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Image similarity metrics.

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master: CircleCI
develop: CircleCI


For full documentation visit documentation site.

Image similarity metrics are often used in image quality assessment for performance evaluation of image restoration and reconstruction algorithms. They require two images:

  • test image (image of interest)
  • reference image (image we compare against)

Such metrics produce numerical values and are widely called full/reduced-reference methods for assessing image quality.

compimg package is all about calculating similarity between images. It provides image similarity metrics (PSNR, SSIM etc.) that are widely used to asses image quality.

import numpy as np
from compimg.similarity import SSIM
some_grayscale_image = np.ones((20,20), dtype=np.uint8)
identical_image = np.ones((20,20), dtype=np.uint8)
result = SSIM().compare(some_grayscale_image, identical_image)
assert result == 1.0 # SSIM returns 1.0 when images are identical


  • common metrics for calculating similarity of one image to another
  • images are treated as numpy arrays which makes compimg compatible with most image processing packages
  • only scipy (and inherently numpy) as a dependency


compimg is available on PyPI. You can install it using pip:
pip install compimg


Keep in mind that metrics are not aware of what kind of image you are passing. If metric relies on intensity values and you have YCbCr image you should probably pass only the first channel to the computing subroutine.


If you have any problems or questions please post an issue.

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