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.
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”.
- CompressAI: https://github.com/InterDigitalInc/CompressAI
- ELiC-ReImplemetation：https://github.com/VincentChandelier/ELiC-ReImplemetation
- QVRF：https://github.com/bytedance/QRAF
conda create -n compress python=3.7
conda activate compress
pip install compressai=1.1.5
pip install pybind11
pip install ptflops$$Usage
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
@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}}