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Proceedings Paper

A novel statistical learning-based rate distortion analysis approach for multiscale binary shape coding
Author(s): Zhenzhong Chen; King Ngi Ngan
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Paper Abstract

In this paper, we propose a statistical learning based approach to analyze the rate-distortion characteristics of multiscale binary shape coding. We employ the polynomial kernel function and incorporate rate-distortion related features for our support vector regression. ε-Insensitive loss function is chosen to improve the estimation robustness. The parameter tuning is also studied. Moreover, we discuss the feature selection which helps to improve the estimation accuracy. Comparing to the traditional method, our proposed framework provides better rate distortion estimation not only on simple shapes but also on complex shapes.

Paper Details

Date Published: 29 January 2007
PDF: 12 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65081J (29 January 2007); doi: 10.1117/12.697021
Show Author Affiliations
Zhenzhong Chen, The Chinese Univ. of Hong Kong (Hong Kong China)
King Ngi Ngan, The Chinese Univ. of Hong Kong (Hong Kong China)

Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)

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