Predicting the Number of DCT Coefficients in the Process of Seabed Data Compression

  • Paweł ForczmańskiEmail author
  • Wojciech Maleika
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9256)


The paper presents Discrete Cosine Transform-based compression method applied to data describing seabed topography. It is an improvement over the previously developed and described algorithms capable of variable compression ratio and a possibility to limit the maximal reconstruction error. The main objective is to find an optimal number of DCT coefficients representing a surface with an acceptable reconstruction accuracy. In the original approach the compression was performed in an iterative manner, where successive values were tested, yielding high computational cost and time overhead. The algorithm presented in this paper allows to predict a number of DCT coefficients based on characteristics of specific input surface. Such characteristics are statistical measures describing a complexity of the surface. The classification using simple, fast and easy to learn classifiers does not introduce additional computational overhead. Presented experiments performed on real data gathered by maritime office gave encouraging results. Developed method can be employed in modern data storage and management system handling seabed topographic data.


Discrete Cosine Transform Binary Search Compression Algorithm Digital Terrain Model Compression Factor 
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. 1.
    Maleika, W.: Development of a Method for the Estimation of Multibeam Echosounder Measurement Accuracy, Przeglad Elektrotechniczny (Electrical Review), R. 88 No. 10b/2012, pp. 205–208 (2012)Google Scholar
  2. 2.
    Wawrzyniak, N., Hyla, T.: Managing depth information uncertainty in inland mobile navigation systems. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 343–350. Springer, Heidelberg (2014) Google Scholar
  3. 3.
    Zhao, J., Yan, J., Zhang, H.: A new method for weakening the combined effect of residual errors on multibeam bathymetric data. Marine Geophysical Research 35(4), 379–394 (2014)CrossRefGoogle Scholar
  4. 4.
    Maleika, W.: Moving average optimization in digital terrain model generation based on test multibeam echosounder data. Geo-Marine Letters 35(1), 61–68 (2015)CrossRefGoogle Scholar
  5. 5.
    Gaboardi, C., Mitishita, E.A., Firkowski, H.: Digital Terrain Modeling generalization with base in Wavelet Transform. Boletim de Ciencias Geodesicas 17(1), 115–129 (2011)CrossRefGoogle Scholar
  6. 6.
    Hamilton, E.L.: Geoacoustic modeling of the sea floor. Journal of the Acoustical Society of America 68(5), 1313–1340 (1980)CrossRefGoogle Scholar
  7. 7.
    Maleika, W.: The influence of the grid resolution on the accuracy of the digital terrain model used in seabed modeling. Marine Geophysical Research 36(1), 35–44 (2015)CrossRefGoogle Scholar
  8. 8.
    Łubczonek, J., Stateczny, A.: Concept of neural model of the sea bottom surface. Neural Networks and Soft Computing Book Series: Advances in Soft Computing 861–866 (2003)Google Scholar
  9. 9.
    Maleika, W., Palczynski, M., Frejlichowski, D.: Interpolation methods and the accuracy of bathymetric seabed models based on multibeam echosounder data. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part III. LNCS, vol. 7198, pp. 466–475. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  10. 10.
    Stateczny, A., Wlodarczyk-Sielicka, M.: Self-organizing artificial neural networks into hydrographic big data reduction process. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 335–342. Springer, Heidelberg (2014) Google Scholar
  11. 11.
    IHO standards for hydrographic surveys, Publication No. 44 of International Hydrographic Organization, 5th edn (2008).
  12. 12.
    Franklin, W.R., Said, A.: Lossy compression of elevation data. In: Seventh International Symposium on Spatial Data Handling, Delft, pp. 29–41 (1996)Google Scholar
  13. 13.
    Xie, Z., Franklin, W., Cutler, B., Andrade M., Inanc, M., Tracy, D.: Surface compression using over-determined Laplacian approximation. In: Proceedings of SPIE, Advanced Signal Processing Algorithms, Architectures, and Implementations XVII, vol. 6697, San Diego, CA. International Society for Optical Engineering (2007)Google Scholar
  14. 14.
