Symbol Representation in Map Image Compression

UNIVERSITY OF JOENSUUDEPARTMENT OF COMPUTER SCIENCEFINLAND

Alexander Akimov and Pasi Fränti

ACM Symposium on Applied Computing (SAC’04)March 2004, Nicosia, Cyprus

Application of map images

V e c to r m a p d a ta b a s e

S e rv e r s id e

1 . V e c to r m a pra s te ris a tio n

2 . C o m p re s s io n

C lie n t s id e

Server side

Operations:• Rasterization• Compression

Benefits:• Independent on

vector formats• Large map

databases at server side

• Low cost client side applications

Client side

=

+

Operations:• Decompression• Viewing

Map image compression

Text rasterizationHershey vector font

2

4 5

1 3

(0,0)+(5,10)+(8,0)+(–1, –1) + (2,5)+ (6,5) =

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

Text rotation process

Text control data

MISS file structure

Text layer

Decoding

• Generic region decoding

• Text region decoding

1. Decoding of symbols bitmaps

2. Decoding of control data

Experimental results

Statistical data of the text rasterization

124101 201401 431204 431306

Size of dictionary 344 804 718 822

Number of symbols 2791 5440 9222 17726

Size of compressed blocks

Strip data 9930 13238 9222 17726

X sizes 351 733 609 736

Y sizes 453 706 633 731

Strip lengths 165 368 245 387

X coordinates 1068 2173 1529 2707

Y coordinates 672 1270 914 1530

Bitmap’s indexes 3332 5574 4249 7045

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.