JPEG2000 Image Compression Using SVM and DWT

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Abstract (2. Language): 
Image compression has expensively been developed as an important technique in multimedia applications and communication arena. This paper presents an effective method for image compression standard based on a Neural Network system for Discrete Wavelet Transform (DWT) and JPEG2000 encoder. We employ a Support Vector Machine (SVM) algorithm to compress the coefficients of DWT more efficiently. “Daubechies 9.7” wavelet have been used to DWT and the algorithm is a combination of SVs and corresponding weights and then coefficients are quantized and encoded. Obtained proposed simulation results show that the proposed algorithm achieve better image quality than that of existing methods for a given compression ratio and an improvement in compression performance.



[1] R. Jiao, Y. Li, Q. Wang, B. Li, “SVM Regression and Its
Application to Image Compression,” D.S. Huang, X. P. Zhang, G.-B. Huang
(Eds.): ICIC 2005, Part I, LNCS 3644, pp. 747 – 756, 2005. © Springer-Verlag Berlin Heidelberg 2005.
[2] R. Ahmed Clerk, “Wavelet-Based Image Compression Using
Support Vector Machine Learning And Encoding Techniques,” proceeding of
the eighth IASTED International Conference, Honolulu, Hawaii, USA, Aug.
15- 17, 2005, pp.162 -166.
[3] J. Robinson and V. Kecman, “Combining Support Vector Machine
with the Discrete Cosine Transform in Image Compression,” IEEE
Transaction on Neural Networks, Vol 14, No 4, July 2003, pp. 950–958
[4] J. Robinson, “The Application of Support Vector Machine to
Compression of The Digital Images,” PHD dissertation, School of
Engineering, university of Auckland, New Zealand, 2004.
[5] A. Tolambiya, P. K. Kalra, “WSVM with Morlet Wavelet Kernel
for Image Compression,” 1 – 4244 - 1 160, 2007 IEEE.
[6] K. S. Thyagarajan, “Still Image and Video Compression with
MATLAB,” ISBN 978-0-470-48416-6, John Wiley & Sons. 2011.
[7] S. R. Gunn, “Support Vector Machines for Classification and
Regression,” Faculty of Engineering, Science and Mathematics School of
Electronics and Computer Science, May 1998.
[8] T. Acharya, P. S. Tsail, “JPEG2000 Standard for Image
Compression Concepts, Algorithms and VLSl Architectures,” ISBN 0-471-48422-9, John Wiley & Sons, 2005.
[9] V. Vapnik, S. Golowich, A. Smola, “Support Vector Machine for
Function Approximation, Regression Estimation, and Signal Processing,” in:
M. Mozer, M. Jordan, T. Petsche (Eds.), Advances in Neural Information
Processing Systems, vol. 9, MIT Press, Cambridge, MA, 1997, pp. 281–287.
[10] J. Nagi, K.S. Yap, F. Nagi, S. K. Tiongb, S. K. Ahmed, “A
Computational Intelligence Scheme for the Prediction of the Daily peak load,”
Elsevier B.V. Applied Soft Computing 11. July 2011, pp. 4773–4788.
[11] N. Sreekumar, S. Santhosh Baboo, “ Image Compression using Wavelet
and Modified Extreme Learning Machine,” Computer Engineering and
Intelligent Systems, ISSN 2222-1719, Vol 2, No 2, 2011.

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