Conventional video compression methods employ a linear transform and block motion model, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized individually due to the combinatorial nature of the end-to-end optimization problem. Learned video compression allows end-to-end rate-distortion (R-D) optimized training of nonlinear transform, motion compensation and entropy model simultaneously. I will first review recent advances in learned image compression. Then, I will discuss the state-of-the-art in learned video compression and present recent results on learned hierarchical bi-directional video compression that combines the benefits of hierarchical bi-directional motion compensation and~end-to-end rate-distortion optimization
A. Murat Tekalp received BS degrees in Electrical Engineering and Mathematics from Bogazici University in 1980 with high honors, and the M.S. and Ph.D. degrees in Electrical, Computer, and Systems Engineering from Rensselaer Polytechnic Institute (RPI) respectively. Since June 2001, he has been a Professor at Koc University, Istanbul, Turkey. He was the Dean of Engineering at Koç University between 2010-2013. His research interests are in the area of digital image and video processing, including video compression and streaming, motion-compensated filtering, super-resolution, video segmentation, object tracking, content-based video analysis and summarization, 3D video processing, deep learning for image and video processing, video streaming and real-time video communications services, and software-defined networking. Prof. Tekalp is a Fellow of IEEE and a member of Turkish Academy of Sciences and Academia Europaea.