Classification Techniques for Digital Map Compression

Abstract

The performance of image classification techniques as applied to color cartographic maps is compared. These color maps have a lot of graininess due to imperfections in the printing process. This graininess decreases the efficiency of compression techniques. The color maps are classified using the K-means clustering algorithm and vector quantization with neighborhood classification to improve the visual quality and compression ratio. The classification is performed in various image representation schemes. The performance of the classifier is evaluated based on the visual quality of the classified image, the time required to classify the image and compression achieved on the classified image. The compression ratio after classification was higher than before classification.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1989
Accession Number
ADA495464

Entities

People

  • A. Bermúdez Martínez
  • H. Barad
  • H. Potlapalli
  • J. Pollard
  • Jason T. Ryan
  • M. C. Lohrenz
  • M. Y. Jaisimha

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Bomber Aircraft
  • Clustering
  • Coding
  • Compression Ratio
  • Computational Complexity
  • Computations
  • Electrical Engineering
  • Grids
  • Image Classification
  • Images
  • Latitude
  • Longitude
  • Machine Learning
  • Three Dimensional
  • Visual Inspection

Fields of Study

  • Computer science

Readers

  • Computer Vision.

Technology Areas

  • AI & ML