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 ◽  
...  

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.

2008 ◽  
Vol 28 (1) ◽  
pp. 168-170 ◽  
Author(s):  
Fei-xiang CHEN ◽  
Zhi-wu ZHOU ◽  
Jian-bing ZHANG

2018 ◽  
Vol 4 (12) ◽  
pp. 142 ◽  
Author(s):  
Hongda Shen ◽  
Zhuocheng Jiang ◽  
W. Pan

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.


2018 ◽  
Vol 117 ◽  
pp. 138-153 ◽  
Author(s):  
Yuliya Marchetti ◽  
Hai Nguyen ◽  
Amy Braverman ◽  
Noel Cressie

Author(s):  
Kai Zhang ◽  
Xiaoya Wang ◽  
Xiaopeng Ma ◽  
Jian Wang ◽  
Yongfei Yang ◽  
...  
Keyword(s):  

Author(s):  
B. Pradhan ◽  
K. Sandeep ◽  
Shattri Mansor ◽  
Abdul Rahman Ramli ◽  
Abdul Rashid B. Mohamed Sharif

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

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