https://github.com/fl0wbar/eic-balle2017
Experiments of applying "End-to-end optimized image compression" Balle, 2017 on CLIC-2019 dataset
https://github.com/fl0wbar/eic-balle2017
compress-images compression deep-learning hobby-project image-compression tensorflow tensorflow-compression
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Experiments of applying "End-to-end optimized image compression" Balle, 2017 on CLIC-2019 dataset
- Host: GitHub
- URL: https://github.com/fl0wbar/eic-balle2017
- Owner: fl0wbar
- License: apache-2.0
- Created: 2022-08-04T15:42:09.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-08-04T15:44:55.000Z (almost 4 years ago)
- Last Synced: 2025-06-16T16:42:56.497Z (about 1 year ago)
- Topics: compress-images, compression, deep-learning, hobby-project, image-compression, tensorflow, tensorflow-compression
- Language: Python
- Homepage:
- Size: 42 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# EIC(Balle 2017)
#### Experiments done in January 2019
The repo is a re-implementation of the model published
in:
> "End-to-end optimized image compression"
> J. Ballé, V. Laparra, E. P. Simoncelli
> https://arxiv.org/abs/1611.01704
#### Code for reproducing the results are modified from Tensorflow Compression (tensorflow 1.13 compatible commit)
##### This directory contains Tensorflow Compression code modified to use range coding ops in the tensorflow.contrib.coder.python.ops in tensorflow 1.13
##### Do not use with original Tensorflow Compression as it has fast custom kernels for range coding.
##### This was done to avoid compilation issues.
##### Please refer to README from Tensorflow > 2.0 compatible Tensorflow Compression (https://github.com/tensorflow/compression) repo for better code for reproducing these results.
##### This repo is a public backup of a hobby project.
### CLIC Dataset
#### For script options
```bash
$ python eeicballe17.py -h
```
#### Training
```bash
$ python eeicballe17.py -v --train_glob="./datasets/CLIC/professional/train/*.png" --checkpoint_dir=./models/balle17CLIC train
```
#### Compressing and Decompressing single image using trained model
- Compress using model trained on CLIC 2019
```bash
$ python eeicballe17.py --verbose --checkpoint_dir=./models/balle17CLIC compress ./tests/groundtruth/kodak/kodim01.png ./tests/reconCLIC/epoch30684/kodim01.tfic
```
- Compress using model trained on BSDS500
```bash
$ python eeicballe17.py --verbose --checkpoint_dir=./models/balle17BSDS500 compress ./tests/groundtruth/kodak/kodim01.png ./tests/reconBSDS500/kodim01_bsds.tfic
```
- Decompress using model trained on CLIC 2019
```bash
$ python eeicballe17.py --verbose --checkpoint_dir=./models/balle17CLIC decompress ./tests/reconCLIC/epoch30684/kodim01.tfic ./tests/reconCLIC/epoch30684/kodim01_recon.png
```
- Decompress using model trained on BSDS500
```bash
$ python eeicballe17.py --verbose --checkpoint_dir=./models/balle17BSDS500 decompress ./tests/reconBSDS500/kodim01_bsds.tfic ./tests/reconBSDS500/kodim01_recon.png
```
## Results on CLIC2019 dataset
## Example
Original | EIC
:-------------------------:|:-------------------------:
| 
| 
|
```python
Original: 491.1 kB
EIC: Compressed File size: 15.1 kB
Mean squared error: 29.7069
PSNR (dB): 33.40
Multiscale SSIM: 0.9706
Multiscale SSIM (dB): 15.32
Information content in bpp: 0.3120
Actual bits per pixel: 0.3148
```
The image shown is an out-of-sample instance from the Kodak dataset. The EIC image is obtained by reconstruction via a learned model on CLIC-2019.
Note that the learned model was not adapted in any way for evaluation on this image.
#### bpp (bits-per-pixel)
#### mse (mean-squared-error)
#### loss (mse + entropy coding auxiliary loss)
#### probabilty mass function plot of entropy coder
#### tensorboard model graph
## Citation
> "End-to-end optimized image compression"
> J. Ballé, V. Laparra, E. P. Simoncelli
> https://arxiv.org/abs/1611.01704
> "Efficient nonlinear transforms for lossy image compression"
> J. Ballé
> https://arxiv.org/abs/1802.00847
If you use this library for research purposes, please cite:
```
@software{tfc_github,
author = "Ballé, Johannes and Hwang, Sung Jin and Johnston, Nick",
title = "{T}ensor{F}low {C}ompression: Learned Data Compression",
url = "http://github.com/tensorflow/compression",
version = "1.2b1 (beta)",
year = "2018",
}
```
In the above BibTeX entry, names are top contributors sorted by number of
commits. Please adjust version number and year according to the version that was
actually used.