A PCA based fast vector quantization coding method for spectral imagery

Author

Hong, Wei ; Wang, Suyu ; Li, Xiaoguang ; Zhuo, Li ; Zhu, Qing

Author_Institution

Signal & Inf. Process. Lab., Beijing Univ. of Technol., Beijing, China

Volume

4

fYear

2010

fDate

9-11 July 2010

Firstpage

216

Lastpage

219

Abstract

In this paper, a PCA (Principal Component Analysis) based fast vector quantization coding method for spectral imagery has been proposed. Firstly, PCA is used to concentrate effective information of image into a few PCs (Principal Components). Then a PC selected method based on the eigenvalues is used to extract the PCs that contain the main information. Furthermore, an improved fast vector quantization (VQ) algorithm is exploited to encode the selected PCs. Experimental results show that, under the same coding conditions, the proposed method can achieve 13-16 dB higher reconstruction quality than JPEG2000 image coding standard.

Keywords

eigenvalues and eigenfunctions; image coding; image reconstruction; principal component analysis; vector quantisation; JPEG2000 image coding standard; PCA based fast vector quantization coding method; eigenvalues; high reconstruction quality; principal component analysis; spectral imagery; Eigenvalues and eigenfunctions; Lakes; Moon; Noise; Streaming media; Transform coding; PCA; Spectral Imagery; VQ;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-5537-9

Type

conf

DOI

10.1109/ICCSIT.2010.5565204

Filename

5565204