Classified coset coding based lossless compression of hyperspectral images

2011 ◽  
Author(s):  
Juan Song ◽  
Yunsong Li ◽  
Haiying Liu ◽  
Xianyun Wu ◽  
Keyan Wang
Keyword(s):  
Lossless Compression ◽  
Hyperspectral Images ◽  
Coset Coding

Lossless Compression of Hyperspectral Images Based on Classification and Coset Coding

2011 ◽  
Vol 33 (1) ◽  
pp. 231-234
Author(s):  
Juan Song ◽  
Cheng-ke Wu ◽  
Jing Zhang ◽  
Hai-ying Liu
Keyword(s):  
Lossless Compression ◽  
Hyperspectral Images ◽  
Coset Coding

Lossless compression of hyperspectral images using backward search in context window

2015 ◽  
Vol 23 (8) ◽  
pp. 2376-2383
Author(s):  
高放 GAO Fang ◽  
刘宇 LIU Yu ◽  
郭树旭 GUO Shu-xu
Keyword(s):  
Lossless Compression ◽  
Hyperspectral Images

Distributed lossless compression of hyperspectral images based on multi-band prediction

2012 ◽  
Vol 20 (4) ◽  
pp. 906-912 ◽  
Author(s):  
粘永健 NIAN Yong-jian ◽  
辛勤 XIN Qin ◽  
汤毅 TANG Yi ◽  
万建伟 WAN Jian-wei
Keyword(s):  
Lossless Compression ◽  
Hyperspectral Images ◽  
Multi Band

scholarly journals Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression

2019 ◽  
Vol 11 (21) ◽  
pp. 2461 ◽  
Author(s):  
Kevin Chow ◽  
Dion Tzamarias ◽  
Ian Blanes ◽  
Joan Serra-Sagristà
Keyword(s):  
Data Structure ◽  
Hyperspectral Image ◽  
Lossless Compression ◽  
Hyperspectral Images ◽  
Direct Access ◽  
Bit Rate ◽  
Compression Algorithms ◽  
Real Time Processing ◽  
Time Processing ◽  
Differential Encoding

This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.

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