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The transport of images across communication paths is an expensive process. Image
compression provides an option for reducing the number of bits in transmission. This in turn
helps increase the volume of data transferred in a space of time, along with reducing the cost
required. It has become increasingly important to most computer networks, as the volume of
data traffic has begun to exceed their capacity for transmission. Traditional techniques that
have already been identified for data compression include: Predictive coding, Transform
coding and Vector Quantization.
In brief, predictive coding refers to the decorrelation of similar neighbouring pixels
within an image to remove redundancy. Following the removal of redundant data, a more
compressed image or signal may be transmitted. Transform-based compression
techniques have also been commonly employed. These techniques execute transformations on
images to produce a set of coefficients. A subset of coefficients is chosen that allows good
data representation (minimum distortion) while maintaining an adequate amount of
compression for transmission. The results achieved with a transform-based technique is highly
dependent on the choice of transformation used (cosine, wavelet, Karhunen-Loeve etc).
Finally, vector quantization techniques require the development of an appropriate codebook to
compress data. Usage of codebooks do not guarantee convergence and hence do not necessarily deliver infallible decoding accuracy.
Also the process may be very slow for large
codebooks as the process requires extensive searches through the entire codebook.
Following the review of some of the traditional techniques for image compression, it is
possible to discuss some of the more recent techniques that may be employed for data
compression.
Artificial Neural Networks (ANNs) have been applied to many problems, and have
demonstrated their superiority over traditional methods when dealing with noisy or
incomplete data. One such application is for image compression. Neural networks seem to be
well suited to this particular function, as they have the ability to preprocess input patterns to
produce simpler patterns with fewer components. This compressed information (stored in
a hidden layer) preserves the full information obtained from the external environment. Not
only can ANN based techniques provide sufficient compression rates of the data in question,
but security is easily maintained. This occurs because the compressed data that is sent along a
communication line is encoded and does not resemble its original form.
There have already been an exhaustive number of papers published applying ANNs to
image compression. Many different training algorithms and architectures have been
used. Some of the more notable in the literature are: nested training algorithms used with
symmetrical multilayer neural networks, Self organising maps, for codebook generation, principal component analysis networks,
backpropagation networks, and the
adaptive principal component extraction algorithm.
Apart from the existing technology on image compression represented by series of JPEG,MPEG and H.26x standards,
new technology such as neural networks and genetic algorithms are being developed to explore the future of
image coding. Successful applications of neural networks to vector quantization have now become well established, and
other aspects of neural network involvement in this area are stepping up to play significant roles in assisting with those
traditional technologies.
Index Terms: Matlab, source, code, neural networks, image compression, image processing, image
reconstruction, codebook, quantization.
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Figure 1. Compressed image |
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A simple and effective source code for Image Compression With Neural Networks. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Communications Toolbox and Matlab Neural Network Toolbox are required. |
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Release |
Date |
Major features |
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1.0 |
2008.10.17 |
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The authors have no relationship or partnership
with The Mathworks. All the code provided is written in Matlab
language (M-files and/or M-functions), with no dll or other
protected parts of code (P-files or executables). The code was
developed with Matlab 2006a. Matlab Image Processing Toolbox, Matlab Communications Toolbox and Matlab Neural Network Toolbox are required.
The code provided has to be considered "as is" and it is without any kind of warranty. The
authors deny any kind of warranty concerning the code as well
as any kind of responsibility for problems and damages which may
be caused by the use of the code itself including all parts of
the source code.
