New Approaches for Image Compression Using Neural Network

ABSTRACT

An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techniques. In this technique, it is possible to eliminate the redundant data contained in an image. The compressed image requires less memory space and less time to transmit in the form of information from transmitter to receiver. Artificial neural net- work with feed forward back propagation technique can be used for image compression. In this paper, the Bipolar Coding Technique is proposed and implemented for image compression and obtained the better results as compared to Principal Component Analysis (PCA) technique. However, the LM algorithm is also proposed and implemented which can acts as a powerful technique for image compression. It is observed that the Bipolar Coding and LM algorithm suits the best for image compression and processing applications.

An image consists of large data and requires more space in the memory. The large data results in more transmission time from transmitter to receiver. The time consumption can be reduced by using data compression techniques. In this technique, it is possible to eliminate the redundant data contained in an image. The compressed image requires less memory space and less time to transmit in the form of information from transmitter to receiver. Artificial neural net- work with feed forward back propagation technique can be used for image compression. In this paper, the Bipolar Coding Technique is proposed and implemented for image compression and obtained the better results as compared to Principal Component Analysis (PCA) technique. However, the LM algorithm is also proposed and implemented which can acts as a powerful technique for image compression. It is observed that the Bipolar Coding and LM algorithm suits the best for image compression and processing applications.

KEYWORDS

Image Compression, Feed Forward Back Propagation Neural Network, Principal Component Analysis (PCA), Levenberg-Marquardt (LM) Algorithm, PSNR, MSE

Image Compression, Feed Forward Back Propagation Neural Network, Principal Component Analysis (PCA), Levenberg-Marquardt (LM) Algorithm, PSNR, MSE

Cite this paper

nullV. Gaidhane, V. Singh, Y. Hote and M. Kumar, "New Approaches for Image Compression Using Neural Network,"*Journal of Intelligent Learning Systems and Applications*, Vol. 3 No. 4, 2011, pp. 220-229. doi: 10.4236/jilsa.2011.34025.

nullV. Gaidhane, V. Singh, Y. Hote and M. Kumar, "New Approaches for Image Compression Using Neural Network,"

References

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[2] M. J. Weinberger, G. Seroussi and G. Sapiro, “The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS,” IEEE Transaction on Image Processing, Vol. 9, No. 8, 2000, pp. 1309-1324.

[3] V. H. Gaidhane, Y. V. Hote and V. Singh, “A New Approach for Estimation of Eigenvalues of Images,” International Journal of Computer Applications, Vol. 26, No. 9, 2011, pp. 1-6.

[4] S.-G. Miaou and C.-L. Lin, “A Quality-on-Demand Algorithm for Wavelet-Based Compression of Electrocardiogram Signals,” IEEE Transaction on Biomedical Engineering, Vol. 49, No. 3, 2002, pp. 233-239.

[5] O. A. Ahmad and M. M. Fahmy, “Application of Multi-layer Neural Networks to Image Compression,” 1997 IEEE International Symposium on Circuits and Systems (ISCAS), Hong Kong, 1997, pp. 1273-1276.

[6] L. Ma and K. Khorasani, “Application of Adaptive Constructive Neural Networks to Image Compression,” IEEE Transactions on Neural Networks, Vol. 13, No. 5, 2002, pp. 1112-1126.

[7] Q. Du, W. Zhu and J. E. Fowler, “Implementation of Low-Complexity Principal Component Analysis for Remotely Sensed Hyper-Spectral Image Compression,” Proceeding of IEEE Workshop on Signal Processing Systems, Shanghai, October 2007, pp. 307-312.

[8] I. Vilovic, “An Experience in Image Compression Using Neural Networks,” Proceeding of 48th International Symposium Elmar, Zadar, 2006, pp. 95-98.

[9] M. Daszykowski, B. Walczak and D. L. Massart, “A Journey into Low-Dimensional Spaces with Autoassociative Neural Networks,” Talanta, Vol. 59, No. 6, 2003, pp. 1095-1105.

[10] V. Gaidhane, V. Singh and M. Kumar, “Image Compression Using PCA and Improved Technique with MLP Neural Network,” Proceedings of IEEE International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, 16-17 October 2010, pp. 106-110.

[11] R. C. Gonzalez, R. E. Woods and S. L. Eddins, “Digital Image Processing Using MATLAB,” Pearson Edition, Dorling Kindersley, London, 2003.

[12] J.-X. Mi and D.-S. Huang, “Image Compression Using Principal Component Analysis Neural Network,” 8th IEEE International Conference on Control, Automation, Robotics and Vision, Kunming, 6-9 December 2004, pp. 698-701.

[13] S.-T. Bow, B. T. Bow and S. T. Bow, “Pattern Recognition and Image Processing,” Revised and Expanded, 2nd Edition, CRC Press, Boca Raton, 2002.

[14] M. Nixon and A. Aguado, “Feature Extraction & Image Processing,” 2nd Edition, Academic Press, Cambridge, 2008, pp. 385-398.

[15] S. N. Sivanandam, S. Sumathi and S. N. Deepa, “Introduction to Neural Network Using MATLAB 6.0,” 2nd Edition, Tata Mc-Graw Hill Publication, Boston, 2008.

