ELIC: Efficient Learned Image Compression withUnevenly Grouped Space-Channel Contextual Adaptive Coding.
A Pytorch Implementation of "ELIC: Efficient Learned Image Compression withUnevenly Grouped Space-Channel Contextual Adaptive Coding."
Note that This Is Not An Official Implementation Code.
More details can be found in the following paper:
@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}
}
- CompressAI: https://github.com/InterDigitalInc/CompressAI
- ELIC-QVRF：https://github.com/VincentChandelier/ELIC-QVRF
- QVRF：https://github.com/bytedance/QRAF
| lambda | Link |
|---|---|
| 0.45 | 0.45 |
| 0.15 | 0.15 |
| 0.032 | 0.032 |
| 0.016 | 0.0016 |
| 0.008 | 0.008 |
| 0.004 | 0.004 |
According to the paper, They train the models on the largest 8000 images picked from ImageNet dataset. so download the ImageNet
The preprocessing and selection of ImageNet dataset is same to QVRF：https://github.com/bytedance/QRAF.
This code is based on the CompressAI.
pip3 install compressai==1.1.5
pip3 install thop
pip3 install ptflops
pip3 install timm
cd Code
python3 train.py -d ./dataset --N 192 --M 320 -e 4000 -lr 1e-4 -n 8 --lambda 13e-3 --batch-size 16 --test-batch-size 16 --aux-learning-rate 1e-3 --patch-size 256 256 --cuda --save --seed 1926 --clip_max_norm 1.0
In ELIC, each model is finetune by 200 epoches.
python3 train.py -d ./dataset --N 192 --M 320 -e 4000 -lr 1e-4 -n 8 --lambda 13e-3 --batch-size 16 --test-batch-size 16 --aux-learning-rate 1e-3 --patch-size 256 256 --cuda --save --seed 1926 --clip_max_norm 1.0 --pretrained --checkpoint Pretrained4000epoch_checkpoint.pth.tar
python3 -m updata.py checkpoint -n updatacheckpoint-name
python Inference.py --dataset ./dataset/Kodak --output_path ELIC_0450_ft_3980_Plateau -p ./ELIC_0450_ft_3980_Plateau.pth.tar --patch 64
We trained the network and ask the RD points from the author.
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