Wavelet image compression by using hybrid kernel SVM

In this paper, we proposed a way through combining the support vector machines (SVM) with hybrid kernel and wavelet transform to compress the image. SVM regression could learn dependency from training data and realized compression by using fewer training point (support vectors) to represent the orig...

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Bibliographic Details
Published in2008 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 3056 - 3060
Main Authors Jia-Ming Chen, Lei Li, Ling-Ye Nie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2008
Subjects
Hybrid Kernel
Image coding
Image Compression
Kernel
Polynomials
Support vector machines
Transforms
VC-dimension
Wavelet domain
Wavelet Transform
Wavelet transforms
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ISBN1424420954
9781424420957
ISSN2160-133X
DOI10.1109/ICMLC.2008.4620932

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Summary:In this paper, we proposed a way through combining the support vector machines (SVM) with hybrid kernel and wavelet transform to compress the image. SVM regression could learn dependency from training data and realized compression by using fewer training point (support vectors) to represent the original data and eliminate the redundancy. Wavelet coefficients could be compressed based on this feature. Further more, the hybrid kernel applied can enhance the compress efficient and improve the picture quality by controlling the VC-dimension (Tan, 2004) of SVM. At last, we use the arithmetic coding to encode the dates from the output of the SVM and finish the image compression.
ISBN:1424420954
9781424420957
ISSN:2160-133X
DOI:10.1109/ICMLC.2008.4620932