README.md

Deep Hierarchical Video Compression

This repository contains our series of works on Deep Hierarchical Video Compression.

• DHVC 1.0: The first hierarchical predictive coding method moves away from the hybrid coding framework, achieving best-in-class performance. Paper is available at Deep Hierarchical Video Compression (AAAI 2024).
• DHVC 2.0: The enhanced hierarchical predictive coding method, which integrates variable-rate intra- and inter-coding into a single model, delivering not only superior compression performance to representative methods but also real-time processing with a significantly smaller memory footprint on standard GPUs. Paper is available at High-Efficiency Neural Video Compression via Hierarchical Predictive Learning (arxiv 2024).

Requirments

• Python 3.8+
• CUDA 11.0
• pytorch 1.11.0
• For others, please refer to requirements.txt

Pretrained Models

Pretrained Models will be released soon.

Dataset

• Train dataset: Vimeo90k
• Test dataset: UVG、MCL-JCV、HEVC Class B

Usage

Testing

Please download the pretrained models into ./pretrained and configure the environment properly mentioned above first.

python test.py

The testing rusults can be found in ./runs.

Citation

If you find this work helpful to your research, please cite

@inproceedings{lu2024deep,
  title={Deep Hierarchical Video Compression},
  author={Lu, Ming and Duan, Zhihao and Zhu, Fengqing and Ma, Zhan},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={8},
  pages={8859--8867},
  year={2024}
}

@article{lu2024high,
  title={High-Efficiency Neural Video Compression via Hierarchical Predictive Learning},
  author={Lu, Ming and Duan, Zhihao and Cong, Wuyang and Ding, Dandan and Zhu, Fengqing and Ma, Zhan},
  journal={arXiv preprint arXiv:2410.02598},
  year={2024}
}