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Video Representation and Coding Using a Sparse Steered Mixture-of-Experts Network
Citation key 1502Lange2016
Author Lieven Lange and Ruben Verhack and Thomas Sikora
Title of Book Picture Coding Symposium
Pages 1–5
Year 2016
Address Nuremberg, Germany
Month dec
Note In IEEE-Explore zugefügt am 24 April 2017! Electronic ISSN: 2472-7822 DOI: 10.1109/PCS.2016.7906369
Publisher IEEE
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.
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