文章基本信息

标题：A Proposed Approach for Image Compression based on Wavelet Transform and Neural Network
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作者：Houda Chakib ; Brahim Minaoui ; Mohamed Fakir 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2017
卷号：8
期号：9
DOI：10.14569/IJACSA.2017.080959
出版社：Science and Information Society (SAI)
摘要：Over the last years, wavelet theory has been used with great success in a wide range of applications as signal de-noising and image compression. An ideal image compression system must yield high-quality compressed image with high compression ratio. This paper attempts to find the most useful wavelet function to compress an image among the existing members of wavelet families. Our idea is that a backpropagation neural network is trained to select the suitable wavelet function between the two families: orthogonal (Haar) and biorthogonal (bior4.4), to be used to compress an image efficiently and accurately with an ideal and optimum compression ratio. The simulation results indicated that the proposed technique can achieve good compressed images in terms of peak signal to noise ratio (PSNR) and compression ratio (t) in comparison with random selection of the mother wavelet.
关键词：Haar wavelet transform; biorthogonal wavelet; backpropagation neural network; scaled conjugate gradient algorithm