Vector map compression: A clustering approach

Shashi Shekhar, Yan Huana, Judy Djugash, Changqing Zhou

Research output: Contribution to conferencePaperpeer-review

26 Scopus citations

Abstract

Vector maps (e.g. road maps) are widely used in a variety of applications such as Geographic Information Systems(GIS), Intelligent Transportation Systems(ITS) and mobile computing. However, the relatively large size of vector maps has in some cases negatively impacted their usage and application in these systems because of the small storage available with mobile wireless devices or the limited bandwidth of the data transportation. In these cases, data compression techniques need to be applied on these vector maps to handle larger datasets and faster data transportation. Among all the data compression techniques, dictionary-based compression is a good candidate since encoding and decoding do not need a significantly large amount of computing resources. This paper explores the problem of dictionary design for dictionary based vector map compression. We propose a novel clustering-based dictionary design which adapts the dictionary to a given dataset, yielding better approximation. Experimental evaluation shows that when the dictionary size is fixed, the proposed clustering-based technique achieves lower error compared with conventional dictionary compression approaches.

Original languageEnglish (US)
Pages74-80
Number of pages7
StatePublished - Dec 1 2002
EventTenth ACM International Symposium on Advances in Geographic Information Systems - McLean, VA, United States
Duration: Nov 8 2002Nov 9 2002

Other

OtherTenth ACM International Symposium on Advances in Geographic Information Systems
CountryUnited States
CityMcLean, VA
Period11/8/0211/9/02

Keywords

  • Clustering
  • Dictionary design
  • Vector map compression

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