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Sampling, Interpolation, and Quantization

  • William F. Schreiber
Part of the Springer Series in Information Sciences book series (SSINF, volume 15)

Abstract

This chapter deals with the conversion between real (i.e., spatially and temporally continuous) images and their discrete representation. The mathematics of the conversion processes is discussed and is found to be straightforward. The introduction of perceptual considerations makes the subject more complex, but also more rewarding. It is shown that substantial improvements in image quality, for a given amount of digital data, are possible using perceptual, rather than mathematical, criteria, particularly in the choice of presampling filters and postsampling interpolators.Similarly, perceptual considerations can be brought to bear on the design of quantizers. Appropriate placement of quantization levels and the use of randomization techniques can significantly reduce the visibility of quantization noise.

Keywords

Impulse Response Quantization Noise Sampling Theorem Discrete Representation Discrete Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • William F. Schreiber
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceMassachusetts Institute of TechnologyCambridgeUSA

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