@INPROCEEDINGS{1502Lange2016,
	AUTHOR = {Lieven Lange and Ruben Verhack and Thomas Sikora},
	TITLE = {Video Representation and Coding Using a Sparse Steered Mixture-of-Experts Network},
	BOOKTITLE = {Picture Coding Symposium},
	YEAR = {2016},
	MONTH = dec,
	PUBLISHER = {IEEE},
	PAGES = {1--5},
	ADDRESS = {Nuremberg, Germany},
	NOTE = {In IEEE-Explore zugefügt am 24 April 2017!
Electronic ISSN: 2472-7822 
DOI: 10.1109/PCS.2016.7906369},
	PDF = {http://elvera.nue.tu-berlin.de/files/1502Lange2016.pdf},
	URL = {http://elvera.nue.tu-berlin.de/files/1502Lange2016.pdf},
	ABSTRACT = {In this paper, we introduce a novel approach for
video compression that explores spatial as well as temporal
redundancies over sequences of many frames in a unified
framework. Our approach supports “compressed domain vision”
capabilities. To this end, we developed a sparse Steered Mixture of-
Experts (SMoE) regression network for coding video in the
pixel domain. This approach drastically departs from the established
DPCM/Transform coding philosophy. Each kernel in the
Mixture-of-Experts network steers along the direction of highest
correlation, both in spatial and temporal domain, with local and
global support. Our coding and modeling philosophy is embedded
in a Bayesian framework and shows strong resemblance to
Mixture-of-Experts neural networks. Initial experiments show
that at very low bit rates the SMoE approach can provide
competitive performance to H.264.}
}