Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
N. R., Rema
- Better Fingerprint Image Compression at Lower Bit-rates: An Approach Using Multiwavelets with Optimised Prefilter Coefficients
Abstract Views :190 |
PDF Views:6
Authors
Affiliations
1 Department of Electronics and Communication Engineering, School of Engineering, Cochin University of Science & Technology, IN
2 Department of Electronics and Communication Engineering, College of Engineering, Kallooppara, IN
1 Department of Electronics and Communication Engineering, School of Engineering, Cochin University of Science & Technology, IN
2 Department of Electronics and Communication Engineering, College of Engineering, Kallooppara, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 1 (2017), Pagination: 1588-1595Abstract
In this paper, a multiwavelet based fingerprint compression technique using set partitioning in hierarchical trees (SPIHT) algorithm with optimised prefilter coefficients is proposed. While wavelet based progressive compression techniques give a blurred image at lower bit rates due to lack of high frequency information, multiwavelets can be used efficiently to represent high frequency information. SA4 (Symmetric Antisymmetric) multiwavelet when combined with SPIHT reduces the number of nodes during initialization to 1/4th compared to SPIHT with wavelet. This reduction in nodes leads to improvement in PSNR at lower bit rates. The PSNR can be further improved by optimizing the prefilter coefficients. In this work genetic algorithm (GA) is used for optimizing prefilter coefficients. Using the proposed technique, there is a considerable improvement in PSNR at lower bit rates, compared to existing techniques in literature. An overall average improvement of 4.23dB and 2.52dB for bit rates in between 0.01 to 1 has been achieved for the images in the databases FVC 2000 DB1 and FVC 2002 DB3 respectively. The quality of the reconstructed image is better even at higher compression ratios like 80:1 and 100:1. The level of decomposition required for a multiwavelet is lesser compared to a wavelet.Keywords
Multiwavelet, Fingerprint, Compression, Lower Bit Rate, Optimised Prefilter Coefficients.
References
- V. Strela, P.N. Heller, G. Strang, P. Topiwala and C. Heil, “The Application of Multiwavelet Filterbanks to Image Processing”, IEEE Transactions on Image Processing, Vol. 8, No. 4, pp. 548-563,1999.
- M. Ashok and D.T.B. Reddy, “Image Compression Techniques using Modified High Quality Multiwavelets”, International Journal of Advanced Computer science and Applications, Vol. 2, No. 7, pp. 153-158, 2011.
- U.S. Ragupathy, D. Baskar and A. Tamilarasi, “New Method of Image Compression using Multiwavelets and Set Partitioning Algorithm”, Proceedings of 10th IEEE International Conference on Industrial and Information Systems, pp. 1-6, 2008.
- S. Radhakrishnan and J. Subramaniam, “Fingerprint Compression using Multiwavelets”, International Journal of Signal Processing, Vol. 2, No. 2, pp. 78-87, 2006.
- X.G. Xia, J.S. Geronimo, D.P. Hardin and B.W. Suter, “Design of Prefilters for Discrete Multiwavelet Transforms”, IEEE Transactions on Signal Processing, Vol. 44, No. 1, pp. 25-35, 1996.
- K. Attakitmongcol, D.P. Hardin and D.M. Wilkes, “Multiwavelet prefilters. II. Optimal orthogonal prefilters”, IEEE Transactions on Image Processing, Vol. 10, No. 10, pp. 1476-1487, 2001.
- H. Shi, Y. Cai and Z. Qiu, “On Design of Multiwavelet Prefilters”, Applied Mathematics and Computation, Vol. 172, No. 2, pp. 1175-1187, 2006.
- S. Esakkirajan, T. Veerakumar, V.S. Murugan and R. Sudhakar, “Fingerprint Compression using Contourlet Transform and Multistage Vector Quantization”, International Journal of Biological and Medical Sciences, Vol. 1, No. 2, pp. 140-147, 2006.
- G. Shao, Y. Wu, A. Yong, X. Liu and T. Guo, “Fingerprint Compression based on Sparse Representation”, IEEE Transactions on Image Processing, Vol. 23, No. 2, pp. 489501, 2014.
- K.T. Shanavaz and P. Mythili, “Faster Techniques to Evolve Wavelet Coefficients for better Fingerprint Image Compression”, International Journal of Electronics, Vol. 100, No. 5, pp. 655-668, 2013.
- B.S. Emmanuel, M.D. Muazu, S.M. Sani and S. Garba, “Improved Algorithm for Biometric Fingerprint Image Compression”, American Journal of Computation, Communication and Control, Vol. 1, No. 5, pp. 75-85, 2014.
- H. Grailu, “Improving the Fingerprint Verification Performance of Set Partitioning Coders at Low Bit Rates”, Multimedia Tools and Applications, Vol. 76, No. 7, pp. 9959-9991, 2017.
- M.B. Martin and A.E. Bell, “New Image Compression Techniques using Multiwavelets and Multiwavelet Packets”, IEEE Transactions on Image Processing, Vol. 10, No. 4, pp. 500-510, 2001.
- R. Sumalatha and M.V. Subramanyam, “Medical Image Compression using Multiwavelets for Telemedicine Applications”, International Journal of Scientific and Engineering Research, Vol. 2, No. 9, pp. 1-4, 2011.
- J.Y. Tham, L. Shen, S.L. Lee and H.H. Tan, “A General Approach for Analysis and Application of Discrete Multiwavelet Transforms”, IEEE Transactions on Signal Processing, Vol. 48, No. 2, pp. 457-464, 2000.
- A. Said and W.A. Pearlman, “A New, Fast, and Efficient Image Codec based on Set partitioning in Hierarchical Trees”, IEEE Transactions on Circuits and systems for Video Technology, Vol. 6, No. 3, pp. 243-250, 1996.
- M.D. Adams and R. Ward, “Wavelet Transforms in the JPEG-2000 Standard”, Proceedings of IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Vol. 1, pp. 160-163, 2001.