A Novel Method of Macro Block Motion Prediction Using Particle Filter

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As we all know, there is conspicuous connectivity shared by neighboring image block attributes between consecutive frames. In this paper, we used particle filter to estimate the motion curve for every macro blocks during a certain time period, and then developed a video reconstruction computing method by doing the mean and interpolation processes. Compared with MVs, the video constructed by our method can remain more structural features existed in the original image sequences, and has great potential in increasing coding efficiency.

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447-451

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February 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Guillaume Laroche, Joel Jung, and Beatrice Pesquet-Popescu: RD Optimized Coding for Motion Vector Predictor Selection, IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 9, p.1247–1257, Sept. (2008).

DOI: 10.1109/tcsvt.2008.928882

Google Scholar

[2] Hui Yuan, Y. Chang, Z. Lu, Y. Ma,: Model Based Motion Vector Predictor for Zoom Motion, IEEE Signal Processing Letters, Volume 17, Issue 9, September (2010).

DOI: 10.1109/lsp.2010.2055051

Google Scholar

[3] Reuben A. Farrugia: Improving Motion Vector Prediction using Linear Regression, 2012 5th International Symposium on Communications Control and Signal Processing , Page(s): 1 – 4.

DOI: 10.1109/isccsp.2012.6217750

Google Scholar

[4] A.J. Huang, A Tutorial on Bayesian Estimation and Tracking Techniques Applicable to Non-linear and Non-Gaussian Process, MITRE Technique Report, Jan (2005).

Google Scholar

[5] Arnaud. D, Simon. G, Chistophe. A, On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and Computing, vol. 10 , pp.197-208, (2000).

Google Scholar

[6] Michael. I, Andrew. B, Condensation-Conditional Density Propagation for Visual Tracking, International Journal of Computer Vision, vol. 29(1), pp.5-28, (1998).

Google Scholar

[7] F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P. Nordlund, Particle filters for positioning, navigation, and tracking, IEEE Transactions on Signal Processing, vol. 50, pp.425-435, Feb. (2002).

DOI: 10.1109/78.978396

Google Scholar

[8] Haykin, S.; Huber, K.; Zhe Chen, Bayesian sequential state estimation for MIMO wireless communications, Proceedings of the IEEE, Vol. 92, Issue 3, p.439 – 454, Mar (2004).

DOI: 10.1109/jproc.2003.823143

Google Scholar

[9] Bing Zeng, Jingjing Fu, Directional Discrete Cosine Transforms—A New Framework for Image Coding, IEEE Transactions on Circuits and Systems for Video Technology, VOL. 18, NO. 3, MARCH (2008).

DOI: 10.1109/tcsvt.2008.918455

Google Scholar