Research proposal presentation

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Research proposal presentation

  1. 1. MATLAB SIMULATION FOR IMPROVED IMAGE COMPRESSION USING BACKPROPAGATION NETWORKS H.N.Gunasinghe-AS2010379 CSC 363 1.5 Research Methodologies and Scientific Computing Department of Computer Science and Statistics , USJP
  2. 2. OVERVIEW  Problem Identification  Introduction  Methodology  Limitations  Summary  Bibliography 2 AS2010379
  3. 3. 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. 3 AS2010379
  4. 4. 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. 4 AS2010379
  5. 5. INTRODUCTION (2) NEURAL NETWORK High PSNR Centralized backpropagation Low MSE 5 AS2010379
  6. 6. INTRODUCTION (3) AS2010379 6 MATLAB IMAGE COMPRESSION NEURAL NETWORK  MATLAB is consist with NN toolbox  MATLAB is consist with image compression toolbox  Proposed simulation can be easily implemented with MATLAB
  7. 7. 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. 7 AS2010379
  8. 8. METHODOLOGY (1) AS2010379 8 1. Image segmentation. 2. Choose network parameters. I. Input parameters II. Output parameters (target) III. Activation function IV. Number of layers V. Number of neurons in each layer. 3. Build and train the NN. 4. Reconstruct images. MATLAB IMAGE COMPRESSION NEURAL NETWORK
  9. 9. METHODOLOGY (2)  Analyzing and Evaluation  Test NN.  Apply NN with novel images.  Evaluate performance. 9 AS2010379
  10. 10. 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 10 AS2010379
  11. 11. 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. 11 AS2010379
  12. 12. 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. 12 AS2010379
  13. 13. THANK YOU !!!

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