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.

❤️ If you find this repository useful, please star this repo 🌟 and cite our paper 📄.

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}
}

This GIF image has been tone-mapped to SDR and compressed for visibility.

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

⚠️ Note: The HDR.mp4 file 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.

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

The code framework follows BasicSR.

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.

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 ([email protected]).