{"url":"/dataset/konvid-1k","name":"KoNViD-1k","full_name":"KoNViD-1k VQA Database","description_markdown":"Subjective video quality assessment (VQA) strongly depends on semantics, context, and the types of visual distortions. A lot of existing VQA databases cover small numbers of video sequences with artificial distortions. When testing newly developed Quality of Experience (QoE) models and metrics, they are commonly evaluated against subjective data from such databases, that are the result of perception experiments. However, since the aim of these QoE models is to accurately predict natural videos, these artificially distorted video databases are an insufficient basis for learning. Additionally, the small sizes make them only marginally usable for state-of-the-art learning systems, such as deep learning. In order to give a better basis for development and evaluation of objective VQA methods, we have created a larger datasets of natural, real-world video sequences with corresponding subjective mean opinion scores (MOS) gathered through crowdsourcing.\r\n​\r\nWe took YFCC100m as a baseline database, consisting of 793436 Creative Commons (CC) video sequences, filtered them through multiple steps to ensure that the video sequences are representative of the whole spectrum of available video content, types of distortions, and subjective quality. The resulting 1200 videos are available to download, alongside the subjective data and evaluation of the best-performing techniques available for multiple video attributes. Namely, we have evaluated blur, colorfulness, contrast, spatial information, temporal information and video quality.","description_withheld":null,"homepage":"http://database.mmsp-kn.de/konvid-1k-database.html","introduced_date":"2017-05-10","introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons","url":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":["KoNViD-1k"],"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-konvid-1k","task":"Video Quality Assessment","dataset_variant":"KoNViD-1k","rows":21,"metrics":["PLCC"],"first_row_in_archive_order":{"model":"DOVER (end-to-end)","paper":"/paper/disentangling-aesthetic-and-technical-effects","metrics":{"PLCC":"0.905"},"code_links":[{"title":"vqassessment/dover","url":"https://github.com/vqassessment/dover"},{"title":"QualityAssessment/DOVER","url":"https://github.com/QualityAssessment/DOVER"},{"title":"VQAssessment/FAST-VQA-and-FasterVQA","url":"https://github.com/VQAssessment/FAST-VQA-and-FasterVQA"}]},"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/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/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."}