Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding + QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression

Pytorch implementation of the paper "Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding" + “QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression”

We make the ELIC into variable rate model This repository is based on ELiC-ReImplemetation. We kept network scripts, and removed other components. The major changes are provided in network.py. For the official code release, see the ELiC-ReImplemetation.

About

This repo defines ELIC in varibale rate model for learned image compression in "Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding" + “QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression”.

Related links

• CompressAI: https://github.com/InterDigitalInc/CompressAI
• ELiC-ReImplemetation：https://github.com/VincentChandelier/ELiC-ReImplemetation
• QVRF：https://github.com/bytedance/QRAF

Installation

conda create -n compress python=3.7
conda activate compress
pip install compressai=1.1.5
pip install pybind11
pip install ptflops

### Training stage 1 ``` python train.py -d ./dataset -e 2000 -lr 1e-4 -n 8 --batch-size 8 --test-batch-size 64 --aux-learning-rate 1e-3 --patch-size 256 256 --cuda --save --seed 1926 --clip_max_norm 1.0 --stage 1 --ste 0 --loadFromPretrainedSinglemodel 0 ``` stage 2 ``` python3 train.py -d ./dataset -e 500 -lr 1e-4 -n 8 --batch-size 8 --test-batch-size 64 --aux-learning-rate 1e-3 --patch-size 256 256 --cuda --save --seed 1926 --clip_max_norm 1.0 --stage 2 --ste 0 --refresh 1 --loadFromPretrainedSinglemodel 0 --checkpoint checkpoint_best_loss.pth.tar ``` stage 3 ``` python3 train.py -d ./dataset -e 500 -lr 1e-4 -n 8 --batch-size 8 --test-batch-size 64 --aux-learning-rate 1e-3 --patch-size 256 256 --cuda --save --seed 1926 --clip_max_norm 1.0 --stage 3 --ste 1 --refresh 1 --loadFromPretrainedSinglemodel 0 --checkpoint checkpoint_best_loss.pth.tar ``` ###Inference ``` python3 Inference.py --dataset ./dataset/Kodak --s 8 --output_path ELICVR -p ./checkpoint_best_loss.pth.tar --patch 64 --factormode 0 --factor 0 ``` ## Citation ``` @inproceedings{he2022elic, title={Elic: Efficient learned image compression with unevenly grouped space-channel contextual adaptive coding}, author={He, Dailan and Yang, Ziming and Peng, Weikun and Ma, Rui and Qin, Hongwei and Wang, Yan}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={5718--5727}, year={2022} } @INPROCEEDINGS{10222717, author={Tong, Kedeng and Wu, Yaojun and Li, Yue and Zhang, Kai and Zhang, Li and Jin, Xin}, booktitle={2023 IEEE International Conference on Image Processing (ICIP)}, title={QVRF: A Quantization-Error-Aware Variable Rate Framework for Learned Image Compression}, year={2023}, volume={}, number={}, pages={1310-1314}, doi={10.1109/ICIP49359.2023.10222717}} ``` ```