Dataset of MSU HDR Video Reconstruction Benchmark 2022: HLG
In this section you can observe the frames from the videos included in the dataset. All images were processed by tonemapper
We used a Fujifilm XT-4 camera for filming
Here we provide the key characteristics of Dataset.
SDR* – tonemapped HDR video
We shot 100 different scenes to create the dataset. The video selection process for the final version of the dataset can be divided into several steps:
- Manual selection. At this step, we have selected visually different scenes, so that the objects, the amount of movement and the type of lighting are different.
- Feature calculation. We consider the following features:
- After calculating the features, we selected videos to get an even distribution.
After shooting, all videos are transcoded with Apple ProRes 422 HQ codec. We do not use raw files as the ProRes losses are insignificant for our task. We can easily work with such files in Davinci Resolve.
Unless otherwise specified, we always use Apple ProRes 422 HQ in this work, including for storing method results.
As in other works on the topic of Inverse Tone Mapping, we need to get SDR versions of HDR video. We have tested traditional tonemappers such as Reinhard, Mantiuk, Drago and others. We have also collected statistics on articles in the field, and it turned out that the most popular method is Reinhard. However, its results were of insufficient quality for us. Therefore, we gave up using traditional approaches.
We settled on the HDR Tools utility integrated into FinalCut 10.6. It has no disadvantages of Reinhard and others:
- It is fully automatic and does not require any manual adjustments, except for the input and output gamma curve
- It gives a significantly more natural-looking video.
We did not conduct a subjective research on this topic and we have found no other studies showing that HDR Tools is the best tonmapping method. However, perhaps we will do that research in the future.
SDR version of the Dataset is available here
Note that the HDR version of Dataset have not been published and will not be published in the future
MSU Benchmark Collection
- MSU Super-Resolution for Video Compression Benchmark 2022
- MSU Video Quality Metrics Benchmark 2022
- MSU Video Upscalers 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
- Video Quality Measurement Tool 3D
MSU Datasets Collection
- Video Filters