Precoding and Decoding Paradigms for Distributed Vector Data Compression

2007 ◽  
Vol 55 (4) ◽  
pp. 1445-1460 ◽  
Author(s):  
Azadeh Vosoughi ◽  
Anna Scaglione
Keyword(s):  
Data Compression ◽  
Vector Data

Improved algorithm for vector data compression based on dynamic programming

2008 ◽  
Vol 28 (1) ◽  
pp. 168-170 ◽  
Author(s):  
Fei-xiang CHEN ◽  
Zhi-wu ZHOU ◽  
Jian-bing ZHANG
Keyword(s):  
Dynamic Programming ◽  
Data Compression ◽  
Vector Data ◽  
Improved Algorithm

scholarly journals A new vector data compression approach for WebGIS

2011 ◽  
Vol 14 (1) ◽  
pp. 48-53 ◽  
Author(s):  
Yunjin Li ◽  
Ershun Zhong
Keyword(s):  
Data Compression ◽  
Vector Data

scholarly journals A Multiresolution Vector Data Compression Algorithm Based on Space Division

2020 ◽  
Vol 9 (12) ◽  
pp. 721
Author(s):  
Dongge Liu ◽  
Tao Wang ◽  
Xiaojuan Li ◽  
Yeqing Ni ◽  
Yanping Li ◽  
...  
Keyword(s):  
Data Compression ◽  
Spatial Data ◽  
Grid Cell ◽  
Lossless Compression ◽  
Floating Point ◽  
Vector Data ◽  
Binary Forms ◽  
Accuracy Requirement ◽  
Space Division ◽  
Point Type

Vector data compression can significantly improve efficiency of geospatial data management, visualization and data transmission over internet. Existing compression methods are either based on information theory for lossless compression mainly or based on map generalization methods for lossy compression. Coordinate values of vector spatial data are mostly represented using floating-point type in which data redundancy is small and compression ratio using lossy algorithms is generally better than that of lossless compression algorithms. The purpose of paper is to implement a new algorithm for efficient compression of vector data. The algorithm, named space division based compression (SDC), employs the basic idea of linear Morton and Geohash encoding to convert floating-point type values to strings of binary chain with flexible accuracy level. Morton encoding performs multiresolution regular spatial division to geographic space. Each level of regular grid splits space horizontally and vertically. Row and column numbers in binary forms are bit interleaved to generate one integer representing the location of each grid cell. The integer values of adjacent grid cells are proximal to each other on one dimension. The algorithm can set the number of divisions according to accuracy requirements. Higher accuracy can be achieved with more levels of divisions. In this way, multiresolution vector data compression can be achieved accordingly. The compression efficiency is further improved by grid filtering and binary offset for linear and point geometries. The vector spatial data compression takes visual lossless distance on screen display as accuracy requirement. Experiments and comparisons with available algorithms show that this algorithm produces a higher data rate saving and is more adaptable to different application scenarios.

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