Progressive vector quantization on a massively parallel SIMD machine with application to multispectral image data

Author

Manohar, Mareboyana ; Tilton, James C.

Author_Institution

Dept. of Comput. Sci., Bowie State Univ., MD, USA

Volume

5

Issue

1

fYear

1996

fDate

1/1/1996 12:00:00 AM

Firstpage

142

Lastpage

147

Abstract

This correspondence discusses a progressive vector quantization (VQ) compression approach, which decomposes image data into a number of levels using full-search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIMD machine. We demonstrate progressive VQ on multispectral imagery obtained from the advanced very high resolution radiometer (AVHRR) and other earth-observation image data, and investigate the tradeoffs in selecting the number of decomposition levels and codebook training method

Keywords

geophysical signal processing; image coding; image reconstruction; parallel processing; remote sensing; vector quantisation; advanced very high resolution radiometer; codebook training method; decomposition levels; full-search VQ; image data decomposition; lossless compression; lossless reconstruction; massively parallel SIMD machine; multispectral image data; multispectral imagery; progressive vector quantization; Concurrent computing; Decoding; Dictionaries; Image coding; Image reconstruction; Image resolution; Multispectral imaging; Radiometry; Space technology; Vector quantization;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.481678

Filename

481678