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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Bibliographic Details
Published in:DCC (Los Alamitos, Calif.) p. 584
Main Authors: Hao Chen, Yi Hua, Shuang Zhou
Format: Conference Proceeding
Language:English
Published: IEEE 01.03.2016
Subjects:
Data compression
Diversity reception
Hyperspectral imaging
Image coding
Matrix decomposition
Three-dimensional displays
Two dimensional displays
ISSN:1068-0314
Online Access:Get full text
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Summary: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