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mdyao / HDR-BiTNet

[TMM 2023] Official Implementation of "Bidirectional Translation Between UHD-HDR and HD-SDR Videos"
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bidirectional enhancement hdr hdr-generation hdr-image hdr-standard hdr-video image sdr-to-hdr

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## Bidirectional Translation between UHD-HDR and HD-SDR Videos [Paper](https://ieeexplore.ieee.org/document/10025794/) [Mingde Yao](https://scholar.google.com/citations?user=fsE3MzwAAAAJ&hl=en), [Dongliang He](https://scholar.google.com/citations?user=ui6DYGoAAAAJ&hl=en), [Xin Li](https://scholar.google.com/citations?user=4BEGYMwAAAAJ&hl=zh-CN), [Zhihong Pan](https://scholar.google.com/citations?user=IVxQvz0AAAAJ&hl=en), and [Zhiwei Xiong](http://staff.ustc.edu.cn/~zwxiong/) University of Science and Technology of China (USTC) :rocket: This is the official repository of HDR-BiTNet (TMM 2023).

HDR-BiTNet aims at addressing the practical translation between UHD-HDR (HDRTV) and HD-SDR (SDRTV) videos.

We provide the training and test code along with the trained weights and the dataset (train+test) used for the HDR-BiTNet.

:heart: If you find this repository useful, please star this repo :star2: and cite our paper :page_facing_up:.

Reference:

Mingde Yao, Dongliang He, Xin Li, Zhihong Pan, and Zhiwei Xiong, "Bidirectional Translation Between UHD-HDR and HD-SDR Videos", IEEE Transactions on Multimedia, 2023.

Bibtex:

@article{yao2023bidirectional,
  title={Bidirectional Translation Between UHD-HDR and HD-SDR Videos},
  author={Yao, Mingde and He, Dongliang and Li, Xin and Pan, Zhihong and Xiong, Zhiwei},
  journal={IEEE Transactions on Multimedia},
  year={2023},
  publisher={IEEE}
}

Video example

:warning: Note: This GIF file has been tone-mapped to SDR and compressed for visibility.

test_set_AdobeExpress_AdobeExpress_AdobeExpress

See more comparison of SDR video and HDR video in example/ folder.

Note: The HDR file in example/ is encoded to comply with the HDR10 standard. For the best viewing experience, please watch the video on certified HDR displays with maximum brightness. Alternatively, a Mac Book Pro can be used for convenience.

Test code

Quick Start

  1. Download the test dataset from this link.
  2. Unzip and place the 'test' dataset in a proper folder, e.g., /testdata.
  3. Put the pretrained model file (./model.pth) in a proper folder.

  4. Set a config file in options/test/, then run as following:

    python test.py -opt options/test/test.yml

Code Framework

The code framework follows BasicSR.

Contents

Config: options/ Configure the options for data loader, network structure, model, training strategies and etc.

Data: data/ A data loader to provide data for training, validation and testing.

Model: models/ Construct models for training and testing.

Network: models/modules/ Construct network architectures.

Contact

If you have any problem with the released code, please do not hesitate to open an issue.

For any inquiries or questions, please contact me by email (mdyao@mail.ustc.edu.cn).