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

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 km 2 ). 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 1:80,0001:40 000 (generated from 1:20 000) 1:20,000

7. Multi-scale organization

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

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 BasicWaterElevation linesPropertiesFields

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

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

14. Semantic vs. color separation

15. Block decomposition 1. Binary layers divided into non-overlapping rectangular blocks 2. Each block compressed separately 3. Compressed blocks are stored in the same file 4. Index table is stored in the header of the file

16. Use in the client device Current viewMovementUpdate 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.