EVC

EVC: Towards Real-Time Neural Image Compression with Mask Decay

This is the official Pytorch implementation for EVC: Towards Real-Time Neural Image Compression with Mask Decay, in ICLR 2023.

Prerequisites

Environment:

conda create -n $YOUR_PY38_ENV_NAME python=3.8
conda activate $YOUR_PY38_ENV_NAME
conda install pytorch=1.11.0 torchvision=0.12.0 torchaudio=0.11.0 cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

If you want to test the model with writing bitstream, please build this project.

On Windows

cd src
mkdir build
cd build
conda activate $YOUR_PY38_ENV_NAME
cmake ../cpp -G "Visual Studio 16 2019" -A x64
cmake --build . --config Release

On Linux

sudo apt-get install cmake g++
cd src
mkdir build
cd build
conda activate $YOUR_PY38_ENV_NAME
cmake ../cpp -DCMAKE_BUILD_TYPE=Release
make -j

Test Dataset

Please download the Kodak dataset from http://r0k.us/graphics/kodak/.

The folder is organized as

.../kodak/
  - kodim01.png
  - kodim02.png
  - ...

Please modify the root_path in test_cfg/local_kodak.json to match the data location.

Pretrained Models

• Download our pretrained models and put them into ./checkpoints folder.
• Or run the script in ./checkpoints directly to download the model.

i_frame_model	i_frame_model_path
EVC_LL	EVC_LL.pth.tar
EVC_ML	EVC_ML_MD.pth.tar
EVC_SL	EVC_SL_MD.pth.tar
EVC_LM	EVC_LM_MD.pth.tar
EVC_LS	EVC_LS_MD.pth.tar
EVC_MM	EVC_MM_MD.pth.tar
EVC_SS	EVC_SS_MD.pth.tar
Scale_EVC_SL	Scale_EVC_SL_MDRRL.pth.tar
Scale_EVC_SS	Scale_EVC_SS_MDRRL.pth.tar

Test the Models

We use test_image_codec.py to test our models. Please check this file and make sure you have downloaded models in checkpoints. Then, just run

python run_test_image.py

If you want to test with actual bitstream writing, set --write_stream 1 in command_line.

CPU performance scaling

Note that the arithmetic coding runs on the CPU, please make sure your CPU runs at high performance while writing the actual bitstream. Otherwise, the arithmetic coding may take a long time.

Check the CPU frequency by

grep -E '^model name|^cpu MHz' /proc/cpuinfo

Run the following command to maximum CPU frequency

echo performance | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor

Run the following command to recover the default frequency

echo ondemand | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor

Architecture

The framework:

The encoder and decoder:

The hyperprior:

The dual prior:

R-D Curves

Citation

If you find the work useful for your research, please cite:

@inproceedings{wang2023EVC,
  title={EVC: Towards Real-Time Neural Image Compression with Mask Decay},
  author={Wang, Guo-Hua and Li, Jiahao and Li, Bin and Lu, Yan},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

Acknowledgement

• CompressAI: https://github.com/InterDigitalInc/CompressAI