A novel statistical learning-based rate distortion analysis approach for multiscale binary shape coding

Zhenzhong Chen; King Ngi Ngan

doi:10.1117/12.697021

29 January 2007 A novel statistical learning-based rate distortion analysis approach for multiscale binary shape coding

Zhenzhong Chen, King Ngi Ngan

Proceedings Volume 6508, Visual Communications and Image Processing 2007; 65081J (2007) https://doi.org/10.1117/12.697021
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

Zhenzhong Chen and King Ngi Ngan "A novel statistical learning-based rate distortion analysis approach for multiscale binary shape coding", Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65081J (29 January 2007); https://doi.org/10.1117/12.697021

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