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]
We provide the test code of our DVC framework. In our imolementation, we use the learning based image compression algorithm (Variational image compression with a scale hyperprior, ICLR'18) as the intra coompression.
Specifically, for the video codec model wuth lambda=k
, the image codec model with lambda=4k
is used as the intra frames.
Both the pre-train models of video codec and image codecs in our framework are available at Dropbox.
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.
cd ./TestDemo/ImageCodec
Image Encoding,
python Encoder.py --EncoderModel /path/to/encoder/model/.pb --input_frame /path/to/image/x.png --output /output/feature/folder/
Image Decoding,
python Decoder.py --DecoderModel /path/to/decoder/model/.pb --loadpath /load/feature/folder/
cd ./TestDemo/VideoCodec
Video Encoding,
python Encoder.py --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 Decoder.py --DecoderModel /path/to/decoder/model/.pb --refer_frame /path/to/previous/im001.png --loadpath /path/to/feature/folder/
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:
@article{lu2018dvc,
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},
year={2018}
}
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Deploy Model
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Source Code
You can contact Guo Lu by sending mail to [email protected]