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Using vector quantization for image processing

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4 Author(s)
Cosman, P.C. ; Inf. Syst. Lab., Stanford Univ., CA, USA ; Oehler, K.L. ; Riskin, E.A. ; Gray, R.M.

A review is presented of vector quantization, the mapping of pixel intensity vectors into binary vectors indexing a limited number of possible reproductions, which 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 detection, and to reduce the computational complexity by performing the tasks simultaneously with the compression. The fundamental ideas of vector quantization are explained, and vector quantization algorithms that perform image processing are surveyed

Published in:

Proceedings of the IEEE  (Volume:81 ,  Issue: 9 )