In the past years our research group has been investigating algorithms that drastically depart from “Motion Compensated DPCM/Transform (MC-DPCM)”. Our prime intention is to allow ourselves a completely “fresh view” on how non-linear dependencies between pixels and motion in images and video can be described and harvested for compression. To this end we employ non-linear machine learning algorithms that explore dependencies between vast amounts of pixels in images and video sequences. Our current approaches are based on non-linear Kernel methods, Steered Mixture of Experts networks (MoE) and Restricted Boltzmann Machines and show strong resemblance to recent work on deep neural networks. Our experiments give hope that our networks may provide far better visual quality compared to DPCM/Transform approaches in the long run - presently we can illustrate this at very low and ultra low rates only.
The Communication Systems Group of the TU Berlin, led by Prof. Dr. Thomas Sikora, has a strong track record in multimedia analysis and processing with more than 50 publications in this field, and was involved in many national and international funded projects related to multimedia analysis and processing. The following list gives an overview of research areas we are involved in:
The Communication Systems Group of the TU Berlin, led by Prof. Dr. Thomas Sikora, employed their results of multimedia analysis and processing in a couple of innovative multimedia applications and was involved in many national and international funded projects related to multimedia applications. The following list gives an overview of research areas we are involved in: