direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Inhalt des Dokuments

Wissenschaftliche Veröffentlichungen

Video Representation and Coding Using a Sparse Steered Mixture-of-Experts Network
Zitatschlüssel 1502Lange2016
Autor Lieven Lange and Ruben Verhack and Thomas Sikora
Buchtitel Picture Coding Symposium
Seiten 1–5
Jahr 2016
Adresse Nuremberg, Germany
Monat dec
Notiz In IEEE-Explore zugefügt am 24 April 2017! Electronic ISSN: 2472-7822 DOI: 10.1109/PCS.2016.7906369
Verlag IEEE
Zusammenfassung 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.
Link zur Publikation Download Bibtex Eintrag

Zusatzinformationen / Extras


Schnellnavigation zur Seite über Nummerneingabe