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Map image compression for real-time applications

Map image compression for real-time applications. Pasi Fränti , Eugene Ageenko, Pavel Kopylov, Sami Gr öhn, and Florian Berger. UNIVERSITY OF JOENSUU DEPARTMENT OF COMPUTER SCIENCE. Image Compression Research group: http://cs.joensuu.fi/pages/franti/comp/. Real-time application.

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Map image compression for real-time applications

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  1. Map image compression for real-time applications Pasi Fränti, Eugene Ageenko, Pavel Kopylov, Sami Gröhn, and Florian Berger UNIVERSITY OF JOENSUU DEPARTMENT OF COMPUTER SCIENCE Image Compression Research group: http://cs.joensuu.fi/pages/franti/comp/

  2. Real-time application • Visual view of the surrounding area. • GPS or MPS based navigation. • Real time panning and zooming

  3. Map storage vs. Portable device • Uncompressed: Electronic library of Finnish Road maps with resolution 1:250000 takes an entire CD (over 600 Mb). • Compressed: The map must be decompressed in the memory, before the image can be viewed. • Portable devices: Small storage size 16/64 Mb (up to 512Mb with CompactFlash) Weak processor performance: up to 200 Mhz

  4. Properties of maps • Maps of 50005000 pixels (1010 km2). • Uncompressed file size 12 Mb. • Topographic and Road maps. National Land Survey of Finland: www.nls.fi/index_e.html

  5. Map image storage system (MISS) • Zooming: Multi-scale representation. • Panning: block decomposition + direct access to compressed file. • Compact size: Image compression.

  6. Maps in different scale 1:20,000 1:80,000 1:40 000 (generated from 1:20 000)

  7. Multi-scale organization

  8. Compression method • Modelling • Context based statistical modelling • Coding • Arithmetic coding

  9. Map image organization Step 1. Map divided into layers Step 2. Layers divided into blocks Step 3. Blocks compressed separately

  10. Decomposition to binary layers Semantic decomposition Color separation Bit-level separation

  11. Semantic decomposition Basic Properties Elevation lines Water Fields

  12. Color separation Color 1 Color 2 Color 3 Color 4 Color 5

  13. Bit-level separation Plane 1 Plane 2 Plane 3 Plane 4 Plane 5 Plane 6 Plane 7 Plane 8

  14. Semantic vs. color separation

  15. Block decomposition Binary layers divided into non-overlapping rectangular blocks Each block compressed separately Compressed blocks are stored in the same file Index table is stored in the header of the file

  16. Use in the client device Current view Movement Update of view

  17. Real-time image decoding

  18. Dynamic map handling

  19. Compression results

  20. Semantic vs. color separation

  21. The effect of the block size

  22. Decompression times Times are for Set #1 using a processor of 1000 MIPS

  23. Retrieval timings of full screen

  24. Conclusions • Map image storage system (MISS) proposed for real-time applications. • System architecture designed to minimize storage size, transmission time, and memory requirements.

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