An Introduction to Image Compression, Old and New
Image compression, as a subset of image processing, intersects many areas of applied mathematics. In this talk, I will describe and compare the "classic" view of image compression, such as the JPEG algorithm and it's variants, against the "new kid on the block", namely compression using neural networks (NN). I will survey the relative merits of NN-based compression algorithms, provide a run-down of the inner workings, and discuss some of their flaws. We'll see that the neural network approach promises impressive performance gains over traditional image compression algorithms, though some hurdles still remain.
Chris Finlay is a research scientist at Deep Render, a UK startup focused on AI-based image and video compression. His background is in applied mathematics, but has spent time dabbling in machine leaning and computer science. Prior to moving to industry, he worked as a post-doc in machine learning, mainly researching neural ODEs, generative modeling, and the robustness of deep learning computer vision algorithms. He obtained a PhD in applied mathematics from McGill University, where he studied numerical methods for nonlinear elliptic PDEs.