Towards Optimal Compression of Meteorological Data: A Case Study of Using Interval-Motivated Overestimators in Global Optimization

  • Olga Kosheleva
Part of the Optimization and Its Applications book series (SOIA, volume 4)


The existing image and data compression techniques try to minimize the mean square deviation between the original data f(x, y, z) and the compressed-decompressed data f(x, y, z). In many practical situations, reconstruction that only guaranteed mean square error over the data set is unacceptable.


Mean Square Error Meteorological Data Data Compression JPEG2000 Compression Interval Uncertainty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [Cab02]
    Cabrera, S.D.: Three-dimensional compression of mesoscale meteorological data based on JPEG2000. In: Battlespace Digitization and Network-Centric Warfare II, Proc. SPIE, 4741, 239–250 (2002)Google Scholar
  2. [Flo00]
    Floudas, C.A.: Deterministic Global Optimization: Theory, Methods, and Applications. Kluwer, Dordrecht (2000)Google Scholar
  3. [JKDW01]
    Jaulin, L., Kieffer, M., Didrit, O., Walter, E.: Applied Interval Analysis: With Examples in Parameter and State Estimation, Robust Control and Robotics. Springer, London (2001)zbMATHGoogle Scholar
  4. [KLRK97]
    Kreinovich, V., Lakeyev, A., Rohn, J., Kahl, P.: Computational Complexity and Feasibility of Data Processing and Interval Computations. Kluwer, Dordrecht (1997)Google Scholar
  5. [Kos03]
    Kosheleva, O.M.: Task-Specific Metrics and Optimized Rate Allocation Applied to Part 2 of JPEG2000 and 3-D Meteorological Data. Ph.D. Dissertation, University of Texas at El Paso (2003)Google Scholar
  6. [KUCV04]
    Kosheleva, O.M., Usevitch, B., Cabrera, S., Vidal, E. Jr.: MSE optimal bit allocation in the application of JPEG2000 Part 2 to meteorological data. In: Proceedings of the 2004 IEEE Data Compression Conference DCC’2004, Snowbird, Utah, March 2004, 546 (2004)Google Scholar
  7. [KUV05]
    Kosheleva, O.M., Usevitch, B., Vidal, E. Jr.: Compressing 3D measurement data under interval uncertainty. In: Dongarra, J., Madsen, K., Wasniewski, J. (eds) PARA’04 Workshop on State-of-the-Art in Scientific Computing. Springer Lecture Notes in Computer Science, 3732, 142–150 (2005)Google Scholar
  8. [MF98]
    Mallat, S., Falzon, F.: Analysis of low bit rate image transform coding. IEEE Trans. Signal Proc., 46, 1027–1042 (1998)CrossRefGoogle Scholar
  9. [Moo79]
    Moore, R.E.: Methods and Applications of Interval Analysis. SIAM, Philadelphia (1979)zbMATHGoogle Scholar
  10. [TM02]
    Taubman, D.S., Marcellin, M.W.: JPEG2000 Image Compression Fundamentals, Standards and Practice. Kluwer, Boston, Dordrecht, London (2002)Google Scholar
  11. [Vav91]
    Vavasis, S.A.: Nonlinear Optimization: Complexity Issues. Oxford University Press, New York (1991)zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Olga Kosheleva
    • 1
  1. 1.Department of Electrical and Computer Engineering and Department of Teacher EducationUniversity of TexasEl PasoUSA

Personalised recommendations