Halftoning and Image Processing Algorithms

Abstract

The focus of this study was theoretical and experimental research on topics in the fields of color half toning, image processing and compression, and image quality. Our goals in this research were to advance the understanding in image science for our new halftone algorithm and to contribute to image retrieval and noise theory for such imagery. In the field of color halftone printing, research was conducted on deriving a theoretical model of our new halftone algorithm based on a novel resampling of the output pixels, developing halftone algorithms for combining the speed advantages of halftone screening techniques with the quality advantages of error diffusion in the half toning of color maps, and on color image enhancement for halftone printing. In conjunction with this work, a software development effort was conducted both to implement efficiently the half toning algorithm itself and to ease its use through a graphical user interface. Research efforts were also conducted in the areas of remote sensing and image compression of color and monochrome images. In image compression we studied the use of controlled blurring to improve both lossless and lossy methods, like DOT based algorithms. In remote sensing we studied topics in image classification of a scene according to such categories as terrain, vegetation, and image quality.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1999
Accession Number
ADA369915

Entities

People

  • David M. Berfanger
  • Nicholas George

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Classification
  • Color Separation
  • Compression
  • Computer Programming
  • Data Compression
  • Diffusion
  • Graphical User Interface
  • Image Classification
  • Image Compression
  • Image Processing
  • Object Recognition
  • Pattern Recognition
  • Recognition
  • Remote Sensing
  • Scientists
  • User Interface

Fields of Study

  • Computer science

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms