Reference line approach for vector data compression

Akimov, Alexander; Kolesnikov, Alexander; Fränti, Pasi

doi:10.1109/icip.2004.1421447

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…A differential vector for point (x i , y i ) is determined by taking differences (or deltas) between the current point (x i , y i ) and its previous point on the line segment (x i-1 , y i-1 ) [5]. For example, consider a line with coordinates (1, 1), (2, 3), (3,2) and (5,4). The line is encoded using differential vectors as follows: (1,1) , (1,2) , (1,-1) and (2,2) using the formula of Delta(x) = X i -X (i-1) and Delta(y) = Y i -Y (i-1) .…”

Section: The Clustering Approachmentioning

confidence: 99%

“…A lossy compression algorithm is proposed in [3,16]. The basic steps of the algorithm can be described as follows.…”

Section: Figure 1: Node Prediction During Decompressionmentioning

confidence: 99%

“…In this step, we discuss two modes: (i) In the aggressive mode of the reference line approach[3], which achieves higher compression ratio but less accurate recovery, the original coordinate values for the two ends of the line are stored, along with the number of intermediate points and the error tolerance e. At the decompression phase, the algorithm runs two threads of equations to predict the intermediate points of the original curve. The first to restore points closest to the left side of the reference line and the second for the points closest to the right end point of the line.…”

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confidence: 99%

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Road network compression techniques in spatiotemporal embedded systems

Khot

Hendawi

Nascimento

et al. 2014

Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

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The storage and manipulation of road network graphs are critical to navigational and location-based services. The widespread use of GPS devices combined with low-cost storage has enabled portable and embedded systems to handle several spatiotemporal operations against a natively-stored version of the road network graph. However, the increase in amount of map detail data over the years poses several challenges for such systems. In this paper, we highlight the need for adoption of road network compression techniques in embedded geographic information systems. We also provide a technical overview of proposed road network compression techniques.

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Section: The Clustering Approachmentioning

confidence: 99%

“…A lossy compression algorithm is proposed in [3,16]. The basic steps of the algorithm can be described as follows.…”

Section: Figure 1: Node Prediction During Decompressionmentioning

confidence: 99%

mentioning

confidence: 99%

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Road network compression techniques in spatiotemporal embedded systems

Khot

Hendawi

Nascimento

et al. 2014

Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoStreaming

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“…The common practice to address the storage/transmission issue in GIS applications is to send a raster image, which is a rendition of requested geospatial data at the server side, to the client. Google Maps 1 and Microsoft Live Local 2 are examples of such an approach. However, the transmitted raster image is only a visual representation of the geospatial data and hence does not include the geometric objects and corresponding metadata.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the boundary of a hexagon-shaped park must be invariant to the compression. The problem of vector data compression has been addressed by two independent communities: 1) data compression community that takes numerical approaches embedded in compression schemes to focus on the problem of compressing road segments [1][2][3]. The advantages of these methods lie in their ability to compress a given vector data up to a certain level.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Resolution Compression Scheme for EfficientWindow Queries over Road Network Databases

Khoshgozaran

Khodaei

Sharifzadeh

et al. 2006

Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)

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Vector data and in particular road networks are being queried, hosted, and processed by many application domains such as mobile computing. However, many hosting/processing clients such as PDAs cannot afford this bulky data due to their storage and transmission limitations. In particular, the result of a typical spatial query such as window query is too huge for a transfer-and-store scenario. While several general vector data compression schemes have been studied by different communities, we propose a novel approach in vector data compression which is easily integrated within a geospatial query processing system. It uses line aggregation to reduce the number of relevant tuples and Huffman compression to achieve a multi-resolution compressed representation of a road network database. Our empirical results verify that our approach exhibits both a high compression ratio and fast query processing.

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