https://github.com/tinyvision/ipcodec
https://github.com/tinyvision/ipcodec
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/tinyvision/ipcodec
- Owner: tinyvision
- License: apache-2.0
- Created: 2022-07-08T08:30:23.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-07-08T08:30:55.000Z (over 3 years ago)
- Last Synced: 2025-04-06T06:34:33.938Z (12 months ago)
- Language: Python
- Size: 397 KB
- Stars: 19
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Introduction
IPCodec is an open source entropy-based compression toolbox with **tensorflow 1.12.0**, which contains Interpolation Variable Rate (IVR) I-frame and P-frame compression models.
***
## License
This project is developed by Alibaba and licensed under the [Apache 2.0 license](LICENSE).
***
## Installation
### Prerequisites
* Linux
* Python 3.6+
* tensorflow 1.12.0
* CUDA 10.0+
### Prepare environment
1. Create a conda virtual environment and activate it.
```shell
conda create -n tf12 python=3.6 -y
conda activate tf12
```
2. Install other packages with the following command.
```shell
pip install -r requirements.txt
```
***
## Easy to use
Use the [checkpoint/download.sh](checkpoint/download.sh) to download all pretrained models.
```shell
cd checkpoint
sh download.sh
```
### Train or test for image compression
- If not use Unet post network, set is_post to False.
- Download [Kodak](http://r0k.us/graphics/kodak/) test image to `dataset/kodak`
```shell
python I_train.py/I_test.py --loss_metric PSNR --model_name CM \
--is_post True --with_context_model True --is_multi True
python I_train.py/I_test.py --loss_metric SSIM --model_name CM \
--is_post True --with_context_model True --is_multi True
python I_train.py/I_test.py --loss_metric PSNR --model_name NoCM \
--is_post False --with_context_model False --is_multi True
```
### Train or test for video compression
- If not use Unet post network, set is_post to False.
- Convert video to PNG images to `test_set_dir` in `IP_config.py`.
- `IP_test_multi.py` is the Multi videos processing script, need to carefully use it since the model_path is changed.
```shell
python IP_train.py/IP_test.py --loss_metric PSNR --model_name STPM \
--is_post True --with_context_model True --is_multi True --ckpt_dir_pre ./checkpoint/I_model/CM_PSNR --idx_test 0
python IP_train.py/IP_test.py --loss_metric SSIM --model_name STPM \
--is_post True --with_context_model True --is_multi True --ckpt_dir_pre ./checkpoint/I_model/CM_SSIM --idx_test 0
python IP_train.py/IP_test.py --loss_metric PSNR --model_name NoSPM \
--is_post True --with_context_model False --is_multi True --ckpt_dir_pre ./checkpoint/I_model/CM_PSNR --idx_test 0
python IP_train.py/IP_test.py --loss_metric SSIM --model_name NoSPM \
--is_post True --with_context_model False --is_multi True --ckpt_dir_pre ./checkpoint/I_model/CM_SSIM --idx_test 0
```
***
## Results
### Results for I-frame compression (Image compression) on Kodak Dataset.
### Results for P-frame compression (Video compression) on MCL-JCV, UVG, HEVC-ClassB Dataset.
**Note**:
If you find this useful, please support us by citing the following paper.
```
@inproceedings{ivr,
title={Interpolation variable rate image compression},
author={Sun, Zhenhong and Tan, Zhiyu and Sun, Xiuyu and Zhang, Fangyi and Qian, Yichen and Li, Dongyang and Li, Hao},
booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
pages={5574--5582},
year={2021}
}
```
```
@article{sun2021spatiotemporal,
title={Spatiotemporal Entropy Model is All You Need for Learned Video Compression},
author={Sun, Zhenhong and Tan, Zhiyu and Sun, Xiuyu and Zhang, Fangyi and Li, Dongyang and Qian, Yichen and Li, Hao},
journal={arXiv preprint arXiv:2104.06083},
year={2021}
}
```
***
## Main Contributors
[Zhenhong Sun](https://sites.google.com/view/sunzhenhong),[Dongyang Li](https://scholar.google.com.hk/citations?user=ko3eGS0AAAAJ), [Xiuyu Sun](https://sites.google.com/view/sunxiuyu).