A Multiresolution Vector Data Compression Algorithm Based on Space Division
Bibliographic Data
| ID | 22032250 |
|---|---|
| Authors | Dongge Liu (0000-0003-4821-7033, Capital Normal University), Tao Wang (0000-0002-8367-8946, Capital Normal University, corresponding author), Xiaojuan Li (0000-0002-7510-6593, Capital Normal University), Yeqing Ni (Capital Normal University), Yanping Li (0009-0006-7880-4609, Capital Normal University), Zhao Jin (0000-0003-2547-4723, MOE Lab of 3D Spatial Data Acquisition and Application, Capital Normal University, Beijing 100048, China), Jin Zhao (0000-0002-5287-7205, Capital Normal University) |
| Year | 2020 |
| Volume | 9 |
| Issue | 12 |
| Pages | 721 |
| Publication date | 2020-12-02 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9120721 |
| OpenAlex | W3107402458 |
| Language | EN |
| Citations received | 1 |
| References cited | 24 |
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
Algorithm · Data compression · Data compression ratio · Image compression · Image processing · Lossless compression · Lossy compression · Texture compression · Advanced Data Compression Techniques · Algorithms and Data Compression · Computer Science · Distributed and Parallel Computing Systems · Artificial Intelligence
| Citations per year | 0.33 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |