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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.






Figure 1. Compressed image

A simple and effective source code for Image Compression With Neural Networks.

Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Communications Toolbox and Matlab Neural Network Toolbox are required.

Major features


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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.

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