Image Compression of 2-D Continuous Exponential Functions, Continuous Periodic Functions and Product of Sine and Cosine Functions using Discrete Wavelet Transform

G. K. Jagatheswari, Murugesan R., Gajanan V. Honnavar

Abstract


Wavelet transforms plays an important role in image compression techniques that developed recently. Here three 2-D functions are considered which are approximated and compressed using multilevel discrete 2-D wavelet transforms like Haar, Daubechies, Coiflet and Symlet. The images that are compressed are tested for quality using the error metrics like mean square error (MSE), peak to signal noise ratio (PSNR), maximum error (MAXERR), L2RAT, compression ratio and bit per pixel. The evaluation of the above mentioned wavelets is synthesized in terms of experimental results which demonstrates that Haar wavelets provides high compression ratios for 2-D exponential functions and the product of sine and cosine functions whereas Daubechies wavelet gives good compression ratio for 2-D periodic function.

Keywords


Image compression; DWT; MSE; MAXERR; PSNR; L2RAT; Haar; Daubechies; Coiflet; Symlet; Compression Ratio and Bit Error Rate

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References


R.N. Khatke, Image compression using wavelet transform, Imperial Journal of Interdisciplinary Research 2 (9) (2016), 82 – 84.

R.J. Sapkal and S.M. Kulkarni, Image fusion based on wavelet transform for medical application, International Journal of Engineering Research and Applications 2 (5) (September-October 2012), 624 – 627.

Ruchika, M. Singh and A.R. Singh, Compression of medical images using wavelet transforms, International Journal of Soft Computing and Engineering 2 (2) (May 2012), 2231 – 2307.

T. Bruylants, A. Munteanu and P. Schelkens, Wavelet based volumetric medical image compression, Signal Processing: Image Communication 31 (February 2015), 112 – 133.

J. Munoz-Gomez, J. Bartrina-Rapesta, M.W. Marcellin and J. Serra-Sagrista, Correlation modeling for compression of computed tomography, IEEE Journal of Biomedical and Health Informatics 17 (5) (2013), 928 – 935.

K.S. Thyagarajan, Still Image and Video Compression with Matlab, John Wiley and Sons, Inc., New Jersey (2011).

I. Daubechies, Ten Lectures on Wavelets, SIAM (in Wavelet Transforms-Image Compression) 5 (1992).

R.C. Gonzalec, R.E. Woods and S.L. Eddins, Digital Image Processing using Matlab, Prentice Hall (2004).

S.G. Chang, B. Yu and M. Vetterli, Adaptive wavelets thresholding for image denoising and compression, IEEE Trans. Image Processing 9(9) (2000), 1532 – 1546.

S. Mallat, A theory of mulitiresolution signal decomposition; the wavelet representation, IEEE Pattern Anal. And Machine Intel. 11 (7) (1989), 674 – 693.

P. Telagarapu et al., Image compression using DCT and wavelet transformations, International Journal of Signal Processing, Image Processing and Pattern recognition 4 (3) (September, 2011), 61 – 74.

Y. Zhang and X.Y. Wang, Fractal compression coding based on wavelet transform with diamond search, Nonlinear Analysis: Real World Applications 13 (1) (February 2012), 106 – 112.

V. Singh, N. Rajpal and K.S. Murthy, Neuro Wavelet based Approach for Image Compression, Computer Graphics, Imaging and Visualization, CGIV APOS’2007, 280 – 286.

G. Boopathi and S. Arockiasamy, Image compression: wavelet transform using Radial Basis Function (RBF) neural network, in 2012 Annual IEEE India Conference (INDICON) (2012), 340 – 344.

A. Rani and M. Mehta, Image compression of radiograph using neural network and wavelet, International Journal of Engineering Science and Technology 5 (4) (April 2013), 803 – 809.

R.C. Gonzalez and R. Eugene, Digital Image Processing, 3rd edition, Pearson Prentice Hall, Pearson Education, Inc. Upper Saddle River, New Jersey (2008).




DOI: http://dx.doi.org/10.26713%2Fjims.v9i3.940

eISSN 0975-5748; pISSN 0974-875X