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.
Supported by the National Defense Basic Research Fund and the Program for New Century Excellent Talents in University. The research is made in the State Key Lab of Software Development Environment.
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© 2005 Springer-Verlag Berlin Heidelberg
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Jiao, R., Li, Y., Wang, Q., Li, B. (2005). SVM Regression and Its Application to Image Compression. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_78
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DOI: https://doi.org/10.1007/11538059_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
Online ISBN: 978-3-540-31902-3
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