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Lossy Image Compression with Compressive Autoencoders
key words: Image Compression, Compressive Autoencoders
The model utilizes the Compressive Autoencoders to compress images. The paper is published in 2017, and readers can read the original paper via the link.
The translated model file is in CAE/demo/model_baseline.py.
demo
├── ckpt
│ └── latest.ckpt # test model file
├── model_baseline.py # reproduce model
├── pycache
├── test.py # test in reality
└── train.py # train
Please intall compressai via
pip install -e .
under dictionary CAE.
Train the model via:
cd demo
python train.py -d your/own/dataset/address
We code and decode via:
cd demo
python test.py -d your/own/dataset/address
Actually, it is very fast although it is related to disk I/O. Therefore, we will not provide the fast version of testing.
mindspore version
bpp | PSNR | MSSSIM | enc_time | dec_time | GPU Memory(MiB) | lambda |
---|---|---|---|---|---|---|
0.327 | 26.618 | 0.933 | 5.784 | 21.105 | 4002 | 0.01 |
0.586 | 27.635 | 0.958 | 5.967 | 22.718 | 4002 | 0.04 |
0.761 | 27.772 | 0.96 | 5.497 | 21.23 | 4002 | 0.08 |
1.296 | 27.799 | 0.96 | 6.94 | 24.043 | 4002 | 0.2 |
PSNR | bpp | |
---|---|---|
Official PT version | about 32* | about 0.7* |
*: There is not official implementation. We infer the value via the presentation in the paper.
Besides, we reproduce the model according to the Suro Lee's project. We appreciate the contribution of her/him from the bottom of our heart.
@article{theis2017lossy,
title={Lossy image compression with compressive autoencoders},
author={Theis, Lucas and Shi, Wenzhe and Cunningham, Andrew and Huszar, Ferenc},
journal={arXiv preprint arXiv:1703.00395},
year={2017}
}
Name:
Zhuozhen Yu
email: yuzhuozhen@stu.pku.edu.cn
Please keep free to contact us.
Hua Ye
The project is the reproduce of paper "Lossy Image Compression with Compressive Autoencoders "
Python C++ Text Makefile other
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Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
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