Matlab is a registered trademark of The Mathworks, Inc.


 Advanced Source Code . Com

 
 
HOME SOURCE CODE SOFTWARE INFO SUPPORT CONTACT US
 
Source code for fingerprint recognition, face recognition and much more


Software Info    About us     
Go To Matlab Official Website

.: Click here to download :.

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.

Release
Date
Major features
1.0

2008.10.17



We recommend to check the secure connection to PayPal, in order to avoid any fraud.
This donation has to be considered an encouragement to improve the code itself.

Image Compression With Neural Networks - Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 95 EUROS (less than 133 U.S. Dollars).

Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Image Compression With Neural Networks.

Alternatively, you can bestow using our banking coordinates:
Name :
Luigi Rosa
Address :
Via Pozzo Strada 5 10139 Torino Italy
Bank name:
Poste Italiane
Bank address:
Viale Europa 190 00144 Roma Italy
IBAN (International Bank Account Number) :
IT-50-V-07601-03600-000058177916
BIC (Bank Identifier Code) :
BPPIITRRXXX

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.

New - AI Trader
 Stock Price Trend Forecasting An emerging trading market is represented by binary options. Binary options are a convenient way of investments as they donít require a trader to forecast actual quotes.
 
New - Speaker Verification System
 Text-Independent Speaker Authentication There are two major applications of speaker recognition technologies and methodologies. If the speaker claims to be of a certain identity and the voice is used to verify this claim, this is called verification or authentication.
 
New - Java Face Recognition
 Java-based Biometric Authentication System Face recognition is essential in many applications, including mugshot matching, surveillance, access control and personal identification, and forensic and law enforcement applications.
 
New - Software References
 Papers and lectures A list of papers that included Advanced Source Code .Com in the references section. If you have written a paper where our software is cited in the references list please email us and your work will be published at our web site.
 
New - White Papers
 High Capacity Wavelet Watermarking Using CDMA Multilevel Codes This paper proposes a technique based on CDMA and multilevel coding in order to achieve a high capacity watermarking scheme. The bits of watermark are grouped together and for each sequence a different modulation coefficient is used.
 
New - WebCam Face Identification
 Face Recognition Based on Fractional Gaussian Derivatives Local photometric descriptors computed for interest regions have proven to be very successful in applications such as wide baseline matching, object recognition, texture recognition, image retrieval, robot localization, video data mining, building panoramas, and recognition of object categories.
 
New - Speaker Recognition System
 Source code for speaker recognition
Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves.
 
New - Speech Recognition System
 Source code for isolated words recognition
Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy.
 



The MathWorks, Inc. Google NeuralNetworks.It Octave Scilab The R Project for Statistical Computing Python Other available resources English Dictionary Download .Com
 
Software Info    About us