Study of a New Wavelet Neural Network of Image Compression Simulation

Xue Zhang Zhao; Qun Qi

doi:10.4028/www.scientific.net/AMR.490-495.623

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 490-495Study of a New Wavelet Neural Network of Image...

Study of a New Wavelet Neural Network of Image Compression Simulation

Abstract:

In the practical need in order to make the most effective image compression in this paper, a new image compression used wavelet neural network model, and gives the corresponding calculation formula and algorithm procedures, By using wavelet transform good time-frequency local area on the characteristics and neural network self-learning function characteristics, overcome traditional BP neural network of hidden-layer points are difficult to be determined and the convergence speed is slow and easy to converge to a local minimum points shortcomings. The results of the simulation experiment prove wavelet neural network image compression characteristic and the convergence speed are much better than traditional BP neural network, and show that the algorithm is effective and feasible.

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Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

623-627

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.490-495.623

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Online since:

March 2012

Authors:

Xue Zhang Zhao, Qun Qi

Keywords:

BP Neural Network (BPNN), Image Compression, Wavelet Neural Network (WNN)

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