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Just how to calculate the Structural Similarity Index (SSIM) between two images with Python

Just how to calculate the Structural Similarity Index (SSIM) between two images with Python

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The Structural Similarity Index (SSIM) is a perceptual metric that quantifies the image quality degradation this is certainly brought on by processing such as for instance information compression or by losses in information transmission. This metric is actually a complete reference that will require 2 pictures through the exact exact exact same shot, what this means is 2 graphically identical pictures to your eye. The 2nd image generally speaking is compressed or has a unique quality, that will be the purpose of this index. SSIM is generally found in the video clip industry, but has aswell a strong application in photography. SIM really steps the difference that is perceptual two comparable pictures. It cannot judge which of this two is way better: that needs to be inferred from once you understand which will be the one that is original that has been confronted with extra processing such as for example compression or filters.

In this specific article, we shall explain to you how exactly to calculate accurately this index between 2 pictures making use of Python.

Needs

To check out this guide you will require:

  • Python 3
  • PIP 3

With that said, why don’t we begin !

1. Install Python dependencies

Before applying the logic, it is important to install some crucial tools that are going to be employed by the logic. This tools could be installed through PIP utilizing the after demand:

These tools are:

  • scikitimage: scikit-image is an accumulation algorithms for image processing.
  • opencv: OpenCV is really a library that is highly optimized give attention to real-time applications.
  • imutils: a few convenience functions to produce basic image processing functions such as for example interpretation, rotation, resizing, skeletonization, showing Matplotlib pictures, sorting contours, detecting edges, plus much more easier with OpenCV and both Python 2.7 and Python 3. Continue reading