Study of Perceptual Video Quality","first_author":"Zeina Sinno","url":null},"license":null,"modalities":[],"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-VQC"],"data_loaders":[],"num_papers_in_archive":68,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-quality-assessment-on-live-vqc","task":"Video Quality Assessment","dataset_variant":"LIVE-VQC","rows":20,"metrics":["PLCC"],"first_row_in_archive_order":{"model":"ReLaX-VQA (finetuned on LIVE-VQC)","paper":"/paper/relax-vqa-residual-fragment-and-layer-stack","metrics":{"PLCC":"0.8876"},"code_links":[{"title":"xinyiw915/relax-vqa","url":"https://github.com/xinyiw915/relax-vqa"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/relax-vqa-residual-fragment-and-layer-stack","title":"ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment","date":"2024-07-16","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"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":2,"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/2bivqa-double-bi-lstm-based-video-quality","title":"2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos","date":"2022-08-31","rows_on_this_dataset":1,"code_links":1,"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":2,"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/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/chipqa-no-reference-video-quality-prediction","title":"ChipQA: No-Reference Video Quality Prediction via Space-Time Chips","date":"2021-09-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/rapique-rapid-and-accurate-video-quality","title":"RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content","date":"2021-01-26","rows_on_this_dataset":1,"code_links":1,"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},{"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/two-level-approach-for-no-reference-consumer","title":"Two-Level Approach for No-Reference Consumer Video Quality Assessment","date":"2019-06-20","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":14,"samples_ran":9,"samples_unverified":5,"pointer_only_for_licence":11,"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."}