DVC: An End-to-end Deep Video Compression Framework

PyTorch reimplemetation 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]

Requirements

• Python==3.6
• PyTorch==1.2

Data Preparation

Training data

1. Download Vimeo-90k dataset: original training + test set (82GB)

2. Unzip the dataset into ./data/.

3. Remember to put the file test.txt in ./data/vimeo_septuplet/ to the root of your vimeo dataset if you edit the path of vimeo.

Test data

This method only provide P-frame compression, so we first need to generate I frames by H.265. We take UVG dataset as an example.

1. Download UVG dataset(1080p/8bit/YUV/RAW) to data/UVG/videos/.
2. Crop Videos from 1920x1080 to 1920x1024.

   cd data/UVG/
   ffmpeg -pix_fmt yuv420p  -s 1920x1080 -i ./videos/xxxx.yuv -vf crop=1920:1024:0:0 ./videos_crop/xxxx.yuv
   

3. Convert YUV files to images.

   python convert.py
   

4. Create I frames. We need to create I frames by H.265 with $crf of 20,23,26,29.

   cd CreateI
   sh h265.sh $crf 1920 1024
   

   After finished the generating of I frames of each crf, you need to use bpps of each video in result.txt to fill the bpps in Class UVGdataset in dataset.py.

Training

cd examples/example
sh cp.sh
sh run.sh

If you want models with more λ, you can edit config.json

If you want to use tensorboard:

cd examples
sh tf.sh xxxx

Testing

Our pretrained model with λ=2048,1024,512,256 is provided on Google Drive. You can put it to snapshot/ and run test.sh:

sh test.sh

Citation

If you find this 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}
}