If our open source codes are helpful for your research, please cite our technical report:
@Article{e26100836,
AUTHOR = {Leiderman, Timor and Ben Ezra, Yosef},
TITLE = {Information Bottleneck Driven Deep Video Compression—IBOpenDVCW},
JOURNAL = {Entropy},
VOLUME = {26},
YEAR = {2024},
NUMBER = {10},
ARTICLE-NUMBER = {836},
URL = {https://www.mdpi.com/1099-4300/26/10/836},
ISSN = {1099-4300},
DOI = {10.3390/e26100836}
}
prepare the dataset:
install BPG for training we used BPG to compress the first frame for the I frame compression training
BPG (Download link)
Download the training data. We train the models on the Vimeo90k dataset (Download link) (82G).
unzip the files to some dir
Use the script to generate the .npy file containing all the paths to the dataset images tools/gen_vimeo_npy.py
We used 240x240x3 resolution for training
docker build -t tensorflow-wavelets:1.0 .
docker run --privileged=true -v /mnt/:/mnt/ --gpus all --user 1000:1000 -p 6006:6006 -p 8080:8080 tensorflow-wavelets:1.0
Based on
Lu, Guo, et al. "DVC: An end-to-end deep video compression framework." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019. Paper
conda create -n py38 python=3.8
pip install -r Free Software, Hell Yeah!