Using Vector Quantization for Image Processing

Pamela C. Cosman, Karen L. Oehler, Robert M. Gray, Eve A. Riskin

Research output: Contribution to journalArticlepeer-review

90 Scopus citations

Abstract

Image compression is the process of reducing the number of bits required to represent an image. Vector quantization, the mapping of pixel intensiry vectors into binary vectors indexing a limited number of possible reproductions, is a popular image compression algorithm. Compression has traditionally been done with little regard for image processing operations that may precede or follow the compression step. Recent work has used vector quantization both to simplify image processing tasks-such as enhancement, classification, halftoning, and edge detectio-nd to reduce the computational complexiry by performing them simultaneously with the compression. After briefly reviewing the fundamental ideas of vector quantization, we present a survey of vector quantization algorithms that perform image processing.

Original languageEnglish
Pages (from-to)1326-1341
Number of pages16
JournalProceedings of the IEEE
Volume81
Issue number9
DOIs
StatePublished - Sep 1993

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