Image Compression Using Multiwavelet and Multi-Stage Vector Quantization
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32807
Image Compression Using Multiwavelet and Multi-Stage Vector Quantization

Authors: S. Esakkirajan, T. Veerakumar, V. Senthil Murugan, P. Navaneethan

Abstract:

The existing image coding standards generally degrades at low bit-rates because of the underlying block based Discrete Cosine Transform scheme. Over the past decade, the success of wavelets in solving many different problems has contributed to its unprecedented popularity. Due to implementation constraints scalar wavelets do not posses all the properties such as orthogonality, short support, linear phase symmetry, and a high order of approximation through vanishing moments simultaneously, which are very much essential for signal processing. New class of wavelets called 'Multiwavelets' which posses more than one scaling function overcomes this problem. This paper presents a new image coding scheme based on non linear approximation of multiwavelet coefficients along with multistage vector quantization. The performance of the proposed scheme is compared with the results obtained from scalar wavelets.

Keywords: Image compression, Multiwavelets, Multi-stagevector quantization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081205

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1881

References:


[1] K. R. Rao, and Yip, "Discrete Cosine Transform: Algorithms, Advantages, Applications," Academic Press, 1990.
[2] I. Daubechies, "Ten Lectures on Wavelets", Philadelphia, PA:SIAM, 1992.
[3] X. G. Xia, J. S. Geronimo, D. P. Hardin, and B. W. Suter, "Design of prefilters for discrete multiwavelet transforms," IEEE Trans. Signal Process., vol. 44, pp. 25-35, Jan. 1996.
[4] V. Strela, P. N. Hellers, G. Strang, P. Topiwala, and C. Heil, " The application of multiwavelet filter banks to image processing", IEEE Trans. Image Process., vol. 8, pp. 548-563, Apr. 1999
[5] R. M. Gray, "Vector Quantization," IEEE ASSP magazine, vol. 1, No.2, pp. 4-29, Apr. 1984
[6] A. Gersho and R.Gray, "Vector Quantization and Signal Compression", Kluwer Academic Publishers, M.A, 1995.
[7] M. Vetterli and G. Strang "Time-varying filter banks and multiwavelets," Sixth IEEE DSP workshop, Yosemite, 1994
[8] S.G. Mallat, "A theory for multiresolution signal decomposition: The wavelet representation", IEEE Trans. Pattern Anal. Mech. Intell., vol. 11, pp. 674-693, Jul. 1989.
[9] J. T. Miller, and C.C. Li, "Adaptive multiwavelet initialization", IEEE Trans. Signal Process., vol. 46, pp. 3282-3291, Dec. 1998.
[10] V. Strela, "Multiwavelets: theory and application" PhD dissertation, Department of Mathematics, Massachusetts Institute of Technology, 1996.
[11] Albert Cohen, I. Daubechies, G. Guleryuz and Micheal T.Orchard, "On the importance of combining wavelet-based nonlinear approximation with coding strategies", IEEE Trans. Information Theory, vol. 48, pp. 1895-1921, July 2002.
[12] B.H. Juang and A. Gray, "Multiple stage vector quantization for speech coding", in Proc. IEEE Int. Conf. Acous., Speech, Signal processing (Paris, France), pp. 597-600, Apr. 1982.
[13] M. B. Martin "Applications of Multiwavelets to Image Compression", Master Thesis, Department of Electrical Engineering, Virginia Tech, 1999.