Abstract
In this paper, we present a new image compression algorithm which combines Wavelet Support Vector Machines (WSVM) learning with the wavelet transform. Based on the characteristic of wavelet transform, Daubechies 9/7 wavelet has been used to transform the image and the wavelet coefficients are trained with WSVM using translation-invariant wavelet kernels. Compression is achieved by using WSVM learning to approximate wavelet coefficients with the predefined level of accuracy. A minimal number of coefficients (support vectors) are then encoded by an effective entropy coder based on run-length and arithmetic coding. Experimental results show that the proposed method gains better performance than that of existing compression algorithm.
Supported by the Doctor Degree Teacher Research Fund in North China Electric Power University and the Natural Science Foundation of Hebei province (F2007001042).
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Li, Y., Hu, H. (2007). Image Compression Using Wavelet Support Vector Machines. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_93
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DOI: https://doi.org/10.1007/978-3-540-74171-8_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74170-1
Online ISBN: 978-3-540-74171-8
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