MSU HDR Video Reconstruction Benchmark 2022: HLG
The most comprehensive comparison of HDR video reconstruction methods
Nikolay Safonov

Key features of the Benchmark
- Comparison of 14 methods of HDR video reconstruction
- A new private dataset for testing. 20 different scenes: fireworks, flowers, soccer and others
- 10 metrics for restoration quality assessment
- HDR video player for self-assessment of quality
- (Soon) Subjective comparison
- Split comparison of video restoration with two most popular gamma curves: PQ (soon), HLG
Leaderboard
Click on the labels to sort the table.
In the methodology you can read brief information
about all metrics.
You can scroll the table to see all the results.
Rank | Model | HDR-VDP-3 | HDR-VQM | HDR-PSNR | HDR-SSIM | PQ-NIQE | PQ-PSNR | PQ-SSIM | PQ-VMAF | Shifted HDR-PSNR | FovVideoVDP |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | Kuo | 8.6776 | 0.0579 | 40.7642 | 0.9954 | 5.0287 | 34.9679 | 0.9862 | 94.0270 | 47.4611 | 9.8880 |
2 | Maxon | 8.2153 | 0.0622 | 45.6478 | 0.9949 | 4.9400 | 35.1188 | 0.9846 | 89.9478 | 45.3267 | 9.8374 |
3 | DeepHDR | 8.0572 | 0.1000 | 33.5267 | 0.9907 | 5.0832 | 21.9155 | 0.9584 | 89.5382 | 37.0040 | 9.3205 |
4 | HDRTV | 6.9813 | 0.1296 | 35.9721 | 0.9918 | 5.4744 | 22.4636 | 0.9555 | 85.6109 | 26.8880 | 9.8788 |
5 | KovOliv | 7.4724 | 0.1329 | 32.4223 | 0.9937 | 4.8269 | 20.1296 | 0.9389 | 91.3117 | 28.9170 | 9.8861 |
6 | twostageHDR | 6.6004 | 0.1350 | 31.6717 | 0.9864 | 7.7687 | 22.4022 | 0.9139 | 46.1830 | TBP | TBP |
7 | HuoPhys | 7.0618 | 0.1518 | 32.6570 | 0.9946 | 5.0754 | 18.7458 | 0.9383 | 90.0419 | 22.1553 | 9.8866 |
8 | HDRUNet | 6.6857 | 0.1830 | 34.9894 | 0.9845 | 6.7712 | 24.7347 | 0.9388 | 92.1209 | TBP | TBP |
9 | HDRCNN | 6.3880 | 0.1919 | 33.0200 | 0.9663 | 4.9391 | 21.2604 | 0.8539 | 50.5081 | 35.0804 | 9.8778 |
10 | ExpNet | 7.5910 | 0.1942 | 34.0555 | 0.9892 | 5.2161 | 24.2994 | 0.9321 | 61.6722 | 40.8574 | 9.8887 |
11 | Huo | 8.9004 | 0.2103 | 30.0900 | 0.9720 | 4.9126 | 23.3886 | 0.9527 | 82.4653 | 44.4314 | 9.2408 |
12 | KUnet | 6.1543 | 0.2126 | 32.5082 | 0.9838 | 7.3623 | 24.1380 | 0.9236 | 77.6478 | TBP | TBP |
13 | Akyuz | 6.4114 | 0.2521 | 28.1420 | 0.9917 | 4.7872 | 14.6789 | 0.8535 | 71.5814 | 23.3069 | 9.8799 |
14 | SingleHDR | 8.4180 | 0.2630 | 34.2872 | 0.9845 | 5.5764 | 25.8042 | 0.9606 | 70.6792 | 42.5944 | 9.8305 |
TBP* – to be published
Charts
In this section you can observe the values of different metrics for each individual video and the average values. In the "Video" selector, you can observe the names of the videos. For your convenience, the Dataset tab has a preview of each video and its full name match.
Metric: Video:

Results
In this section, you can quickly evaluate the quality of the algorithms yourself. The video player below show you the HDR video if your device supports this technology. We recommend opening this video via Google Chrome or Safari.
Method:
Visualization
This section presents a visualization of the result of each of the algorithms.
- The first line is a preview
- The second line is a tonemapped crop
- The third and fourth lines are the two exposures, which show in detail the differences in the dark and bright areas
Please note that we do not show GT exposures, as this information makes it possible to obtain the HDR version of the GT video.
Video:
Model 1: Model 2: Model 3:
Drag a red rectangle in the area, which you want to crop.
GT



GT

HDRTVNet

HDRCNN

Your method submission
Verify the restoration ability of your HDR Video Reconstruction algorithm and compare it with state-of-the-art solutions. You can see information about all other participants here.
1. Download input data
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Download SDR videos |
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2. Apply your algorithm |
Convert SDR videos to HDR using your algorithm. You can also send us the code of your method or the executable file and we will run it ourselves. |
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3. Send us result |
Send us an email to itm-benchmark@videoprocessing.ai
with the following information:
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Contacts
We would highly appreciate any suggestions and ideas on how to improve our benchmark. For questions and propositions, please contact us: itm-benchmark@videoprocessing.ai
Also you can subscribe to updates on our benchmark:
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MSU Benchmark Collection
- MSU Video Upscalers Benchmark 2022
- MSU Video Deblurring Benchmark 2022
- MSU Video Frame Interpolation Benchmark 2022
- MSU HDR Video Reconstruction Benchmark 2022
- MSU Super-Resolution for Video Compression Benchmark 2022
- MSU No-Reference Video Quality Metrics Benchmark 2022
- MSU Full-Reference Video Quality Metrics Benchmark 2022
- MSU Video Alignment and Retrieval Benchmark
- MSU Mobile Video Codecs Benchmark 2021
- MSU Video Super-Resolution Benchmark
- MSU Shot Boundary Detection Benchmark 2020
- MSU Deinterlacer Benchmark
- The VideoMatting Project
- Video Completion
- Codecs Comparisons & Optimization
- VQMT
- MSU Datasets Collection
- Metrics Research
- Video Quality Measurement Tool 3D
- Video Filters
- Other Projects