6 Conclusion
We have presented a number of different predictive coding schemes for the compression of hyperspectral images. While the schemes differ in the details of their implementation their outline is essentially the same. Each algorithm tries to approximate the ideal case of stationary data for which optimal predictors can be computed. The approximations can be viewed as attempting to partition the data space into sets within which the stationarity assumption can be applied with some level of plausibility. The extent to which these algorithms function or fail depends upon the validity of their assumption. There is clearly much more work to be done before we can claim that the problem of predictive lossless compression has been solved.
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References
K. Sayood. Data Compression in Remote Sensing Applications. IEEE Geoscience and Remote Sensing Newsletter, (84):7–15, September 1992.
M.J. Ryan and J.F. Arnold. The Lossless Compression of AVIRIS Images by Vector Quantization. IEEE Transactions on Geoscience and Remote Sensing, Vol 35:546–550, March 1997.
G. Motta, F. Rizzo, and J.A. Storer. Compression of Hyperspectral Imagery. In Proceedings of the Data Compression Conference, DCC’ 03. IEEE, 2003.
X. Wu, N.D. Memon, and K. Sayood. A Context Based Adaptive Lossless/Nearly-Lossless Coding Scheme for Continuous Tone Images. ISO Working Document ISO/IEC SC29/WG1/N256, 1995.
X. Wu and N.D. Memon. CALIC-A Context Based Adaptive Lossless Image Coding Scheme. IEEE Transactions on Communications, May 1996.
M. Weinberger, G. Seroussi, and G. Sapiro. The LOCO-I Lossless Compression Algorithm: Principles and Standardization into JPEG-LS. Technical Report HPL-98-193, Hewlett-Packard Laboratory, November 1998.
J.M. Shapiro. Embedded Image Coding Using Zerotrees of Wavelet Coefficients. IEEE Transactions on Signal Processing, SP-41:3445–3462, December 1993.
A. Said and W.A. Pearlman. A New Fast and Efficient Coder Based on Set Partitioning in Hierarchical Trees. IEEE Transactions on Circuits and Systems for Video Technologies, pages 243–250, June 1996.
A.C. Miguel, A.R. Askew, A. Chang, S. Hauck, R.E. Ladner, and E.A. Riskin. Reduced Complexity Wavelet-Based Predictive Coding of Hyper-spectral Images for FPGA Implementation. In Proceedings of the Data Compression Conference, DCC’ 04. IEEE, 2004.
S.R. Tate. Band Ordering in Lossless Compression of Multispectral Images. IEEE Transactions on Computers, pages 477–483, April 1997.
R.E. Roger and M.C. Cavenor. Lossless Compression of AVIRIS Images. IEEE Transactions on Image Processing, pages 713–719, May 1996.
R. F. Rice, P. S. Yeh, and W. Miller. Algorithms for a very high speed universal noiseless coding module. Technical Report 91-1, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California., February 1991.
G. K. Wallace. The JPEG still picture compression standard. Communications of the ACM, 34:31–44, April 1991.
N.D. Memon, K. Sayood, and S.S. Magliveras. Lossless Compression of Multispectral Image Data. IEEE Transactions on Geoscience and Remote Sensing, 32:282–289, March 1994.
X. Wu and N.D. Memon. Context Based Lossless Intraband Adaptive Compression-Extending CALIC. IEEE Transactions on Image Processing, 9:994–1001, June 2000.
E. Magli, G. Olmo, and E. Quacchio. Optimized Onboard Lossless and Near-Lossless Compression of Hyperspectral Data Using CALIC. IEEE Geoscience and Remote Sensing Letters, 1:21–25, January 2004.
B. Aiazzi, P. Alba, L. alparone, and S. Baronti. Lossless Compression of Multi/Hyper-spectral Imagery Based on 3-D Fuzzy Prediction. IEEE Transactions on Geoscience and Remote Sensing, 37:2287–2294, September 1999.
J. Mielikainen, P. Toivanen, and A. Kaarna. Linear Prediction in Lossless Compression of Hyperspectral Images. Optical Engineering, 42:1013–1017, April 2003.
Y. Linde, A. Buzo, and R. M. Gray. An algorithm for vector quantization design. IEEE Transactions on Communications, COM-28:84–95, Jan. 1980.
F. Rizzo, B. Carpentieri, G. Motta, and J.A. Storer. High Performance Compression of Hyperspectral Imagery with Reduced Search Complexity in the Compressed Domain. In Proceedings of the Data Compression Conference, DCC’ 04. IEEE, 2004.
N.D. Memon and K. Sayood. Lossless Compression of Video Sequences. IEEE Transactions on Communications, Vol 44:1340–1345, October 1996.
M.J. Slyz and D.L. Neuhoff. A Nonlinear VQ-based Predictive Lossless Image Coder. In Proceedings of the Data Compression Conference, DCC’ 94. IEEE, 1994.
J. G. Cleary and I. H. Witten. Data compression using adaptive coding and partial string matching. IEEE Transactions on Communications, 32(4):396–402, 1984.
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Wang, H., Sayood, K. (2006). Lossless Predictive Compression of Hyperspectral Images. In: Motta, G., Rizzo, F., Storer, J.A. (eds) Hyperspectral Data Compression. Springer, Boston, MA. https://doi.org/10.1007/0-387-28600-4_2
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