Share This Article:

Lossy-to-Lossless Compression of Hyperspectral Image Using the 3D Set Partitioned Embedded ZeroBlock Coding Algorithm

Full-Text HTML Download Download as PDF (Size:346KB) PP. 86-95
DOI: 10.4236/jsea.2009.22013    5,897 Downloads   9,793 Views   Citations
Author(s)    Leave a comment

ABSTRACT

In this paper, we propose a three-dimensional Set Partitioned Embedded ZeroBlock Coding (3D SPEZBC) lossy-to-lossless compression algorithm for hyperspectral image which is an improved three-dimensional Embedded ZeroBlock Coding (3D EZBC) algorithm. The algorithm adopts the 3D integer wavelet packet transform proposed by Xiong et al. to decorrelate, the set-based partitioning zeroblock coding to process bitplane coding and the con-text-based adaptive arithmetic coding for further entropy coding. The theoretical analysis and experimental results demonstrate that 3D SPEZBC not only provides the same excellent compression performances as 3D EZBC, but also reduces the memory requirement compared with 3D EZBC. For achieving good coding performance, the diverse wave-let filters and unitary scaling factors are compared and evaluated, and the best choices were given. In comparison with several state-of-the-art wavelet coding algorithms, the proposed algorithm provides better compression performance and unsupervised classification accuracy.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Y. Hou, "Lossy-to-Lossless Compression of Hyperspectral Image Using the 3D Set Partitioned Embedded ZeroBlock Coding Algorithm," Journal of Software Engineering and Applications, Vol. 2 No. 2, 2009, pp. 86-95. doi: 10.4236/jsea.2009.22013.

References

[1] [1] X. Tang and W. A. Pearlman, “Three-dimensional wave-let-based compression of hyperspectral images,” Hyper-spectral Data Compression, MA: Kluwer Academic Pub-lishers, pp. 273-308, 2006.
[2] [2] J. E. Fowler and J. T. Rucker, “3D wavelet-based com-pression of hyperspectral imagery,” Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons Inc., Hoboken, NJ, pp. 379-407, 2007.
[3] [3] B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compres-sion,” in the Proceedings of IEEE Transactions on Geo-science and Remote Sensing, Vol. 45, No. 5, pp. 1408 -1421, 2007.
[4] [4] T. W. Fry and S. Hauck, “Hyperspectral image compres-sion on reconfigurable platforms,” in the Proceedings of IEEE Symposium on Field-Programmable Custom Com-puting Machines, pp. 251-260, April 2002.
[5] [5] X. Tang, S. Cho, and W. A. Pearlman, “3D set partition-ing coding methods in hyperspectral image compres-sion,” in the Proceedings of IEEE International Confer-ence on Image Processing, pp. 239-242, September 2003.
[6] [6] X. Tang and W. A. Pearlman, “Lossy-to-lossless block- based compression of hyperspectral volumetric data,” in the Proceedings of IEEE International Conference on Image Processing, pp. 1133-1136, 2006.
[7] [7] J. J. Wu, Z. S. Wu, and C. K. Wu, “Lossy to lossless compressions of hyperspectral images using three-dimen-sional set partitioning algorithm,” SPIE Optical Engi-neering, Vol. 45, No. 2, pp. 0270051-0270058, 2006.
[8] [8] B. Penna, T. Tillo, E. Magli, and G. Olmo, “Embedded lossy-to-lossless compression of hyperspectral images using JPEG 2000,” in the Proceedings of IEEE Interna-tional Geoscience and Remote Sensing Symposium, Vol. 1, pp. 25-29, 2005.
[9] [9] J. Zhang, J. E. Fowler, and G. Z. Liu, “Lossy-to-lossless compression of hyperspectral imagery using 3D-TCE and an integer KLT,” IEEE Geoscience and Remote Sensing Letters, Vol. 4, No. 2, pp. 201-205, 2008.
[10] [10] S. T. Hsiang, “Highly scalable subband/wavelet image and video coding,” Ph.D dissertation, Rensselaer Poly-technic Institute, Troy, 2002.
[11] [11] A. Islam and W. A. Pearlman, “An embedded and effi-cient low-complexity hierarchical image coder,” in the Proceedings of SPIE Conference on Visual Communica-tions and Image Processing, Vol. 3653, pp. 294-305, 1999.
[12] [12] Y. Hou and G. Z. Liu, “3D set partitioned embedded zero block coding algorithm for hyperspectral image compres-sion,” in the Proceedings of SPIE Symposium on MIPPR, Vol. 6790, pp. 561-567, 2007.
[13] [13] A. R. Calderbank, I. Daubechies, W. Sweldens, and B. L. Yeo, “Wavelet transforms that map integers to integers,” Applied and Computational Harmonic Analysis, Vol. 5, No. 3, pp. 332-369, 1998.
[14] [14] M. D. Adams and F. Kossentini, “Reversible inte-ger-to-integer wavelet transforms for image compression: Performance evaluation and analysis,” IEEE Transactions on Image Processing, Vol. 9, No. 6, pp. 1010-1024, 2000.
[15] [15] Z. X. Xiong, X. L. Wu, S. Cheng, and J. P. Hua, “Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms,” IEEE Transactions on Medical Imaging, Vol. 22, No. 3, pp. 459-470, 2003.
[16] [16] http://aviris.jpl.nasa.gov/html/aviris.overview.html.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.