The week's pick
Reseach Article
Comparison of SPIHT and Lifting Scheme Image Compression Techniques for Satellite Imageries
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 25 - Number 3 |
| Year of Publication: 2011 |
| Authors: Nagamani.K, A.G Ananth |
10.5120/3014-4069
|
Nagamani.K, A.G Ananth . Comparison of SPIHT and Lifting Scheme Image Compression Techniques for Satellite Imageries. International Journal of Computer Applications. 25, 3 ( July 2011), 7-12. DOI=10.5120/3014-4069
Abstract
Wavelets offer an elegant technique for representing the levels of details present in an image. When an image is decomposed using wavelets, the low pass components carry more information than the high pass components. The possibility of better elimination of high pass components gives higher compression ratio for wavelet based techniques. To achieve higher compression ratio, various coding schemes have been considered. The study of 2-D Discrete Wavelet Technique (DWT) architectures reveals that there are two schemes for implementing DWT, one is based on convolution and other based on lifting scheme. . In the present paper a detailed study of the lifting compression scheme for satellite imageries has been carried out. A comparison between the performance of the lifting scheme and SPHIT technique has been made in the context of satellite imageries. For a given compression ratio, the PSNR (peak signal to noise ratio) values are estimated for both the schemes to achieve better quality of the reconstructed image. The results of the analysis demonstrate that for both the schemes, the PSNR values increases with the level of decomposition. The results of the analysis further indicates that for satellite imageries, the lifting scheme is more efficient for obtaining higher compression ratio ~8 and better PSNR values ~29 for achieving good quality of the reconstructed image.
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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