Progressive vector compression for high-accuracy vector map data

Abstract

With the increase in the number of applications using digital vector maps and the development of surveying techniques, a large volume of GIS (geographic information system) vector maps having high accuracy and precision is being produced. However, to achieve their effective transmission while preserving their high positional quality, these large amounts of vector map data need to be compressed. This paper presents a compression method based on a bin space partitioning data structure, which preserves a high-level accuracy and exact precision of spatial data. To achieve this, the proposed method a priori divides a map into rectangular local regions and classifies the bits of each object in the local regions to three types of bins, defined as category bin (CB), direction bin (DB), and accuracy bin (AB). Then, it encodes objects progressively using the properties of the classified bins, such as adjacency and orientation, to obtain the optimum compression ratio. Experimental results verify that our method can encode vector map data constituting less than 20% of the original map data at a 1-cm accuracy degree and that constituting less than 9% at a 1-m accuracy degree. In addition, its compression efficiency is greater than that of previous methods, whereas its complexity is lower for close to real-time applications.