Image compression with autoencoder & the discrete cosine transform

Millions of pictures are captured each year for different purposes, making digital images an ubiquitous part of modern day life. This proliferation was made possible by image compression standards since these images need to be stored somewhere and somehow. In this thesis I explore the use of machine learning together with the discrete cosine transform to compress images. An autoencoder was developed which was able to compress images with results comparable to the JPEG standard. The results lend credence to the hypothesis that the combination of a simple autoencoder and the discrete cosine transform offers a simple and effective method for image compression