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Switching of Wavelet Transforms by Neural Network for Image Compression

Switching of Wavelet Transforms by Neural Network for Image Compression

Houda Chakib, Brahim Minaoui, Abderrahim Salhi, Imad Badi
Copyright: © 2018 |Volume: 16 |Issue: 1 |Pages: 14
ISSN: 1539-2937|EISSN: 1539-2929|EISBN13: 9781522542360|DOI: 10.4018/JECO.2018010104
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MLA

Chakib, Houda, et al. "Switching of Wavelet Transforms by Neural Network for Image Compression." JECO vol.16, no.1 2018: pp.43-56. https://doi.org/10.4018/JECO.2018010104

APA

Chakib, H., Minaoui, B., Salhi, A., & Badi, I. (2018). Switching of Wavelet Transforms by Neural Network for Image Compression. Journal of Electronic Commerce in Organizations (JECO), 16(1), 43-56. https://doi.org/10.4018/JECO.2018010104

Chicago

Chakib, Houda, et al. "Switching of Wavelet Transforms by Neural Network for Image Compression," Journal of Electronic Commerce in Organizations (JECO) 16, no.1: 43-56. https://doi.org/10.4018/JECO.2018010104

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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).

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