Image-compression-using-K-means-algorithm

K-means is a clustering algorithm used to group similar data points in a dataset. The basic idea is to group similar pixels of an image together and represent them using their mean value. The motivation for this project is that as new digital data is generated every single day, it is essential to reduce the cost for storage or transmission of images.

I considered a (640, 427) RGB image. All the pixel values of the image are clustered into ‘k’ clusters until the algorithm converges. Then, each pixel value is replaced with the centroid value of its cluster. These new values are used to generate the compressed image when clusters = ‘k’.

The image was converted to a 2D array of pixel values, where each row represents a pixel and each column represents a color channel.

I considered cluster values K = 2, 5, 10, 20, 50, 100. The size of the compressed images decreased compared to the original size of the image. The quality of the compressed image increases as the number of clusters increase. As the number of clusters increased, the time taken to compress the images increased.

Some advantages are it is easy to implement, and the algorithm works particularly well for images with large areas of uniform color. And some disadvantages are loss of image quality and may not perform well on complex images.

Image by sarajuggernaut from Pixabay