An Image Compression Method Based on Wavelet Transform and Neural Network
Abstract: Image compression is
to compress the redundancy between the pixels as much as possible by using the
correlation between the neighborhood pixels so as to reduce the transmission
bandwidth and the storage space. This paper applies the integration of wavelet
analysis and artificial neural network in the image compression, discusses its
performance in the image compression theoretically, analyzes the
multiresolution analysis thought, constructs a wavelet neural network model
which is used in the improved image compression and gives the corresponding
algorithm. Only the weight in the output layer of the wavelet neural network
needs training while the weight of the input layer can be determined according
to the relationship between the interval of the sampling points and the
interval of the compactly-supported intervals. Once determined, training is
unnecessary, in this way, it accelerates the training speed of the wavelet
neural network and solves the problem that it is difficult to determine the
nodes of the hidden layer in the traditional neural network. The computer simulation
experiment shows that the algorithm of this paper has more excellent
compression effect than the traditional neural network method.
Author: Suqing Zhang, Aiqiang
Wang
Journal Code: jptkomputergg150083
