The official PyTorch implementation of our ICME 2023 paper:

Low-complexity Deep Video Compression with A Distributed Coding Architecture

Xinjie Zhang, Jiawei Shao, Jun Zhang

[ArXiv Preprint]

Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce temporal redundancy, which makes it challenging to deploy them on resource-constrained devices. Since the 1970s, distributed source coding theory has indicated that independent encoding and joint decoding with side information (SI) can achieve high-efficient compression of correlated sources. This has inspired a distributed coding architecture aiming at reducing the encoding complexity. However, traditional distributed coding methods suffer from a substantial performance gap to predictive coding ones. Inspired by the great success of learning-based compression, we propose the first end-to-end distributed deep video compression framework to improve the rate-distortion performance. A key ingredient is an effective SI generation module at the decoder, which helps to effectively exploit inter-frame correlations without computation-intensive encoder-side motion estimation and compensation. Experiments show that our method significantly outperforms conventional distributed video coding and H.264. Meanwhile, it enjoys $6\sim 7\times$ encoding speedup against DVC with comparable compression performance.

❤️❤️❤️Our idea is implemented based on the following projects. We really appreciate their wonderful open-source works!

• CompressAI [related paper]
• RIFE [related paper]

If any parts of our paper and code help your research, please consider citing us and giving a star to our repository.

@inproceedings{zhang2023low,
  title={Low-complexity Deep Video Compression with A Distributed Coding Architecture},
  author={Zhang, Xinjie and Shao, Jiawei and Zhang, Jun},
  booktitle={IEEE International Conference on Multimedia and Expo},
  year={2023},
}

@article{zhang2023low,
  title={Low-complexity Deep Video Compression with A Distributed Coding Architecture},
  author={Zhang, Xinjie and Shao, Jiawei and Zhang, Jun},
  journal={arXiv preprint arXiv:2303.11599},
  year={2023}
}