SVM regression and its application to image compression

Authors:
Runhai Jiao;Yuancheng Li;Qingyuan Wang;Bo Li
Affiliations:
Digital Media Laboratory, School of Computer Science and Engineering, Beihang University, Beijing, China;Digital Media Laboratory, School of Computer Science and Engineering, Beihang University, Beijing, China;Digital Media Laboratory, School of Computer Science and Engineering, Beihang University, Beijing, China;Digital Media Laboratory, School of Computer Science and Engineering, Beihang University, Beijing, China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

This paper proposes a new image compression algorithm which combines SVM regression with wavelet transform. Compression is achieved by using SVM regression to approximate wavelet coefficients. Based on the characteristic of wavelet decomposition, the coefficient correlation in wavelet domain is analyzed. According to the correlation characteristic at different scales and orientations, three kinds of arranging methods of wavelet coefficients are designed, which make SVM compress the coefficients more efficiently. Moreover, an effective entropy coder based on run-length and arithmetic coding is used to encode the support vectors and weights. Experimental results show that the compression performance of the algorithm achieve much improvement.