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Wavelet based Effective Color Image Compression using Neural Networks and Modified RLC
Abstract
Image compression is a technique of reducing the size
of image by eliminating data redundancy. It helps in reducing the amount of memory required to store an image and the time required to transmit the image over long distance. Earlier image compression
is performed by using wavelet and neural network. This paper proposes a method for image compression that uses wavelet and Multilayer Feed forward neural network (MLFFN) with Error Back
Propagation algorithm (EBPA), which is used to train multi layer feed forward neural network with an excellent input and output mapping. This algorithm is used for LL2 component and Modified Run Length Coding (RLC) to LH2, HL2 components with hard
threshold to discard insufficient coefficients. Performance of proposed image compression method is evaluated using Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Mean Square Error
(MSE). These estimation parameters were found to be greater when compared to image compression methods SOFM, EZW, SPIHT.
Keywords
References
Nait-Charif, H. and Salam, F., 2000. Neural networks-based image
compression system. In: Proceedings of the 43rd IEEE Midwest
Symposium on Circuits and Systems. Lansing, MI, USA: IEEE, pp. 446-
J.Villasenor, B. Belzer, J. Liao, "Wavelet Filter Evaluation for Image
Compression," IEEE Transactions on Image Processing, Vol. 2, pp.
-1060, August 1995.
Rohit Arora et al, “An Algorithm for Image Compression Using 2D
Wavelet Transform” International Journal of Engineering Science and
Technology (IJEST), Vol. 3 No. 4 Apr 2011.
Dr. K. Anitha Sheelaa Mr. P. Sreenivasulu , Dr.M.Asha Rani ”
Neural Networks and Lifting Scheme based Image Compression”
World Academy of Science, Engineering and Technology 69 2010.
S.P. Raja , Dr. A. Suruliandi ” Analysis Of Efficient Wavelet based
Image Compression Techniques” 2010 Second International conference
on Computing, Communication and Networking Technologies.
Weiwei Xiao, Haiyan LiuCollege of ScienceNorth China University of
TechnologyBeijing, P. R. China” Using Wavelet Networks in Image
Compression” 2011 Seventh International Conference on Natural
Computation.
James S. Walker., A Primer on Wavelets and Their Scientific
Applications, Second edition, Taylor & Francis Group, LLC,
Beijing, Jun.2008.
V. Mohan Y. Venkataramani “Compression of Iris images sing
DTCNN based Wavelet Decomposition and Directional Filter
BankAnalysis”proceedings of IEEE conference 2011.
Michel Misiti, Georges Oppenheim, Jean-Michel Poggi ,Yves
Misiti, Wavelet Toolbox™ User’s Guide, Second Edition,
Minor revision for Version 4.4.1 (Release 2009b),Online only,
Beijing, Sep.2009.
Peter L Venetianer, Tomas Roska, “Image compression by Cellular
Neural Networks”, IEEE Trans. On Circuits and systems –I, Vol.45,
No.3, March 1998.
N. Senthilkumaran, Member IACSIT and Dr. J. Suguna “Neural
Network Technique for Lossless ImageCompression Using X-Ray
Images”International Journal of Computer and Electrical Engineering,
Vol. 3, No. 2, April, 2011
H. Simon, Neural Networks and Learning Machines. 3rd ed. Beijing,
China: China-Machine, pp. 124-156, 2009
Dong Changhong, Neural Networks and Applications. 2rd ed.
Beijing,China: National Defense Industry, pp. 14-120, 2009.
Hadi Veisi, Mansour Jamzad” A Complexity-Based Approach in Image
Compression using Neural Networks” International Journal of
Information and Communication Engineering 5:2 2009.
Xiulian Peng, Jizheng Xu, Member, IEEE, and Feng Wu, Senior
Member, IEEE” Directional Filtering Transform forImage/Intra-Frame
Compression” IEEE Transactions On Image Processing, Vol. 19, No.
, November 2010.
Khashman A. and Dimililer K., Image Compression using
Neural Networks and Haar Wavelet, WSEASTrans Signal
rocessing 4 (5), pp. 330-339, May 2008.
Digital Image Processing by Esakkirajan S, Veerakumar T, Jayaraman
S, MGH International.
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