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https://github.com/quzanh1130/multi_metrics_to_compare_images
Comparing two images by using 9 metrics: VIFP, PSNR, SSIM, FSIM, RMSE, ISSM, SRE, SAM, UIQ.
https://github.com/quzanh1130/multi_metrics_to_compare_images
compare-image fsim issm psnr rmse sam sre ssim uiq vifp
Last synced: 13 days ago
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Comparing two images by using 9 metrics: VIFP, PSNR, SSIM, FSIM, RMSE, ISSM, SRE, SAM, UIQ.
- Host: GitHub
- URL: https://github.com/quzanh1130/multi_metrics_to_compare_images
- Owner: quzanh1130
- License: mit
- Created: 2022-12-15T08:31:57.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-02-07T16:32:06.000Z (almost 2 years ago)
- Last Synced: 2023-10-04T12:51:38.576Z (about 1 year ago)
- Topics: compare-image, fsim, issm, psnr, rmse, sam, sre, ssim, uiq, vifp
- Language: Python
- Homepage:
- Size: 8.97 MB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# compare-images
Implementation of nine evaluation metrics to access the similarity between two images and obtain the regions of the two input images that differ. The nine metrics are as follows:
* Root mean square error (RMSE),
* Peak signal-to-noise ratio (PSNR),
* Structural Similarity Index (SSIM),
* Feature-based similarity index (FSIM),
* Information theoretic-based Statistic Similarity Measure (ISSM),
* Signal to reconstruction error ratio (SRE),
* Spectral angle mapper (SAM),
* Universal image quality index (UIQ),
* Visual Information Fidelity (VIFP),
## Instructions
The following step-by-step instructions will guide you through installing this package and run evaluation using the command line tool.
**Note:** Supported python versions are 3.6, 3.7, 3.8, and 3.9.
### Install package library
```bash
pip install image-similarity-measures
```
```bash
python3 -m pip install -r requirements.txt
```
### Usage
#### Parameters
```
--org_img_path FILE_PATH Path to original input image
--pred_img_path FILE_PATH Path to predicted image
--metric METRIC select an evaluation metric (fsim, issm, psnr, rmse,
sam, sre, ssim, uiq, vifp, all) (can be repeated)
```
#### Terminal
```bash
python main.py --org_img_path FILE_PATH --pred_img_path FILE_PATH --metric METRIC
```
#### Example
```bash
python main.py --org_img_path Images/1.png --pred_img_path Images/2.png --metric all
```
## References
Müller, M. U., Ekhtiari, N., Almeida, R. M., and Rieke, C.: SUPER-RESOLUTION OF MULTISPECTRAL
SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORKS, ISPRS Ann. Photogramm. Remote Sens.
Spatial Inf. Sci., V-1-2020, 33–40, https://doi.org/10.5194/isprs-annals-V-1-2020-33-2020, 2020.
H. R. Sheikh and A. C. Bovik, “Image information and visual quality,” Image Processing, IEEE Transactions on, vol. 15, no. 2, pp. 430–444, 2006.
V. Baroncini, L. Capodiferro, E. D. Di Claudio, and G. Jacovitti, “The polar edge coherence: a quasi blind metric for video quality assessment,” EUSIPCO 2009, Glasgow, pp. 564–568, 2009.
Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2003, vol. 2, pp. 1398–1402.
Mittal, Anish, Rajiv Soundararajan, and Alan C. Bovik. "Making a completely blind image quality analyzer." Signal Processing Letters, IEEE 20.3 (2013): 209-212.