Abstract
A Comparative Analysis of Machine Learning Methods for Image Compression
Highlights
Digital media accounts for about 80% of the world’s internet traffic, according to Cisco’s Annual Internet Report [1]
Eigen Value based K-Means Clustering: this method is not on the bleeding-edge of machine learning techniques for image compression, it does show some promise for replacing traditional methods of image compression
Generative Adversarial Networks: Mentzer et al had proposed an extremely promising method [5] based on the usage of Generative Adversarial Networks (GANs), or Conditional Generative Adversarial Networks
Summary
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; in recent times, advances in machine learning have achieved great success in many computer vision tasks, and are gradually being used in image compression. 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
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