Lossless compression of hyperspectral images using hybrid context prediction

In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband...

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Veröffentlicht in:Optics express Jg. 20; H. 7; S. 8199
Hauptverfasser: Liang, Yuan, Li, Jianping, Guo, Ke
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 26.03.2012
Schlagworte:
Algorithms
Spectrum Analysis - methods
ISSN:1094-4087, 1094-4087
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Abstract In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost.
AbstractList In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost.In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost.
In this letter a new algorithm for lossless compression of hyperspectral images using hybrid context prediction is proposed. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. The decorrelation stage supports both intraband and interband predictions. The intraband (spatial) prediction uses the median prediction model, since the median predictor is fast and efficient. The interband prediction uses hybrid context prediction. The hybrid context prediction is the combination of a linear prediction (LP) and a context prediction. Finally, the residual image of hybrid context prediction is coded by the arithmetic coding. We compare the proposed lossless compression algorithm with some of the existing algorithms for hyperspectral images such as 3D-CALIC, M-CALIC, LUT, LAIS-LUT, LUT-NN, DPCM (C-DPCM), JPEG-LS. The performance of the proposed lossless compression algorithm is evaluated. Simulation results show that our algorithm achieves high compression ratios with low complexity and computational cost.
Author Guo, Ke
Liang, Yuan
Li, Jianping
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Cites_doi 10.1109/LSP.2005.862604
10.1109/LGRS.2005.859942
10.1109/LGRS.2007.890546
10.1117/12.478794
10.1109/LSP.2009.2016834
10.1109/83.855427
10.1016/S0165-1684(02)00305-5
10.1109/LSP.2004.840907
10.1117/12.690659
10.1109/LGRS.2003.822312
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References Aiazzi (oe-20-7-8199-R3) 2009; 16
Tang (oe-20-7-8199-R6) 2003; 1
Rizzo (oe-20-7-8199-R11) 2005; 12
Huang (oe-20-7-8199-R5) 2006; 6365
Aiazzi (oe-20-7-8199-R1) 2002; 82
Magli (oe-20-7-8199-R2) 2004; 1
Zhang (oe-20-7-8199-R8) 2007; 4
Mielikainen (oe-20-7-8199-R4) 2006; 13
Penna (oe-20-7-8199-R7) 2006; 3
Weinberger (oe-20-7-8199-R9) 2000; 9
Mielikainen (oe-20-7-8199-R10) 2002; 4725
References_xml – volume: 13
  start-page: 157
  year: 2006
  ident: oe-20-7-8199-R4
  publication-title: IEEE Signal Process. Lett.
  doi: 10.1109/LSP.2005.862604
– volume: 3
  start-page: 125
  year: 2006
  ident: oe-20-7-8199-R7
  publication-title: IEEE Geosci. Remote Sens. Lett.
  doi: 10.1109/LGRS.2005.859942
– volume: 4
  start-page: 283
  year: 2007
  ident: oe-20-7-8199-R8
  publication-title: IEEE Geosci. Remote Sens. Lett.
  doi: 10.1109/LGRS.2007.890546
– volume: 4725
  start-page: 600
  year: 2002
  ident: oe-20-7-8199-R10
  publication-title: Proc. SPIE
  doi: 10.1117/12.478794
– volume: 16
  start-page: 481
  year: 2009
  ident: oe-20-7-8199-R3
  publication-title: IEEE Signal Process. Lett.
  doi: 10.1109/LSP.2009.2016834
– volume: 9
  start-page: 1309
  year: 2000
  ident: oe-20-7-8199-R9
  publication-title: IEEE Trans. Image Process.
  doi: 10.1109/83.855427
– volume: 1
  start-page: 1037
  year: 2003
  ident: oe-20-7-8199-R6
  publication-title: Proc.SPIE/IS&T Electron, Imaging
– volume: 82
  start-page: 1619
  year: 2002
  ident: oe-20-7-8199-R1
  publication-title: Signal Process.
  doi: 10.1016/S0165-1684(02)00305-5
– volume: 12
  start-page: 138
  year: 2005
  ident: oe-20-7-8199-R11
  publication-title: IEEE Signal Process. Lett.
  doi: 10.1109/LSP.2004.840907
– volume: 6365
  start-page: 63650L
  year: 2006
  ident: oe-20-7-8199-R5
  publication-title: Proc. SPIE
  doi: 10.1117/12.690659
– volume: 1
  start-page: 21
  year: 2004
  ident: oe-20-7-8199-R2
  publication-title: IEEE Geosci. Remote Sens. Lett.
  doi: 10.1109/LGRS.2003.822312
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SubjectTerms Algorithms
Spectrum Analysis - methods
Title Lossless compression of hyperspectral images using hybrid context prediction
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