1. Symbol Representation in Map Image Compression UNIVERSITY OF JOENSUU DEPARTMENT OF COMPUTER SCIENCE FINLAND Alexander Akimov and Pasi Fränti ACM Symposium on Applied Computing (SAC’04) March 2004, Nicosia, Cyprus

2. Application of map images

3. Server side Operations: Rasterization Compression Benefits: Independent on vector formats Large map databases at server side Low cost client side applications

4. Client side = + Operations: Decompression Viewing

5. Map image compression

6. Text rasterization Hershey vector font (0,0)+(5,10)+(8,0)+(–1, –1) + (2,5)+ (6,5) =

7. Text rotation Text is stored as bitmaps: Same text after rotation consists from different bitmaps

8. Text rotation process

9. Text control data

10. MISS file structure

11. Text layer

12. Decoding Generic region decoding Text region decoding 1.Decoding of symbols bitmaps 2.Decoding of control data

13. Experimental results

14. Statistical data of the text rasterization 124101201401431204431306 Size of dictionary344804718822 Number of symbols27915440922217726 Size of compressed blocks Strip data993013238922217726 X sizes351733609736 Y sizes453706633731 Strip lengths165368245387  X coordinates 1068217315292707  Y coordinates 67212709141530 Bitmap’s indexes3332557442497045

15. Conclusions Improved map image compression... but where was the intelligence, computational logic, or image analysis? Intelligence was that we avoided all AI and IA operations at the client side.