Abstract
Mobile platforms bring possibilities to use distributed services in several users’ environments. One of the conditions of the mobile platform is to transfer as less as possible amount of data via network. When dealing with geodata in mobile environment it is always necessary to use compression algorithms to lower their size. Digital elevation models in a GRID form are usually compressed with LZW or DCT technique, but the best compression ratio is now available with wavelet algorithms. The compression can be loss or loss less. The paper describes results of tests of JPEG 2000 loss compression technique. We studied impact of JPEG 2000 loss compression on quality of DEM. We have tested several options of OpenJPEG library to find how these options can change resulting compressed DEM. The main part of the research was focused on pre-filtering of DEM before compression. We have find out a way how to minimise average error of loss compression when keeping the same compression ratio.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ben-Moshe B, Serruya L, Shamir A (2007) Image compression terrain simplification. In: Proceedings of the 19th Canadian conference on computational geometry (CCCG2007), pp 125–128. Available at. http://cccg.ca/proceedings/2007/05b1full.pdf
Gladkova I, Gottipati S, Grossberg M (2007) A new lossless compression algorithm for satellite earth science multi-spectral imagers. In: Proceedings of the SPIE, compression, communications, and archiving III, 6683 (668307). Available at. http://glasslab.engr.ccny.cuny.edu/u/grossberg/pubs/imageralgSPIE_07.pdf. Aug 2007
Gladkova I, Grossberg M (2006) A lossless compression algorithm for hyperspectral data. In: Proceedings of the SPIE, satellite data compression, communications, and archiving II, 6300 (630001). Aug 2006
Inanc M (2008) Compressing terrain elevation datasets. Thesis submitted to the graduate faculty of Rensselaer Polytechnic Institute. New York, 62 p. Available at. http://www.ecse.rpiscrews.us/Homepages/wrf/theses/inanc-phd.pdf
MicroImages, Inc. (2009) JPEG2000 Compression for global ASTER DEM. Available at. http://www.microimages.com/documentation/TechGuides/75jp2AsterDEM.pdf
Mittal ML, Singh VK, Krishnan R (2013) Effect of data compression on DEM accuracy derived from satellite images. Docstoc, 6 p. Available at. http://www.docstoc.com/docs/160718730/effect-of-data-compression-on-dem-accuracy-derived-from-satellite
Vatolin D et al (2005) JPEG 2000 image codecs comparison. CS MSU Graphics and Media Lab. Available at. http://compression.ru/video/codec_comparison/jpeg2000_codecs_comparison_en.html
Acknowledgments
The ASTER GDEM data were obtained through the online Data Pool at the NASA portal http://asterweb.jpl.nasa.gov/gdem-wist.asp.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Růžička, J., Růžičková, K. (2015). Impact of GDAL JPEG 2000 Lossy Compression to a Digital Elevation Model. In: Růžičková, K., Inspektor, T. (eds) Surface Models for Geosciences. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-18407-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-18407-4_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18406-7
Online ISBN: 978-3-319-18407-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)