Context-based predictive lossless coding for hyperspectral images

Author

De Giusti, A. ; Andriani, S. ; Mian, G.A.

Author_Institution

Dept. of Inf. Eng., Univ. of Padova, Padua, Italy

fYear

2005

fDate

4-8 Sept. 2005

Firstpage

1

Lastpage

4

Abstract

A cluster-based lossless compression algorithm for hyperspectral images is presented. Clustering is carried out on the original data according to the vectors spectra, and it is used to set up multiple contexts for predictive lossless coding. Low-order prediction is performed using adaptive Linear Least Squares (LLS) estimation which exploits the additional information provided by clustering. Prediction errors are then entropy-coded using an adaptive arithmetic coder also driven by data clusters. The proposed scheme is used to losslessly code a set of AVIRIS hyperspectral images. Comparisons with the JPE-GLS, JPEG-2000 and the clustered DPCM coding algorithms are given.

Keywords

data compression; entropy codes; hyperspectral imaging; image coding; least squares approximations; AVIRIS hyperspectral image; DPCM coding algorithm; JPE-GLS; JPEG-2000; adaptive arithmetic coder; adaptive linear least squares estimation; cluster-based lossless compression algorithm; context-based predictive lossless coding; entropy-coding; low-order prediction; prediction error; vectors spectra; Algorithm design and analysis; Clustering algorithms; Encoding; Hyperspectral imaging; Image coding; Prediction algorithms; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference, 2005 13th European

Conference_Location

Antalya

Print_ISBN

978-160-4238-21-1

Type

conf

Filename

7078235