[16] A. Laha, N. R. Pal and B. Chanda, “Design of Vector Quantizer for Image Compression Using Self Organizing Feature Map and Surface Fitting,” IEEE Transactions on Image Processing, Vol. 13, No. 10, October 2004, pp. 1291-1303. doi:10.1109/TIP.2004.833107

[17] G. Qiu, T. J. Terrell and M. R. Varley, “Improved Image Compression Using Back Propagation Networks,” In: P. J. G. Lisbao and M. J. Taylor, Eds., Proceeding of the Workshop on Neural Network Applications and Tools, IEEE Computer Society Press, Washington DC, 1994, pp. 73-81.

[18] V. Singh, I. Gupta and H. O. Gupta, “ANN Based Estimator for Distillation Using Levenberg-Marquardt Approach,” Engineering Applications of Artificial Intelligence, Vol. 20, No. 2, 2007, pp. 249-259. doi:10.1016/j.engappai.2006.06.017

[19] M. I. A. Lourakis, “A Brief Description of the Levenberg-Marquardt Algorithm Implemented by Levmar,” Foundation of Research and Technology, Vol. 4, 2005, pp. 1-6.

[1] E. Watanabe and K. Mori, “Lossy Image Compression Using a Modular Structured Neural Network,” Proceedings of IEEE Signal Processing Society Workshop, Washington DC, 2001, pp. 403-412.

[2] M. J. Weinberger, G. Seroussi and G. Sapiro, “The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS,” IEEE Transaction on Image Processing, Vol. 9, No. 8, 2000, pp. 1309-1324.

[3] V. H. Gaidhane, Y. V. Hote and V. Singh, “A New Approach for Estimation of Eigenvalues of Images,” International Journal of Computer Applications, Vol. 26, No. 9, 2011, pp. 1-6.

[4] S.-G. Miaou and C.-L. Lin, “A Quality-on-Demand Algorithm for Wavelet-Based Compression of Electrocardiogram Signals,” IEEE Transaction on Biomedical Engineering, Vol. 49, No. 3, 2002, pp. 233-239.

[5] O. A. Ahmad and M. M. Fahmy, “Application of Multi-layer Neural Networks to Image Compression,” 1997 IEEE International Symposium on Circuits and Systems (ISCAS), Hong Kong, 1997, pp. 1273-1276.

[6] L. Ma and K. Khorasani, “Application of Adaptive Constructive Neural Networks to Image Compression,” IEEE Transactions on Neural Networks, Vol. 13, No. 5, 2002, pp. 1112-1126.

[7] Q. Du, W. Zhu and J. E. Fowler, “Implementation of Low-Complexity Principal Component Analysis for Remotely Sensed Hyper-Spectral Image Compression,” Proceeding of IEEE Workshop on Signal Processing Systems, Shanghai, October 2007, pp. 307-312.

[8] I. Vilovic, “An Experience in Image Compression Using Neural Networks,” Proceeding of 48th International Symposium Elmar, Zadar, 2006, pp. 95-98.

[9] M. Daszykowski, B. Walczak and D. L. Massart, “A Journey into Low-Dimensional Spaces with Autoassociative Neural Networks,” Talanta, Vol. 59, No. 6, 2003, pp. 1095-1105.

[10] V. Gaidhane, V. Singh and M. Kumar, “Image Compression Using PCA and Improved Technique with MLP Neural Network,” Proceedings of IEEE International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, 16-17 October 2010, pp. 106-110.

[11] R. C. Gonzalez, R. E. Woods and S. L. Eddins, “Digital Image Processing Using MATLAB,” Pearson Edition, Dorling Kindersley, London, 2003.

[12] J.-X. Mi and D.-S. Huang, “Image Compression Using Principal Component Analysis Neural Network,” 8th IEEE International Conference on Control, Automation, Robotics and Vision, Kunming, 6-9 December 2004, pp. 698-701.

[13] S.-T. Bow, B. T. Bow and S. T. Bow, “Pattern Recognition and Image Processing,” Revised and Expanded, 2nd Edition, CRC Press, Boca Raton, 2002.

[14] M. Nixon and A. Aguado, “Feature Extraction & Image Processing,” 2nd Edition, Academic Press, Cambridge, 2008, pp. 385-398.

[15] S. N. Sivanandam, S. Sumathi and S. N. Deepa, “Introduction to Neural Network Using MATLAB 6.0,” 2nd Edition, Tata Mc-Graw Hill Publication, Boston, 2008.

[16] A. Laha, N. R. Pal and B. Chanda, “Design of Vector Quantizer for Image Compression Using Self Organizing Feature Map and Surface Fitting,” IEEE Transactions on Image Processing, Vol. 13, No. 10, October 2004, pp. 1291-1303. doi:10.1109/TIP.2004.833107

[17] G. Qiu, T. J. Terrell and M. R. Varley, “Improved Image Compression Using Back Propagation Networks,” In: P. J. G. Lisbao and M. J. Taylor, Eds., Proceeding of the Workshop on Neural Network Applications and Tools, IEEE Computer Society Press, Washington DC, 1994, pp. 73-81.

[18] V. Singh, I. Gupta and H. O. Gupta, “ANN Based Estimator for Distillation Using Levenberg-Marquardt Approach,” Engineering Applications of Artificial Intelligence, Vol. 20, No. 2, 2007, pp. 249-259. doi:10.1016/j.engappai.2006.06.017

[19] M. I. A. Lourakis, “A Brief Description of the Levenberg-Marquardt Algorithm Implemented by Levmar,” Foundation of Research and Technology, Vol. 4, 2005, pp. 1-6.