{"url":"/dataset/msu-hdr-video-reconstruction-benchmark","name":"MSU HDR Video Reconstruction Benchmark","full_name":null,"description_markdown":"This is a dataset for a video inverse-tone-mapping task. The dataset contains various contents for the task of restoring HDR video: fireworks, flowers, football, night city, scenes with reflections. Videos have different brightness ranges and contain different types of lighting. The camera for shooting the dataset captures 14 stops of the dynamic range.","description_withheld":null,"homepage":"https://videoprocessing.ai/benchmarks/inverse-tone-mapping.html","introduced_date":"2022-05-10","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Inverse-Tone-Mapping","url":"/task/inverse-tone-mapping-1","datasets_with_task":"/datasets/task/inverse-tone-mapping-1"},{"name":"HDR Reconstruction","url":"/task/hdr-reconstruction","datasets_with_task":"/datasets/task/hdr-reconstruction"}],"languages":[],"variants":["MSU HDR Video Reconstruction Benchmark"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/inverse-tone-mapping-on-msu-hdr-video","task":"Inverse-Tone-Mapping","dataset_variant":"MSU HDR Video Reconstruction Benchmark","rows":9,"metrics":["HDR-PSNR","HDR-SSIM","HDR-VQM"],"first_row_in_archive_order":{"model":"HDRTVNet","paper":"/paper/a-new-journey-from-sdrtv-to-hdrtv","metrics":{"HDR-PSNR":"35.9721","HDR-SSIM":"0.9918","HDR-VQM":"0.1296"},"code_links":[{"title":"chxy95/hdrtvnet","url":"https://github.com/chxy95/hdrtvnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learning-a-practical-sdr-to-hdrtv-up","title":"Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models","date":"2023-03-23","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/kunet-imaging-knowledge-inspired-single-hdr","title":"KUNet: Imaging Knowledge-Inspired Single HDR Image Reconstruction","date":"2022-07-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-new-journey-from-sdrtv-to-hdrtv","title":"A New Journey from SDRTV to HDRTV","date":"2021-08-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hdrunet-single-image-hdr-reconstruction-with","title":"HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization","date":"2021-05-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-two-stage-deep-network-for-high-dynamic","title":"A Two-stage Deep Network for High Dynamic Range Image Reconstruction","date":"2021-04-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/single-image-hdr-reconstruction-by-learning","title":"Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline","date":"2020-04-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/expandnet-a-deep-convolutional-neural-network","title":"ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content","date":"2018-03-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hdr-image-reconstruction-from-a-single","title":"HDR image reconstruction from a single exposure using deep CNNs","date":"2017-10-20","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":2,"samples_harvested":10,"samples_ran":5,"samples_unverified":5,"pointer_only_for_licence":7,"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."}