Title :
Multistage Lattice Vector Quantization for Hyperspectral Image Compression
Author :
Liu, Ying ; Pearlman, William A.
Author_Institution :
Rensselaer Polytech. Inst., Troy
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;
Conference_Titel :
Signals, Systems and Computers, 2007. ACSSC 2007. Conference Record of the Forty-First Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4244-2109-1
Electronic_ISBN :
1058-6393
DOI :
10.1109/ACSSC.2007.4487355
