Open AccessA SURVEY ON WAVELET NETWORK, MULTI LIBRARY WAVELET NETWORK TRAINING, 1D-2D FUNCTION APPROXIMATION AND A NEW IMAGE COMPRESSION METHOD
Author(s) -
Wajdi Bellil,
Chokri Ben Amar,
Adel M. Alimi
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.8.1.659
Subject(s) - wavelet , chrominance , computer science , artificial intelligence , image compression , wavelet transform , luminance , wavelet packet decomposition , discrete wavelet transform , pattern recognition (psychology) , computer vision , mathematics , image (mathematics) , image processing
This paper presents an original architecture of Wavelet Neural Network (WNN) based on multi Wavelets activation function and uses a selection method to determine a set of best wavelets whose centers and dilation parameters are used as initial values for subsequent training library WNN for color image compression and coding which consists to transform an RGB image into Luminance-Chrominance space and then segment the luminance in a set of m blocks n by n pixels. These blocks should be transferred row by row (1D input vector) to the input of our wavelet network. Every input vector will be considered as unknown functional mapping and then it will be approximated by the network.