Coarse2Fine
Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression
key words: Image Compression, Hyper-Prior Model
This paper proposes a coarse-to-fine framework with hierarchical layers of hyper-priors to conduct comprehensive analysis of the image and more effectively reduce spatial redundancy, which improves the rate-distortion performance of image compression significantly. The paper is published in 2020, and readers can read the original paper via the link.
Our Contibutions
- Translate from pytorch to mindspore
- Test the mindspore version
File Structure
Coarse2Fine/mindspore
├── __pycache__
├── akg_kernel_meta # akg kernel
├── example.bin # coded binary file
├── example.png # original image
├── module_arithmeticcoding.cpp #arithmetic coder
├── model_baseline.py # model baseline
├── my_model.ckpt # model params
├── reconstruct.png # decoded image
└── test.py # test
Environment
- mindspore-dev==2.0.0dev20230116
- It is recommended to install through the link.
- cuda 11.1
- python 3.7
test
cd Coarse2Fine/mindspore
python test.py
Comparison
Reconstructed image
mindspore version
|
Comparison of the original images with reconsructed ones (original above, reconstructed below) |
Quality measurements on Kodak of MindSpore version
bpp |
PSNR |
MSSSIM |
enc_time |
dec_time |
GPU Memory(MiB) |
lambda |
0.208 |
30.063 |
0.946 |
31.16 |
36.773 |
5036 |
qp1 |
0.309 |
31.611 |
0.963 |
28.08 |
39.709 |
5036 |
qp2 |
1.06 |
38.025 |
0.991 |
51.601 |
61.22 |
6060 |
qp5 |
1.439 |
40.049 |
0.994 |
52.225 |
64.669 |
6060 |
qp7 |
MindSpore models are converted from officially pretrained PyTorch models.
Citation
@inproceedings{hu2020coarse,
title={Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression},
author={Hu, Yueyu and Yang, Wenhan and Liu, Jiaying},
booktitle={AAAI Conference on Artificial Intelligenc},
year={2020}
}
Contributors
Name:
Chenhao Zhang