Introduction
Official Pytorch implementation for Neural Video Compression including:
- Deep Contextual Video Compression, NeurIPS 2021, in this folder.
- Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression, ACM MM 2022, in this folder.
- The first end-to-end neural video codec to exceed H.266 (VTM) using the highest compression ratio configuration, in terms of both PSNR and MS-SSIM.
- The first end-to-end neural video codec to achieve rate adjustment in single model.
- EVC: Towards Real-Time Neural Image Compression with Mask Decay, ICLR 2023, in this folder.
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}
}
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