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

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The Structural Similarity Index (SSIM) is just a perceptual metric that quantifies the image quality degradation this is certainly brought on by processing such as for example information compression or by losses in information transmission. This metric is simply 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 that is human. The second image generally speaking is compressed or has an alternative quality, which will be the aim of this index. SSIM is normally utilized in the video clip industry, but has too an application that is strong photography. SIM really steps the perceptual distinction between two write my essay for me comparable pictures. It cannot judge which for the two is way better: that really must be inferred from once you understand that is the initial one and which was subjected to extra processing such as for example compression or filters.

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


To follow along with this guide you shall require:

That being said, let’s get going !

1. Install Python dependencies

Before applying the logic, you will have to install some essential tools that should be employed by the logic. This tools could be set up through PIP aided by the after demand:

These tools are:

This guide will focus on any platform where Python works (Ubuntu/Windows/Mac).

2. Write script

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

X, Y: ndarray

Pictures of every dimensionality.

win_size: none or int

The side-length associated with the sliding screen found in comparison. Needs to be a value that is odd. If gaussian_weights does work, this is certainly ignored additionally the screen size will rely on sigma.

gradientbool, optional

If real, also get back the gradient with regards to Y.

data_rangefloat, optional

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

multichannelbool, optional

If real, treat the dimension that is last of array as stations. Similarity calculations are done individually for every single channel then averaged.

gaussian_weightsbool, optional

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

fullbool, optional

If real, additionally get back the entire structural similarity image.


The mean structural similarity over the image.


The gradient for the similarity that is structural between X and Y [2]. That is just came back if gradient is placed to real.


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

As first, we shall browse the images with CV through the supplied arguments and then we’ll use a black colored and white filter (grayscale) therefore we’ll apply the mentioned logic to those pictures. Produce the following script namely script.py and paste the logic that is following the file:

This script will be based upon the rule posted by @mostafaGwely about this repository at Github. The rule follows precisely the exact same logic declared from the repository, nonetheless it eliminates a mistake of printing the Thresh of the pictures. The production of operating the script using the pictures using the following command:

Will create the output that is followingthe demand within the photo utilizes the quick argument description -f as –first and -s as –second ):

The algorithm will print a sequence particularly “SSIM: $value”, you could change it out while you want. The value of SSIM should be obviously 1.0 if you compare 2 exact images.

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