@INPROCEEDINGS{1536Jongebloed2018,
	AUTHOR = {Rolf Jongebloed and Ruben Verhack and Lieven Lange and Thomas Sikora},
	TITLE = {Hierarchical Learning of Sparse Image Representations using Steered Mixture-of-Experts},
	BOOKTITLE = {2018 IEEE International Conference on Multimedia Expo Workshops (ICMEW)},
	YEAR = {2018},
	MONTH = jul,
	ORGANIZATION = {IEEE},
	ADDRESS = {San Diego, CA, USA},
	PDF = {http://elvera.nue.tu-berlin.de/files/1536Jongebloed2018.pdf},
	URL = {http://elvera.nue.tu-berlin.de/files/1536Jongebloed2018.pdf},
	ABSTRACT = {Previous research showed highly efficient compression
results for low bit-rates using Steered Mixture-of-Experts
(SMoE), higher rates still pose a challenge due to the non-
convex optimization problem that becomes more difficult
when increasing the number of components. Therefore, a
novel estimation method based on Hidden Markov Random
Fields is introduced taking spatial dependencies of neighbor-
ing pixels into account combined with a tree-structured split-
ting strategy. Experimental evaluations for images show that
our approach outperforms state-of-the-art techniques using
only one robust parameter set. For video and light field mod-
eling even more gain can be expected.}
}