Multistage Lattice Vector Quantization for Hyperspectral Image Compression

Author

Liu, Ying ; Pearlman, William A.

Author_Institution

Rensselaer Polytech. Inst., Troy

fYear

2007

fDate

4-7 Nov. 2007

Firstpage

930

Lastpage

934

Abstract

Lattice vector quantization (LVQ) offers substantial reduction in computational load and design complexity due to the lattice regular structure [1]. In this paper, we extended the SPIHT [2] coding algorithm with lattice vector quantization to code hyperspectral images. In the proposed algorithm, multistage lattice vector quantization (MLVQ) is used to exploit correlations between image slices, while offering successive refinement with low coding complexity and computation. Different four-dimensional lattices and significance metrics are considered. Their rate-distortion performance is compared with other 2D and 3D wavelet-based image compression algorithms.

Keywords

data compression; image coding; vector quantisation; hyperspectral image coding; hyperspectral image compression; image slices; lattice regular structure; multistage lattice vector quantization; Bit rate; Design engineering; Hyperspectral imaging; Image coding; Image processing; Lattices; Partitioning algorithms; Rate-distortion; Systems engineering and theory; Vector quantization; Lattice vector quantization; SPIHT algorithm; successive refinement; volume image compression;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2007. ACSSC 2007. Conference Record of the Forty-First Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

978-1-4244-2109-1

Electronic_ISBN

1058-6393

Type

conf

DOI

10.1109/ACSSC.2007.4487355

Filename

4487355