IPCodec is an open source entropy-based compression toolbox with tensorflow 1.12.0, which contains Interpolation Variable Rate (IVR) I-frame and P-frame compression models.
This project is developed by Alibaba and licensed under the Apache 2.0 license.
- Linux
- Python 3.6+
- tensorflow 1.12.0
- CUDA 10.0+
Create a conda virtual environment and activate it.
conda create -n tf12 python=3.6 -y conda activate tf12
Install other packages with the following command.
pip install -r requirements.txt
Use the checkpoint/download.sh to download all pretrained models.
cd checkpoint sh download.sh- If not use Unet post network, set is_post to False.
- Download Kodak test image to
dataset/kodak
python I_train.py/I_test.py --loss_metric PSNR --model_name CM \ --is_post True --with_context_model True --is_multi True python I_train.py/I_test.py --loss_metric SSIM --model_name CM \ --is_post True --with_context_model True --is_multi True python I_train.py/I_test.py --loss_metric PSNR --model_name NoCM \ --is_post False --with_context_model False --is_multi True- If not use Unet post network, set is_post to False.
- Convert video to PNG images to
test_set_dirinIP_config.py. IP_test_multi.pyis the Multi videos processing script, need to carefully use it since the model_path is changed.
python IP_train.py/IP_test.py --loss_metric PSNR --model_name STPM \ --is_post True --with_context_model True --is_multi True --ckpt_dir_pre ./checkpoint/I_model/CM_PSNR --idx_test 0 python IP_train.py/IP_test.py --loss_metric SSIM --model_name STPM \ --is_post True --with_context_model True --is_multi True --ckpt_dir_pre ./checkpoint/I_model/CM_SSIM --idx_test 0 python IP_train.py/IP_test.py --loss_metric PSNR --model_name NoSPM \ --is_post True --with_context_model False --is_multi True --ckpt_dir_pre ./checkpoint/I_model/CM_PSNR --idx_test 0 python IP_train.py/IP_test.py --loss_metric SSIM --model_name NoSPM \ --is_post True --with_context_model False --is_multi True --ckpt_dir_pre ./checkpoint/I_model/CM_SSIM --idx_test 0
Note: If you find this useful, please support us by citing the following paper.
@inproceedings{ivr, title={Interpolation variable rate image compression}, author={Sun, Zhenhong and Tan, Zhiyu and Sun, Xiuyu and Zhang, Fangyi and Qian, Yichen and Li, Dongyang and Li, Hao}, booktitle={Proceedings of the 29th ACM International Conference on Multimedia}, pages={5574--5582}, year={2021} } @article{sun2021spatiotemporal, title={Spatiotemporal Entropy Model is All You Need for Learned Video Compression}, author={Sun, Zhenhong and Tan, Zhiyu and Sun, Xiuyu and Zhang, Fangyi and Li, Dongyang and Qian, Yichen and Li, Hao}, journal={arXiv preprint arXiv:2104.06083}, year={2021} }