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Image Compression Techniques for High Resolution Satellite Imageries using Classical Lifting Scheme
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 15 - Number 3 |
| Year of Publication: 2011 |
| Authors: K Nagamani, AG Ananth |
10.5120/1927-2572
|
K Nagamani, AG Ananth . Image Compression Techniques for High Resolution Satellite Imageries using Classical Lifting Scheme. International Journal of Computer Applications. 15, 3 ( February 2011), 25-28. DOI=10.5120/1927-2572
Abstract
The properties of Wavelet Transform can be successfully applied for analysis and processing of non stationary signals e.g., speech and image processing, data compression and communications. Due to the growing number of applications in various areas, it is necessary to explore the hardware implementation options of the Discrete Wavelet Transform (DWT). The Wavelet Series is just a sampled version of Continuous Wavelet Transform (CWT) and its computation may consume significant amount of time and resources, depending on the required resolution. The Discrete Wavelet Transform (DWT), based on sub-band coding, is a fast computation technique of Wavelet Transform, easy to implement and reduces the computational time and resources. Wavelet Transform uses multi-resolution technique by which different frequencies are analyzed with different resolutions. The study of 2-D DWT architectures reveals that there are two schemes for implementing DWT, one is based on convolution and other is based on lifting scheme. In this paper detailed study of Lifting Scheme has been carried out. Different architectures have been studied and performance parameters such as PSNR and Compression Ratio are determined. After obtaining double precision value of the image of size 256*256 imagery in BMP format, discrete wavelet transforms techniques are applied to obtain the wavelet coefficients for calculating PSNR and Compression ratio. Inverse Discrete wavelet transform are applied to get back the reconstructed image. It is found that for both satellite Rural and Urban imageries, the lifting scheme is very useful for obtaining higher quality of reconstructed images while achieving better PSNR~29 and Compression Ratios ~8.
References
- Swelden, “ A Fast Progressive Image Sampling UsingLifting Scheme And Non-Uniform B-Splines”, ISIE 2007. IEEE International Symposium on Industrial Electronics. 4-7 June 2007.
- Wavelets and Filter Banks by Gilbert Strang and Truong Nguyen, Wellesley-Cambridge Press, 1997
- Ripples in Mathematics: the Discrete Wavelet Transform by Arne Jense and Anders la Cour-Harbo, Springer, 2001
- Colm Mulcahy, “Image Compression Using The Haar Wavelet Transform” , Spelman College Science & Mathematics Journal, Vol 1, No 1, April 1997, 22-31.
- M L Hilton, B D Jawerth and Ayan Sengupta, “Compressing still and moving images with wavelets” published in the journal “Multimedia Systems” vol-2, no-3, 1994.
- P J Burt and E H Adelson “The Laplacian Pyramid as a compact image code” IEEE Trans. On Communication. Vol 31, pp 532-540, (1983)
- Bryan Usevitch, “A Tutorial on Modern Lossy Wavelet Image Compression : Foundations of JPEG 2000,” IEEE Signal Processing Magazine, 2001.
- R. A. DeVore, B. Jawerth, and B. J. Lucier, “Image Compression Through Wavelet Transform Coding,” IEEE Trans. On Information Theory, Vol. 38, No. 2, pp. 719 - 746, (1992).
- A. S. Lewis and G. Knowles, “Image Compression Using the 2-D Wavelet Transform”, IEEE Trans. on Image Processing, Vol. 1, No. 2, pp. 244-250, April (1992).
- J.M.Shapiro, “An Embedded Wavelet Hierarchical Image Coder,” Proc. IEEE Int. Conf. Acoust. Speech, Signal Processing, San Francisco, CA, (1992).
- J.M.Shapiro,“Embedded Image Coding Using Zerotrees of Wavelet Coefficients”, IEEE Trans. on Signal Processing, Vol. 41, pp 3445-3462, (1993)
- M Vetterli, J Kovacevic and D J Legall “ Perfect Reconstruction Filter Banks for HD TV representation and coding” Image communication Vol 2 essing Magazine . Vol 8, pp 349-364 (1990)
- A. Said and W.A. Pearlman, “A New, Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees”, IEEE Trans. on Circ and Syst for Video Tech, Vol 6, no. 3, pp 243-250, June 1996.
- Said and A. Pearlman, “An Image Multiresolution Representation for Losssless and Lossy Compression.” IEEE Trans. Image Processing, Vol. 5, No. 9, pp 243-250, Sept. 1996.
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