MATLAB SIMULATION FOR IMPROVED IMAGE COMPRESSION USING BACKPROPAGATION NETWORKS

H.N.Gunasinghe-AS2010379

CSC 363 1.5 Research Methodologies and Scientific ComputingDepartment of Computer Science and Statistics , USJP

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OVERVIEW

Problem Identification Introduction Methodology Limitations Summary Bibliography

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PRACTICAL PROBLEM

The development of Internet and multimedia technologies that grow exponentially, resulting in the amount of information managed by computer is necessary.

This causes serious problems in storage and transmission image data.

Therefore, should be considered a way to compress image data so that the storage capacity required will be smaller.

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INTRODUCTION (1)

Image Compression using Artificial Neural Networks (ANN) is a topic where research is being carried out in various directions towards achieving a generalized and economical network.

Feed-forward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression.

Qie et al. in 1994 proposed an image compression scheme using the Backpropagation algorithm.

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INTRODUCTION (2)

NEURAL NETWORK

High PSNR

Centralized backpropaga

tion Low MSE

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INTRODUCTION (3)

MATLAB

IMAGE

COMPRESSI

ON

NEURAL

NETWORK

MATLAB is consist with NN toolbox

MATLAB is consist with image compression toolbox

Proposed simulation can be easily implemented with MATLAB

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INTRODUCTION (4)

Topic: MATLAB system for image compression using

neural networks Question:

To find out how the visual quality of the compressed image is preserved with proposed system

Significance: To make a better image compression system

using neural networks.

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METHODOLOGY (1)

1. Image segmentation.2. Choose network parameters.

I. Input parametersII. Output parameters (target)III. Activation functionIV. Number of layersV. Number of neurons in each layer.

3. Build and train the NN.4. Reconstruct images.

MATLAB

IMAGE COMPRESSI

ON

NEURAL

NETWORK

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METHODOLOGY (2)

Analyzing and Evaluation

Test NN. Apply NN with novel images. Evaluate performance.

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LIMITATIONS

Small training image set Small testing image set Only grayscale images Simulation in a single computer, so that it

will consume considerable computational time

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SUMMARY

Image compression using back propagation is a wide research area.

Improvements to the back propagation can be easily implemented with MATLAB.

Simulation for back propagation will construct a foundation before the actual implementation.

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BIBLIOGRAPHY AL-Allaf, O. N., 2010. Improving the Performance of

Backpropagation Neural Network. Journal of Computer Science 6 (11), pp. 1347-1354.

Anon., n.d. MATLAB Toolbox. [Online] Available at: http://www.mathworks.in/products/neural-network/[Accessed 09 08 2013].

Demuth, H., Beale, M. & Hagan, M., 1992. Neural Network Toolbox™ 6, s.l.: s.n.

Masters, T., 1994. Signal and Image Processing with Neural Networks, s.l.: John Wiley & Sons,Inc.

Qie, G., Terrell, T. J. & Varley, M. R., 1994. Improved Image Compression using Backpropagation Networks. U.K., IEEE, pp. 73-81.

Sivanandam, S. N., 2006. In: Introduction to Neural Networks using MATLAB 6.0. New Delhi: Tata McGrow-Hill, pp. 397-401.

THANK YOU !!!