## Using Vector Quantization for Image Processing (1993)

Venue: | Proc. IEEE |

Citations: | 23 - 1 self |

### BibTeX

@INPROCEEDINGS{Cosman93usingvector,

author = {Pamela C. Cosman and Karen L. Oehler and Eva A. Riskin and Robert M. Gray},

title = {Using Vector Quantization for Image Processing},

booktitle = {Proc. IEEE},

year = {1993},

pages = {1326--1341}

}

### Years of Citing Articles

### OpenURL

### Abstract

Image compression is the process of reducing the number of bits required to represent an image. Vector quantization, the mapping of pixel intensity 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 detection -- and to reduce the computational complexity 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. 1 Introduction Data compression is the mapping of a data set into a bit stream to decrease the number of bits required to represent the data set. With data compression, one can st...