Large volumes of fingerprints are collected and stored
every day in a wide range of applications, including forensics, access
control etc. It is evident from the database of Federal Bureau of
Investigation (FBI) which contains more than 70 million finger
prints. Compression of this database is very important because of this
high Volume. The performance of existing image coding standards
generally degrades at low bit-rates because of the underlying block
based Discrete Cosine Transform (DCT) 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 which are needed for better performance in compression.
New class of wavelets called 'Multiwavelets' which posses more
than one scaling filters overcomes this problem. The objective of this
paper is to develop an efficient compression scheme and to obtain
better quality and higher compression ratio through multiwavelet
transform and embedded coding of multiwavelet coefficients through
Set Partitioning In Hierarchical Trees algorithm (SPIHT) algorithm.
A comparison of the best known multiwavelets is made to the best
known scalar wavelets. Both quantitative and qualitative measures of
performance are examined for Fingerprints.
[1] Pennebaker, W. B. and Mitchell, J. L. JPEG - Still Image Data
Compression Standards Van Nostrand Reinhold,1993
[2] Rao, K. R. and Yip, P. Discrete Cosine Transforms - Algorithms,
Advantages, Applications, Academic Press, 1990.
[3] Soman K.P and Ramachandran K.I, "Insight into Wavelets from Theory
to Practice," Prentice Hall Of India, New Delhi, 2004.
[4] Mallat S, A Wavelet Tour of Signal Processing. New York: Academic,
1998.
[5] Vasily Strela, Peter Niels Heller, Gilbert Strang, Pankaj Topiwala, and
Christopher Heil, "The Application of Multiwavelet Filter banks to
Image Processing", IEEE Transactions on image processing, vol. 8, no.
4, April 1999. pp.548-563.
[6] Vetterli.M and G. Strang"Time-varying filter banks and multiwavelets,"
, Sixth IEEE DSP workshop, Yosemite, 1994
[7] Geronimo.J, D. Hardin, and P. R. Massopust "Fractal functions and
wavelet expansions based on several functions,"J. Approx. Theory, vol.
78, pp. 373-401, 1994.
[8] Strang.G and V. Strela, "Short wavelets and matrix dilation
equations,"IEEE Trans. Signal Processing, vol. 43, pp. 108-115, 1995.
[9] Strang.G and T. Nguyen, Wavelets and Filter Banks. Wellesley,
MA:Wellesley-Cambridge Press, 1995.
[10] Vetterli.M and G. Strang, "Time-varying filter banks and
multiwavelets," Sixth IEEE DSP workshop, Yosemite, 1994.
[11] Wonkookim and Ching Chung, "On preconditioning multiwavelet
system
for image compression", International Journal of Wavelets,
Multiresolution and Information Processing, Vol. 1, No. 1 (2003), pp.
51-74.
[12] Kwak-wai Cheung and Lai-Man Po"Preprocessing for Multiwavelet
Trans form of Two-Dimensional Signals".
[13] Michael B. Martin and Amy E. Bell , "New Image Compression
Techniques using Multiwavelets and Multiwavelet Packets", IEEE
Transactions on image processing, vol. 10, No. 4, , April 2001. pp. 500-
511
[14] Wonkookim and Ching Chung, "On preconditioning multiwavelet
system
For image compression", International Journal of Wavelets,
Multiresolution and Information Processing, Vol. 1, No. 1 (2003), pp.
51-74.
[15] Mariantonia Cotronei, Damiana Lazzaro, Laura B. Montefusco, and
Luigia Puccio, "Image Compression Through Embedded Multiwavelet
Transform Coding", IEEE Trans. on image processing, vol. 9, no. 2,
Febraury 2000
[16] Said and W. A. Pearlman, "A new, fast and efficient image codec based
on set partitioning in hierarchical trees," IEEE Trans. Circuits Syst.
Video Technol., vol. 6, no. 3, pp. 243-250, 1966
[1] Pennebaker, W. B. and Mitchell, J. L. JPEG - Still Image Data
Compression Standards Van Nostrand Reinhold,1993
[2] Rao, K. R. and Yip, P. Discrete Cosine Transforms - Algorithms,
Advantages, Applications, Academic Press, 1990.
