conference paper

Conference/ProceedingsPicture Coding Symposium
Start date04.12.2016
End date07.12.2016
Author(s)Lieven Lange, Ruben Verhack, Thomas Sikora
TitleVideo Representation and Coding Using a Sparse Steered Mixture-of-Experts Network
AbstractIn 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.
Key wordsvideo coding, steered mixture of experts, video representation, video descriptor