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Lossless Compression Method for Digital Terrain Model of Seabed Shape

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Image Processing and Communications Challenges 8 (IP&C 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 525))

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Abstract

Dealing with Digital Terrain Models requires storing and processing of huge amounts of data, obtained from hydrographic measurements. Currently no dedicated methods for DTM data compression exist. In the paper a lossless compression method is proposed, tailored specifically for DTM data. The method involves discarding redundant data, performing differential coding, Variable Length Value coding, and finally compression using LZ77 or PPM algorithm. We present the results of experiments performed on real-world hydrographic data, which prove the validity of the proposed approach.

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References

  1. Chang, Z.Q., Wu, L.X.: Mountain grid DEM data compression based on wavelet transform and mixed entropy coding. Geogr. Geo-Inf. Sci. 1, 24–27 (2004)

    Google Scholar 

  2. Chybicki, A., Moszynski, M.: Applications of compression techniques for reducing the size of multibeam sonar records. In: Proceedings of the 2008 1st International Conference on Information Technology, pp. 1–4 (2008)

    Google Scholar 

  3. Forczmański, P., Maleika, W.: Near-lossless PCA-based compression of seabed surface with prediction. In: Kamel, M., Campilho, A. (eds.) ICIAR 2015. LNCS, vol. 9164, pp. 119–128. Springer, Heidelberg (2015). doi:10.1007/978-3-319-20801-5_13

    Chapter  Google Scholar 

  4. Franklin, W.R., Said, A.: Lossy compression of elevation data. In: Seventh International Symposium on Spatial Data Handling, Delft (1996)

    Google Scholar 

  5. Gerstner, T.: Multiresolution compression and visualization of global topographic data. GeoInformatica 7(1), 7–32 (2003)

    Article  MathSciNet  Google Scholar 

  6. Gordon, V., Cormack, R., Horspool, R.N.: Algorithms for adaptive huffman codes. Inf. Process. Lett. 18(3), 159–165 (1984)

    Article  MathSciNet  Google Scholar 

  7. International Hydrographic Organization standards for hydrographic surveys. Pub. No. 44, 5th Ed. (2008). http://www.iho.int//iho_pubs//standard//S-44_5E.pdf

  8. Maleika, W.: Development of a method for the estimation of multibeam echosounder measurement accuracy. Przegląd Elektrotechniczny 88(10b), 205 (2012)

    Google Scholar 

  9. Maleika, W., Czapiewski, P.: Visualisation of multibeam echosounder measurement data. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds.) PReMI 2013. LNCS, vol. 8251, pp. 373–380. Springer, Heidelberg (2013). doi:10.1007/978-3-642-45062-4_51

    Chapter  Google Scholar 

  10. Maleika, W., Czapiewski, P.: Lossless compression method for ASCII UTM format sea survey data obtained from multibeam echosounder. Roczniki Geomatyki, Vol. XII, 3(65), 289–301 (2014)

    Google Scholar 

  11. Maleika, W.: Moving average optimization in digital terrain model generation based on test multibeam echosounder data. Geo Mar. Lett. 35(1), 61–68 (2015)

    Article  Google Scholar 

  12. 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. Wit Transactions on Information and Communication Technologies, 38 (2007)

    Google Scholar 

  13. Shkarin, D.: PPM: one step to practicality. In: Proceedings of the Data Compression Conference, pp. 202–211 (2002)

    Google Scholar 

  14. 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 (LNAI), vol. 8537, pp. 335–342. Springer, Heidelberg (2014). doi:10.1007/978-3-319-08729-0_34

    Google Scholar 

  15. Stephens, D., Diesing, M.: A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data. PloS ONE 9(4), e93950 (2014)

    Article  Google Scholar 

  16. 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 (2008)

    Google Scholar 

  17. Ulacha, G., Stasinski, R.: New context-based adaptive linear prediction algorithm for lossless image coding. In: International Conference on Signals and Electronic Systems (2014)

    Google Scholar 

  18. 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 (LNAI), vol. 8537, pp. 343–350. Springer, Heidelberg (2014). doi:10.1007/978-3-319-08729-0_35

    Google Scholar 

  19. Xie, Z., Franklin, W., Cutler, B., Andrade, M., Inanc, M., Tracy, D.: Surface compression using over-determined laplacian approximation. In: Proceedings of the SPIE, Advanced Signal Processing Algorithms, Architectures, and Implementations XVII, vol. 6697 (2007)

    Google Scholar 

  20. Ziv, J., Lempel, A.: Universal algorithm for sequential data compression. IEEE Trans. Inf. Theory 23(3), 337–343 (1977)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Żołnierska Street 49, 71–210, Szczecin, Poland

    Wojciech Maleika & Paweł Forczmański

Authors
  1. Wojciech Maleika
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  2. Paweł Forczmański
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Corresponding author

Correspondence to Paweł Forczmański .

Editor information

Editors and Affiliations

  1. Institute of Telecommunications and Computer Sciences, UTP University of Science and Technology, Bydgoszcz, Poland

    Ryszard S. Choraś

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Maleika, W., Forczmański, P. (2017). Lossless Compression Method for Digital Terrain Model of Seabed Shape. In: Choraś, R. (eds) Image Processing and Communications Challenges 8. IP&C 2016. Advances in Intelligent Systems and Computing, vol 525. Springer, Cham. https://doi.org/10.1007/978-3-319-47274-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-47274-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47273-7

  • Online ISBN: 978-3-319-47274-4

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