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All image quality metrics you need in one package.

Project description

Sewar

Sewar is a python package designed to compare similarity between images using different metrics. You can check the documentation here.

Implemented metrics

  • Mean Squared Error (MSE)
  • Root Mean Sqaured Error (RMSE)
  • Peak Signal-to-Noise Ratio (PSNR) [1]
  • Structural Similarity Index (SSIM) [1]
  • Universal Quality Image Index (UQI) [2]
  • Multi-scale Structural Similarity Index (MS-SSIM) [3]
  • Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS) [4]
  • Spatial Correlation Coefficient (SCC) [5]
  • Relative Average Spectral Error (RASE) [6]
  • Spectral Angle Mapper (SAM) [7]
  • Visual Information Fidelity (VIF) [8]

Installation

Just as simple as

pip install sewar

Example usage

a simple example to use UQI

>>> from sewar.metrics import uqi
>>> uqi(img1,img2)
0.9586952304831419

References

[1] "Image quality assessment: from error visibility to structural similarity." 2004)
[2] "A universal image quality index." (2002)
[3] "Multiscale structural similarity for image quality assessment." (2003)
[4] "Quality of high resolution synthesised images: Is there a simple criterion?." (2000)
[5] "A wavelet transform method to merge Landsat TM and SPOT panchromatic data." (1998)
[6] "Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition." (2004)
[7] "Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm." (1992)
[8] "Image information and visual quality." (2006)

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