Switching of Wavelet Transforms by Neural Network for Image Compression

Houda Chakib, Brahim Minaoui, Abderrahim Salhi and Imad Badi
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Houda Chakib: Physics Sciences Department, Sultan Moulay Slimane University, Beni Mellal, Morocco
Brahim Minaoui: Physics Sciences Department, Sultan Moulay Slimane University, Beni Mellal, Morocco
Abderrahim Salhi: Physics Sciences Department, Sultan Moulay Slimane University, Beni Mellal, Morocco
Imad Badi: Computer Sciences Department, Sultan Moulay Slimane University, Beni Mellal, Morocco

Journal of Electronic Commerce in Organizations (JECO), 2018, vol. 16, issue 1, 43-56

Abstract: Nowadays, digital images compression requires more and more significant attention of researchers. Even when high data rates are available, image compression is necessary in order to reduce the memory used, as well the transmission cost. An ideal image compression system must yield high-quality compressed image with high compression ratio. In this article, a neural network is implemented for image compression using the feature of wavelet transform. The idea is that a back-propagation neural network can be trained to relate the image contents to its ideal compression method between two different wavelet transforms: orthogonal (Haar) and biorthogonal (bior4.4).

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jeco00:v:16:y:2018:i:1:p:43-56

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