[3] Soman K.P and Ramachandran K.I, "Insight into Wavelets from Theory
to Practice," Prentice Hall Of India, New Delhi, 2004.
[4] Mallat S, A Wavelet Tour of Signal Processing. New York: Academic,
1998.
[5] Vasily Strela, Peter Niels Heller, Gilbert Strang, Pankaj Topiwala, and
Christopher Heil, "The Application of Multiwavelet Filter banks to
Image Processing", IEEE Transactions on image processing, vol. 8, no.
4, April 1999. pp.548-563.
[6] Vetterli.M and G. Strang"Time-varying filter banks and multiwavelets,"
, Sixth IEEE DSP workshop, Yosemite, 1994
[7] Geronimo.J, D. Hardin, and P. R. Massopust "Fractal functions and
wavelet expansions based on several functions,"J. Approx. Theory, vol.
78, pp. 373-401, 1994.
[8] Strang.G and V. Strela, "Short wavelets and matrix dilation
equations,"IEEE Trans. Signal Processing, vol. 43, pp. 108-115, 1995.
[9] Strang.G and T. Nguyen, Wavelets and Filter Banks. Wellesley,
MA:Wellesley-Cambridge Press, 1995.
[10] Vetterli.M and G. Strang, "Time-varying filter banks and
multiwavelets," Sixth IEEE DSP workshop, Yosemite, 1994.
[11] Wonkookim and Ching Chung, "On preconditioning multiwavelet
system
for image compression", International Journal of Wavelets,
Multiresolution and Information Processing, Vol. 1, No. 1 (2003), pp.
51-74.
[12] Kwak-wai Cheung and Lai-Man Po"Preprocessing for Multiwavelet
Trans form of Two-Dimensional Signals".
[13] Michael B. Martin and Amy E. Bell , "New Image Compression
Techniques using Multiwavelets and Multiwavelet Packets", IEEE
Transactions on image processing, vol. 10, No. 4, , April 2001. pp. 500-
511
[14] Wonkookim and Ching Chung, "On preconditioning multiwavelet
system
For image compression", International Journal of Wavelets,
Multiresolution and Information Processing, Vol. 1, No. 1 (2003), pp.
51-74.
[15] Mariantonia Cotronei, Damiana Lazzaro, Laura B. Montefusco, and
Luigia Puccio, "Image Compression Through Embedded Multiwavelet
Transform Coding", IEEE Trans. on image processing, vol. 9, no. 2,
Febraury 2000
[16] Said and W. A. Pearlman, "A new, fast and efficient image codec based
on set partitioning in hierarchical trees," IEEE Trans. Circuits Syst.
Video Technol., vol. 6, no. 3, pp. 243-250, 1966
@article{"International Journal of Electrical, Electronic and Communication Sciences:55746", author = "Sudhakar.R and Jayaraman.S", title = "Fingerprint Compression Using Multiwavelets", abstract = "Large volumes of fingerprints are collected and stored
every day in a wide range of applications, including forensics, access
control etc. It is evident from the database of Federal Bureau of
Investigation (FBI) which contains more than 70 million finger
prints. Compression of this database is very important because of this
high Volume. The performance of existing image coding standards
generally degrades at low bit-rates because of the underlying block
based Discrete Cosine Transform (DCT) 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 which are needed for better performance in compression.
New class of wavelets called 'Multiwavelets' which posses more
than one scaling filters overcomes this problem. The objective of this
paper is to develop an efficient compression scheme and to obtain
better quality and higher compression ratio through multiwavelet
transform and embedded coding of multiwavelet coefficients through
Set Partitioning In Hierarchical Trees algorithm (SPIHT) algorithm.
A comparison of the best known multiwavelets is made to the best
known scalar wavelets. Both quantitative and qualitative measures of
performance are examined for Fingerprints.", keywords = "Mutiwavelet, Modified SPIHT Algorithm, SPIHT,
Wavelet.", volume = "2", number = "7", pages = "1422-10", }