H.263 to H.264 transconding using data mining
Fernández-Escribano G, Bialkowski J, Kalva H, Cuenca P, Orozco-Barbosa L, Kaup A (2007)
Publication Language: English
Publication Status: Published
Publication Type: Conference contribution, Conference Contribution
Publication year: 2007
Book Volume: 4
Pages Range: 81-84
Article Number: 4379959
Event location: San Antonio, Texas
ISBN: 9781424414376
DOI: 10.1109/ICIP.2007.4379959
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.
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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.
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