RKLT-Based Lossless Hyperspectral Image Compression Combined with Principal Components Selection

Summary form only given: In this paper a lossless compression method for hyperspectral image is given. RKLT-based scheme was first presented by combining with 3D prediction, principal component selection, positive mapping followed by a range coder. The proposed method avoids the float number of coef...

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Vydané v:	DCC (Los Alamitos, Calif.) s. 584
Hlavní autori:	Hao Chen, Yi Hua, Shuang Zhou
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.03.2016
Predmet:	Data compression Diversity reception Hyperspectral imaging Image coding Matrix decomposition Three-dimensional displays Two dimensional displays
ISSN:	1068-0314
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Popis
Shrnutí:	Summary form only given: In this paper a lossless compression method for hyperspectral image is given. RKLT-based scheme was first presented by combining with 3D prediction, principal component selection, positive mapping followed by a range coder. The proposed method avoids the float number of coefficient which can make it much more easier to be processed on hardware. Numerical experiments show that the proposed method outperforms the state-of-the-art methods (i.e., LUT-NN, LAIS-LUT, and AI) by 13% in terms of compression ratio.
ISSN:	1068-0314
DOI:	10.1109/DCC.2016.127