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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 really a perceptual metric that quantifies the image quality degradation that is brought on by processing such as for instance data compression or by losses in information transmission. This metric is actually a full reference that will require 2 pictures through the exact exact same shot, what this means is 2 graphically identical pictures into the eye. The 2nd image generally speaking is compressed or has another type of quality, that will be the purpose of this index. SSIM is generally found in the movie industry, but has also an application that is strong photography. SIM really steps the difference that is perceptual two similar images. It cannot judge which regarding the two is way better: that must definitely be inferred from once you understand which will be the one that is original which includes been confronted with extra processing such as for instance compression or filters.

In this essay, we will explain to you just how to compute this index between 2 pictures making use of Python.


To check out this guide you will require:

  • Python 3
  • PIP 3

With that said, allow’s begin !

1. Install Python dependencies

Before applying the logic, it is important to install some tools that are essential will soon be utilized by the logic. This tools may be set up through PIP with all the after demand:

These tools are:

  • scikitimage: scikit-image is an accumulation algorithms for image processing.
  • opencv: OpenCV is just a library that is highly optimized concentrate on real-time applications.
  • imutils: a few convenience functions to help make basic image processing functions such as for instance interpretation, rotation, resizing, skeletonization, showing Matplotlib pictures, sorting contours, detecting sides, and even more easier with OpenCV and both Python 2.7 and Python 3.

This guide shall work with any platform where Python works (Ubuntu/Windows/Mac).

2. Write script

The logic to compare the pictures would be the following one. Utilizing the compare_ssim approach to the measure module of Skimage. This process computes the mean similarity that is structural between two pictures. It gets as arguments:

X, Y: ndarray

Images of every dimensionality.

win_size: none or int

The side-length regarding the sliding screen found in comparison. Needs to be an odd value. If gaussian_weights holds true, this might be ignored additionally the screen size shall be determined by sigma.

gradientbool, optional

If real, additionally get back the gradient with respect to Y.

data_rangefloat, optional

The information selection of the input image (distance between minimal and maximum feasible values). By standard, this might be calculated through the image data-type.

multichannelbool, optional

If True, treat the last dimension of this array as networks. Similarity calculations are done separately for every channel then averaged.

gaussian_weightsbool, optional

If real, each spot has its mean and variance spatially weighted by way of a normalized gaussian kernel of width sigma=1.5.

fullbool, optional

If real, additionally get back the total similarity image that is structural.


The mean structural similarity over the image.


The gradient of this similarity that is structural between X and Y [2]. This might be just returned if gradient is defined to real.


The SSIM that is full image. That is just came back if complete is placed to real.

As first, we’re going to browse the pictures with CV through the supplied arguments so we’ll use a black colored and white filter (grayscale) essay writer and now we’ll apply the mentioned logic to those pictures. Produce the script that is following and paste the after logic on the file:

This script will be based upon the rule posted by @mostafaGwely on this repository at Github. The rule follows precisely the exact same logic declared from the repository, nonetheless it removes an error of printing the Thresh of the pictures. The production of operating the script utilizing the images using the following command:

Will create the output that is followingthe demand into the image makes use of the quick argument description -f as –first and -s as –second ):

The algorithm will print a sequence specifically “SSIM: $value”, you could change it out while you want. In the event that you compare 2 precise pictures, the worthiness of SSIM should always be demonstrably 1.0.

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