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# Introduction

Official Pytorch implementation for DCVC-DC: [Neural Video Compression with **D**iverse **C**ontexts](https://arxiv.org/abs/2302.14402), in CVPR 2023.
- The first end-to-end neural video codec to exceed [ECM](https://jvet-experts.org/doc_end_user/documents/27_Teleconference/wg11/JVET-AA0006-v1.zip) using the highest compression ratio low delay configuration with a intra refresh period roughly to one second (32 frames), in terms of PSNR and MS-SSIM for RGB content.
- The first end-to-end neural video codec to exceed ECM using the highest compression ratio low delay configuration with a intra refresh period roughly to one second (32 frames), in terms of PSNR for YUV420 content.


# Prerequisites
* Python 3.8 and conda, get [Conda](https://www.anaconda.com/)
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# Introduction

Official Pytorch implementation for DCVC-FM: [Neural Video Compression with **F**eature **M**odulation](https://arxiv.org/abs/2402.17414), in CVPR 2024.
- The first end-to-end neural video codec to exceed ECM using the highest compression ratio low delay configuration with only one intra frame, in terms of PSNR for both YUV420 content and RGB content in a single model.
- The first end-to-end neural video codec that support a large quality and bitrate range in a single model.

# Prerequisites
* Python 3.10 and conda, get [Conda](https://www.anaconda.com/)
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# Introduction

Official Pytorch implementation for DCVC-HEM: [**H**ybrid Spatial-Temporal **E**ntropy **M**odelling for Neural Video Compression](https://arxiv.org/abs/2207.05894), in ACM MM 2022.
- 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.

# Prerequisites
* Python 3.8 and conda, get [Conda](https://www.anaconda.com/)
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# DCVC-family


:rocket: The DCVC-family offers a series of neural image and video codecs. This document provides links to the code, papers, and checkpoints for each codec.

| Model | Link | Checkpoint |
| ----- | ---- | ---------- |
| DCVC | [Code](DCVC) & [Paper (NeurIPS 2021)](https://proceedings.neurips.cc/paper/2021/file/96b250a90d3cf0868c83f8c965142d2a-Paper.pdf) & [Paper (arXiv)](https://arxiv.org/abs/2109.15047)| [Checkpoints](https://1drv.ms/u/s!AozfVVwtWWYoiS5mcGX320bFXI0k?e=iMeykH) |
| DCVC-TCM | [Code](DCVC-TCM) & [Paper (IEEE TMM)](https://ieeexplore.ieee.org/document/9941493) & [Paper (arXiv)](https://arxiv.org/abs/2111.13850)| [Checkpoints](https://onedrive.live.com/?authkey=%21ADwwaonwTGR%5FNR8&id=2866592D5C55DF8C%211234&cid=2866592D5C55DF8C) |
| DCVC-HEM | [Code](DCVC-HEM) & [Paper (ACM MM 2022)](https://dl.acm.org/doi/abs/10.1145/3503161.3547845) & [Paper (arXiv)](https://arxiv.org/abs/2207.05894)| [Checkpoints](https://1drv.ms/u/s!AozfVVwtWWYoiUAGk6xr-oELbodn?e=kry2Nk) |
| DCVC-DC | [Code](DCVC-DC) & [Paper (CVPR 2023)](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Neural_Video_Compression_With_Diverse_Contexts_CVPR_2023_paper.pdf) & [Paper (arXiv)](https://arxiv.org/abs/2302.14402)| [Checkpoints](https://1drv.ms/u/s!AozfVVwtWWYoiWdwDhEkZMIfpon5?e=JcGri5) |
| DCVC-FM | [Code](DCVC-FM) & [Paper (CVPR 2024)](https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Neural_Video_Compression_with_Feature_Modulation_CVPR_2024_paper.pdf) & [Paper (arXiv)](https://arxiv.org/abs/2402.17414)| [Checkpoints](https://1drv.ms/f/s!AozfVVwtWWYoi1QkAhlIE-7aAaKV?e=OoemTr) |
| EVC | [Code](EVC) & [Paper (ICLR 2023)](https://openreview.net/forum?id=XUxad2Gj40n) & [Paper (arXiv)](https://arxiv.org/abs/2302.05071)| [Checkpoints](https://1drv.ms/u/s!AozfVVwtWWYoiUhZLZDx7vJjHK1C?e=qETpA1) |

* As a backup, all the pretrained models could be found [here](https://1drv.ms/f/c/2866592d5c55df8c/EozfVVwtWWYggCitBAAAAAABbT4z2Z10fMXISnan72UtSA?e=BID7DA).

:bouquet: The implementation of DCVC-family is based on [CompressAI](https://github.com/InterDigitalInc/CompressAI) and [PyTorchVideoCompression](https://github.com/ZhihaoHu/PyTorchVideoCompression).

:page_facing_up: 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}
}

@article{sheng2022temporal,
title={Temporal context mining for learned video compression},
author={Sheng, Xihua and Li, Jiahao and Li, Bin and Li, Li and Liu, Dong and Lu, Yan},
journal={IEEE Transactions on Multimedia},
year={2022},
publisher={IEEE}
}

@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{li2023neural,
title={Neural Video Compression with Diverse Contexts},
author={Li, Jiahao and Li, Bin and Lu, Yan},
booktitle={{IEEE/CVF} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2023, Vancouver, Canada, June 18-22, 2023},
year={2023}
}

@inproceedings{li2024neural,
title={Neural Video Compression with Feature Modulation},
author={Li, Jiahao and Li, Bin and Lu, Yan},
booktitle={{IEEE/CVF} Conference on Computer Vision and Pattern Recognition,
{CVPR} 2024, Seattle, WA, USA, June 17-21, 2024},
year={2024}
}

@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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