Color quantization using k-means

#artificialintelligence 

The idea is to give a grasp on some concepts that are necessary to understand what comes next without being too much detailed as a more detailed explanation is out of the scope of this post. Feel free to skip these parts if you already know what they're talking about. As previously anticipated a color can be represented as a point in an n-dimensional space called color space. Most commonly the space is 3-dimensional and the coordinates in that space can be used to encode a color. There are many color spaces for different purposes and with different gamut (range of colors), and in each of them it is possibile to define a distance metric that quantifies the color difference. The most common and easiest distance metric used is the Euclidean distance which is used in RGB and Lab color spaces. The RGB (abbreviation of red-green-blue) color space is by far the most common and used color space. The idea is that it is possibile to create colors by combining red, green and blue. A color in RGB is usually encoded as a 3-tuple of 8 bits each, hence each dimension takes a value within the range [0, 255] where 0 stands for absence of color while 255 stands for full presence of color.