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Fast Precomputed Vector Quantization with Optimal Bit Allocation for Lossless Compression of Ultraspectral Sounder Data

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Satellite Data Compression

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Abstract

The compression of three-dimensional ultraspectral sounder data is a challenging task given its unprecedented size. We develop a fast precomputed vector quantization (FPVQ) scheme with optimal bit allocation for lossless compression of ultraspectral sounder data. The scheme comprises of linear prediction, bit-depth partitioning, vector quantization, and optimal bit allocation. Linear prediction approach a Gaussian Distribution serves as a whitening tool to make the prediction residuals of each channel close to a Gaussian distribution. Then these residuals are partitioned based on bit depths. Each partition is further divided into several sub-partitions with various 2k channels for vector quantization. Only the codebooks with 2m codewords for 2k-dimensional normalized Gaussian distributions are precomputed. A new algorithm is developed for optimal bit allocation among sub-partitions. Unlike previous algorithms [19, 20] that may yield a sub-optimal solution, the proposed algorithm guarantees to find the minimum of the cost function under the constraint of a given total bit rate. Numerical experiments performed on the NASA AIRS data show that the FPVQ scheme gives high compression ratios for lossless compression of ultraspectral sounder data.

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References

  1. H. H. Aumann and L. Strow, “AIRS, the first hyper-spectral infrared sounder for operational weather forecasting,” in Proc. of IEEE Aerospace Conf., vol. 4, pp. 1683–1692, 2001.

    Google Scholar 

  2. T. Phulpin, F. Cayla, G. Chalon, D. Diebel, and D. Schlüssel, “IASI onboard Metop: Project status and scientific preparation,” in 12 th Int. TOVS Study Conf., Lorne, Victoria, Australia, pp. 234–243, 2002.

    Google Scholar 

  3. W. L. Smith, F. W. Harrison, D. E. Hinton, H. E. Revercomb, G. E. Bingham, R. Petersen, and J. C. Dodge, “GIFTS – the precursor geostationary satellite component of the future Earth Observing System,” in Proc. IGARSS’02 vol. 1, pp 357–361.

    Google Scholar 

  4. B. Huang, H.-L. Huang, H. Chen, A. Ahuja, K. Baggett, T. J. Schmit, and R. W. Heymann, “Data compression studies for NOAA hyperspectral environmental suite using 3D integer wavelet transforms with 3D set partitioning in hierarchical trees,” in SPIE Int. Symp. Remote Sensing Europe, 8–12 Sept. 2003, Barcelona, Spain, Proc. SPIE vol. 5238, pp. 255–265, 2003.

    Google Scholar 

  5. B. Huang, W. L. Smith, H.-L. Huang, H. M. Woolf, “Comparison of linear forms of the radiative transfer equation with analytic Jacobians,” Applied Optics 41 (21), 4209–4219.

    Google Scholar 

  6. Y. Linde, A. Buzo, and R. M. Gray, “An Algorithm for vector quantizer design,” IEEE Trans. Commun., vol. COM-28, pp. 84–95, Jan. 1980.

    Article  Google Scholar 

  7. G. P. Abousleman, M. W. Marcellin, and B. R. Hunt, “Hyperspectral image compression using entropy-constrained predictive trellis coded quantization,” IEEE Trans. Image Processing, vol. 6, no. 4, pp. 566–573, 1997.

    Article  Google Scholar 

  8. G. R. Canta and G. Poggi, “Kronecker-product gain-shape vector quantization for multispectral and hyperspectral image coding,” IEEE Trans. Image Processing, vol. 7, no. 5, pp. 668–678, 1998.

    Article  MathSciNet  MATH  Google Scholar 

  9. G. Gelli and G. Poggi, “Compression of multispectral images by spectral classification and transform coding,” IEEE Trans. Image Processing vol. 8, no. 4, pp. 476–489, 1999.

    Article  Google Scholar 

  10. G. Motta, F. Rizzo, and J. A. Storer, “Compression of hyperspectral imagery,” in Proc. 2003 Data Comp. Conf., pp. 333–342, 2003.

    Google Scholar 

  11. 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 Proc. 2004 Data Comp. Conf., pp. 479–488, 2004.

    Google Scholar 

  12. ftp://ftp.ssec.wisc.edu/pub/bormin/Count/.

  13. http://www-airs.jpl.nasa.gov.

  14. R. E. Roger, and M. C. Cavenor, “Lossless compression of AVIRIS images,” IEEE Trans. Image Processing, vol. 5, no. 5, pp. 713–719, 1996.

    Article  Google Scholar 

  15. X. Wu and N. Memon, “Context-based lossless interband compression – extending CALIC,” IEEE Trans. Image Processing, vol. 9, pp. 994–1001, Jun. 2000.

    Article  Google Scholar 

  16. D. Brunello, G. Calvagno, G. A. Mian, and R. Rinaldo, “Lossless compression of video using temporal information,” IEEE Tran. Image Processing, vol. 12, no. 2, pp. 132–139, 2003.

    Article  MathSciNet  Google Scholar 

  17. B. Huang, A. Ahuja, H.-L. Huang, T. J. Schmit, and R. W. Heymann, “Lossless compression of 3D hyperspectral sounding data using context-based adaptive lossless image codec with Bias-Adjusted Reordering,” Opt. Eng. vol. 43, no. 9, pp. 2071–2079, 2004.

    Article  Google Scholar 

  18. G. H. Golub and C. F. Van Loan, Matrix Computations, John Hopkins University Press, 1996.

    Google Scholar 

  19. E. A. Riskin, “Optimal bit allocation via the generalized BFOS algorithm,” IEEE Trans. Inform. Theory, vol. 37, pp. 400–402, Mar. 1991.

    Article  Google Scholar 

  20. V. Cuperman, “Joint bit allocation and dimensions optimization for vector transform quantization,” IEEE Trans. Inform. Theory, vol. 39, pp. 302–305, Jan. 1993.

    Article  MATH  Google Scholar 

  21. B. Fox, “Discrete optimization via marginal analysis,” Management Sci., vol. 13, no. 3, Nov. 1966.

    Google Scholar 

  22. I. H Witten., R. M. Neal, and J. Cleary, “Arithmetic coding for data compression,” Comm. ACM, vol. 30, no. 6, pp. 520–540, 1987.

    Article  Google Scholar 

  23. K. Popat and K. Zeger, “Robust quantization of memoryless sources using dispersive FIR filters,” IEEE Trans. Commun., vol. 40, pp. 1670–1674, Nov. 1992.

    Article  Google Scholar 

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Acknowledgement

This research was supported by National Oceanic and Atmospheric Administration’s National Environmental Satellite, Data, and Information Service under grant NA07EC0676. The views, opinions, and findings contained in this report are those of the author(s) and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision.

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  1. Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin–Madison, Madison, WI, USA

    Bormin Huang

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  1. Bormin Huang
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Correspondence to Bormin Huang .

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  1. , Space Science and Engineering Center, University of Wisconsin - Madison, Madison, 53706, Wisconsin, USA

    Bormin Huang

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Huang, B. (2012). Fast Precomputed Vector Quantization with Optimal Bit Allocation for Lossless Compression of Ultraspectral Sounder Data. In: Huang, B. (eds) Satellite Data Compression. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1183-3_12

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  • DOI: https://doi.org/10.1007/978-1-4614-1183-3_12

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  • Online ISBN: 978-1-4614-1183-3

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