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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
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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
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Server side
Operations:• Rasterization• Compression
Benefits:• Independent on
vector formats• Large map
databases at server side
• Low cost client side applications
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Client side
=
+
Operations:• Decompression• Viewing
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Map image compression
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Text rasterizationHershey vector font
2
4 5
1 3
(0,0)+(5,10)+(8,0)+(–1, –1) + (2,5)+ (6,5) =
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Text rotationText is stored as bitmaps:Same text after rotation consists from different bitmaps
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Text rotation process
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Text control data
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MISS file structure
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Text layer
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Decoding
• Generic region decoding
• Text region decoding
1. Decoding of symbols bitmaps
2. Decoding of control data
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Experimental results
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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
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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.