@article{
 title = {Lossy Image Compression Using Discrete Wavelet Transform and Thresholding Techniques},
 type = {article},
 year = {2013},
 keywords = {Discrete wavelet transform,Hilbert curve,Huffman coding,Image compression,Thresholding techniques},
 pages = {32-38},
 volume = {7},
 websites = {http://benthamopen.com/ABSTRACT/TOCSJ-7-32},
 month = {10},
 day = {4},
 id = {d4d62af4-004a-3a3a-b53d-3deb0324a04c},
 created = {2022-08-29T17:42:51.117Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:42:51.117Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Results of lossy image compression using wavelet transforms and several thresholding techniques are presented here. The analyzed image was divided into little sub-images and each one was decomposed in a vector following a Hilbert fractal curve. The wavelet transform was applied to each vector and some of the high frequency components were suppressed based on some threshold criteria. Different levels of wavelet decomposition and wavelet mother functions were assessed. The Huffman coding algorithm was then applied in order to reduce image weight. Simulation results have revealed that high compression ratios were obtained with the mean and the standard deviation thresholding algorithms at different levels of wavelet decomposition. © Alarcon-Aquino et al.; Licensee Bentham Open.},
 bibtype = {article},
 author = {Alarcon-Aquino, V.},
 doi = {10.2174/1874110X01307010032},
 journal = {The Open Cybernetics & Systemics Journal},
 number = {1}
}

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