H.263 to H.264 transconding using data mining

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Fernández-Escribano G, Bialkowski J, Kalva H, Cuenca P, Orozco-Barbosa L, Kaup A
Publication year: 2007
Volume: 4
Pages range: 81-84
ISBN: 9781424414376
ISSN: 2381-8549
Language: English


Abstract


In this paper, we propose the use of data mining algorithms to create a macroblock partition mode decision algorithm for inter-frame prediction, to be used as part of a high-efficient H.263 to H.264 transcoder. We use machine learning tools to exploit the correlation and derive decision trees to classify the incoming H.263 MC residual into one of the several coding modes in H.264. The proposed approach reduces the H.264 MB mode computation process into a decision tree lookup with very low complexity. Experimental results show that the proposed approach reduces the inter-prediction complexity by as much as 60% while maintaining the coding efficiency. ©2007 IEEE.


FAU Authors / FAU Editors

Kaup, André Prof. Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung


How to cite

APA:
Fernández-Escribano, G., Bialkowski, J., Kalva, H., Cuenca, P., Orozco-Barbosa, L., & Kaup, A. (2007). H.263 to H.264 transconding using data mining. In Proceedings of the 14th IEEE International Conference on Image Processing, ICIP 2007 (pp. 81-84). San Antonio, Texas, US.

MLA:
Fernández-Escribano, G., et al. "H.263 to H.264 transconding using data mining." Proceedings of the 14th IEEE International Conference on Image Processing, ICIP 2007, San Antonio, Texas 2007. 81-84.

BibTeX: 

Last updated on 2019-24-05 at 22:38