Volume 32 Issue 05
May  2006
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Li Yuancheng, Jiao Runhai, Li Boet al. Wavelet image compression based on support vector machines[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32(05): 598-602. (in Chinese)
Citation: Li Yuancheng, Jiao Runhai, Li Boet al. Wavelet image compression based on support vector machines[J]. Journal of Beijing University of Aeronautics and Astronautics, 2006, 32(05): 598-602. (in Chinese)

Wavelet image compression based on support vector machines

  • Received Date: 31 May 2005
  • Publish Date: 31 May 2006
  • A novel image compression algorithm that combined SVM(support vector machines) regression and wavelet transform was presented. SVM regression could learn dependency from training data and realized compression by using fewer training point (support vectors) to represent the original data. Thus, wavelet coefficients could be compressed based on this feature. Image was decomposed into subbands of different scales in using wavelet transform. The lowest subband was coded using DPCM for its great importance, and the other coarser subbands were compressed by SVM. Since the characteristic of the wavelet coefficients was various in different scales and directions, it was a key problem to design the appropriate organization method of the coefficients. Effective entropy coding technique was also studied to encode the support vectors and the corresponding weights. Experiment results demonstrate the coding efficiency of the proposed algorithm.

     

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