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Showing posts from December, 2019

Image Compression and Color Quantization using K-Means Clustering

In this post, you'll able to compress an image of higher size relatively to a smaller size. Here size I mean the image's memory consumption, not the aspect ratio (though it is also somewhat related to the size). Before we begin, let's be familiar with what Image Compression, Color Quantization and K-Means Clustering is. Basically  K-Means Clustering  is used to find the central value (centroid) for k  clusters of data. Then each data point is assigned to the cluster whose center is nearest to k . Then, a new centroid is calculated for each of the k  clusters based upon the data points that are assigned in that cluster. In our case, the data points will be Image pixels. Assuming that you know what pixels are, these pixels actually comprises of 3 channels, Red, Green and Blue . Each of these channels' have intensity ranging from 0 to 255, i.e., altogether 256. So as a whole, total number of colors in each pixel is, 256 x 256 x 256.  Each pixel(color) has 2^8 colors