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DVC: An End-to-end Deep Video Compression Framework, CVPR 2019 (Oral)
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DVC: An End-to-end Deep Video Compression Framework

This repo holds the code for the paper:

DVC: An End-to-end Deep Video Compression Framework, Guo Lu, Wanli Ouyang, Dong Xu, Xiaoyun Zhang,Chunlei Cai, Zhiyong Gao, CVPR 2019 (Oral). [arXiv]


Pretrain models

We provide the test code of our DVC framework. In our implementation, we use the learning based image compression algorithm (Ballé et al) as the intra compression. Specifically, for the video codec model with lambda=k, the image codec model with lambda=4k is used as the intra frames. Both the pre-train models of video codecs and image codecs in our framework are available at Dropbox.

Entropy Coding

Currently, we do not provide the entropy coding module. The generated features from image/video codecs are saved to .pkl files. We give the estimated Bpp for these features. It is straightforward to compress these features by using traditional entroy coding tools, such as CABAC or Ranger Codec.

Image Compression

cd ./TestDemo/ImageCodec

Image Encoding,

python --EncoderModel /path/to/encoder/model/.pb --input_frame /path/to/image/x.png  --output /output/feature/folder/

Image Decoding,

python --DecoderModel /path/to/decoder/model/.pb   --loadpath  /load/feature/folder/

Video Compression

cd ./TestDemo/VideoCodec

Video Encoding,

python --EncoderModel /path/to/encoder/model/.pb  --input_frame /path/to/currentframe/im002.png --refer_frame  /path/to/previousframe/im001.png  	--output /output/feature/folder/

Video Decoding,

python --DecoderModel /path/to/decoder/model/.pb  --refer_frame /path/to/previous/im001.png --loadpath  /path/to/feature/folder/

Experimental Results

Evaluation results on the UVG dataset and HEVC Class B (1080p) and Class E (720p). Please refer our paper for more experimental results.

We also provide the scrips for generating all the RD curves of our paper in folder RDCurve.


If you find our paper useful, please cite:

  title={DVC: An End-to-end Deep Video Compression Framework},
  author={Lu, Guo and Ouyang, Wanli and Xu, Dong and Zhang, Xiaoyun and Cai, Chunlei and Gao, Zhiyong},
  journal={arXiv preprint arXiv:1812.00101},


  • Deploy Model

  • Source Code


You can contact Guo Lu by sending mail to

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