A Comparative Analysis of Machine Learning Methods for Image Compression

Abstract

Cameras are everywhere in today's world, from security cameras to smartphone cameras and webcams. All of these cameras produce an endless stream of images, which often need to be compressed for efficient storage and/or transmission. Image compression has been researched upon for many decades; however, in recent times, advances in machine learning have achieved great success in many computer vision tasks, and are now gradually being used in image compression. In this paper, we compare techniques involving Eigen Value based K-means clustering, GANs, CAEs, and the Gaussian Mixture Model using Bits Per Pixel(bpp) and PSNR(dB) as metrics