    Stookey, J., Xie, Z., Cutler, B., Franklin, W., Tracy, D., Andrade, M.: Parallel ODETLAP for terrain compression and reconstruction. In: GIS 2008: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–9 (2008)Google Scholar
  15. 15.
    Pradhan, B., Mansor, S.: Three dimensional terrain data compression using second generation wavelets. In: 8th International Conference on Data, Text and Web Mining and Their Business Applications. Book Series: WIT Transactions on Information and Communication Technologies, vol. 38 (2007)Google Scholar
  16. 16.
    Rane, S.D., Sapiro, G.: Evaluation of JPEG-LS, the new lossless and controlled-lossy still image compression standard, for compression of high-resolution elevation data. IEEE Transactions on Geoscience and Remote Sensing 39(10), 2298–2306 (2001)CrossRefGoogle Scholar
  17. 17.
    Abousleman, G.P., Marcellin, M.W., Hunt, B.R.: Compression of Hyperspectral Imagery Using the 3-D DCT and Hybrid DPCM/DCT. IEEE Trans. on Geoscience and Remote Sensing 33(1), 26–34 (1995)CrossRefGoogle Scholar
  18. 18.
    Klimesh, M.: Compression of Multispectral Images. TDA Progress Report, pp. 42–129 (1997)Google Scholar
  19. 19.
    Cao, W., Li, B., Zhang, Y.: A remote sensing image fusion method based on PCA transform and wavelet packet transform. Neural Networks and Signal Processing 2, 976–981 (2003)Google Scholar
  20. 20.
    Fowler, J.E., Fox, D.N.: Wavelet based coding of three dimensional oceanographic images around land masses. In: Proceedings of the IEEE International Conference on Image Processing Vancouver, Canada, pp. 431–434 (2000)Google Scholar
  21. 21.
    Kazimierski, W., Zaniewicz, G.: Analysis of the possibility of using radar tracking method based on GRNN for processing sonar spatial data. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds.) RSEISP 2014. LNCS, vol. 8537, pp. 319–326. Springer, Heidelberg (2014) Google Scholar
  22. 22.
    Bruun, B.T., Nilsen, S.: Wavelet representation of large digital terrain models. Computers & Geosciences 29, 695–703 (2003)CrossRefGoogle Scholar
  23. 23.
    Stateczny, A., Łubczonek, J.: Radar sensors implementation in river information services in Poland. In: 15th International Radar Symposium (IRS), Book Series: International Radar Symposium Proceedings, pp. 1–5 (2014)Google Scholar
  24. 24.
    Wessel, P.: Compression of large data grids for Internet transmission. Computers & Geosciences 29, 665–671 (2003)CrossRefGoogle Scholar
  25. 25.
    Wright, D.J., Goodchild, M.F.: Data From the Deep: Implications for the GIS Community. The International Journal of Geographical Information Science 11(6), 523–528 (1997)CrossRefGoogle Scholar
  26. 26.
    Maleika, W.: Adaptive compression of real data describing sea bottom using DCT. In: Proceedings of 8th International Conference Advanced Computer Systems, Szczecin (2001)Google Scholar
  27. 27.
    Forczmański, P., Maleika, W.: Wavelets in adaptive compression of data describing sea bottom. In: Proc. 9th International Multi-conference Advanced Computer Systems ACS 2002, Miedzyzdroje, pp. 381–388 (2002)Google Scholar
  28. 28.
    Maleika, W.: Compression of sea floor data by means of Principal Component Analysis. In: 10th Marine Traffic Engineering Conference, pp. 189–197. Szczecin (2003) [in Polish]Google Scholar
  29. 29.
    Forczmański, P., Mantiuk, R.: Adaptive and quality-aware storage of JPEG files in the web environment. In: Chmielewski, L.J., Kozera, R., Shin, B.-S., Wojciechowski, K. (eds.) ICCVG 2014. LNCS, vol. 8671, pp. 212–219. Springer, Heidelberg (2014) Google Scholar
  30. 30.
    Maes, J., Bultheel, A.: Surface Compression With Hierarchical Powell Sabin B Splines. International Journal of Wavelets, Multiresolution and Information Processing 177–196 (2004)Google Scholar
  31. 31.
    Forczmański, P., Markiewicz, A.: Low-level image features for stamps detection and classification. In: 8th International Conference on Computer Recognition Systems (CORES). Advances in Intelligent Systems and Computing, vol. 226, pp. 383–392 (2013)Google Scholar
  32. 32.
    Choras, R.S., Andrysiak, T., Choras, M.: Integrated color, texture and shape information for content-based image retrieval. Pattern Analysis and Applications 10(4), 333–343 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

Personalised recommendations