/DCVC

Deep Contextual Video Compression

Primary LanguagePythonMIT LicenseMIT

Introduction

Official Pytorch implementation for Neural Video Compression including:

On the comparison

Please note that different methods may use different configurations to test different models, such as

  • Source video may be different, e.g., cropped or padded to the desired resolution.
  • Intra period may be different, e.g., 32, 12, or 10.
  • Number of encoded frames may be different.

So, it does not make sense to compare the numbers in different methods directly, unless making sure they are using same test conditions.

Please find more details on the test conditions.

Acknowledgement

The implementation is based on CompressAI and PyTorchVideoCompression.

Citation

If you find this work useful for your research, please cite:

@article{li2021deep,
  title={Deep Contextual Video Compression},
  author={Li, Jiahao and Li, Bin and Lu, Yan},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

@inproceedings{li2022hybrid,
  title={Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression},
  author={Li, Jiahao and Li, Bin and Lu, Yan},
  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
  year={2022}
}

@inproceedings{wang2023EVC,
  title={EVC: Towards Real-Time Neural Image Compression with Mask Decay},
  author={Wang, Guo-Hua and Li, Jiahao and Li, Bin and Lu, Yan},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.