Entropy-constrained predictive trellis coded quantization and compression of hyperspectral imagery.

Persistent Link:
http://hdl.handle.net/10150/186748
Title:
Entropy-constrained predictive trellis coded quantization and compression of hyperspectral imagery.
Author:
Abousleman, Glen Patrick.
Issue Date:
1994
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
A training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the MSE performance of an 8-state ECPTCQ system exceeds that of entropy-constrained DPCM by up to 1.0 dB. In addition, three systems--an ECPTCQ system, a 3-D Discrete Cosine Transform (DCT) system and a hybrid system--are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system utilizes a 2-D DCT and ECPTCQ. The 2-D DCT is used to transform all nonoverlapping 8 x 8 blocks of each band. Thereafter, ECPTCQ is used to encode the transform coefficients in the spectral dimension. The 3-D DCT system uses TCQ to encode transform coefficients resulting from the application of an 8 x 8 x 8 DCT. The hybrid system uses DPCM to spectrally decorrelate the data, while a 2-D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies for all systems are discussed. Entropy-constrained codebooks are optimized for various generalized Gaussian distributions using a modified version of the generalized Lloyd algorithm. The first system can compress a hyperspectral image sequence at 0.125 bits/pixel/band while retaining an average peak signal-to-noise ratio of greater than 43 dB over the spectral bands. The 3-D DCT and hybrid systems achieve compression ratios of 77:1 and 69:1 while maintaining average peak signal-to-noise ratios of 40.75 dB and 40.29 dB, respectively, over the coded bands.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Electrical and Computer Engineering; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Marcellin, Michael W.; Hunt, Bobby R.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleEntropy-constrained predictive trellis coded quantization and compression of hyperspectral imagery.en_US
dc.creatorAbousleman, Glen Patrick.en_US
dc.contributor.authorAbousleman, Glen Patrick.en_US
dc.date.issued1994en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractA training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme is presented for encoding autoregressive sources. For encoding a first-order Gauss-Markov source, the MSE performance of an 8-state ECPTCQ system exceeds that of entropy-constrained DPCM by up to 1.0 dB. In addition, three systems--an ECPTCQ system, a 3-D Discrete Cosine Transform (DCT) system and a hybrid system--are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system utilizes a 2-D DCT and ECPTCQ. The 2-D DCT is used to transform all nonoverlapping 8 x 8 blocks of each band. Thereafter, ECPTCQ is used to encode the transform coefficients in the spectral dimension. The 3-D DCT system uses TCQ to encode transform coefficients resulting from the application of an 8 x 8 x 8 DCT. The hybrid system uses DPCM to spectrally decorrelate the data, while a 2-D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies for all systems are discussed. Entropy-constrained codebooks are optimized for various generalized Gaussian distributions using a modified version of the generalized Lloyd algorithm. The first system can compress a hyperspectral image sequence at 0.125 bits/pixel/band while retaining an average peak signal-to-noise ratio of greater than 43 dB over the spectral bands. The 3-D DCT and hybrid systems achieve compression ratios of 77:1 and 69:1 while maintaining average peak signal-to-noise ratios of 40.75 dB and 40.29 dB, respectively, over the coded bands.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairMarcellin, Michael W.en_US
dc.contributor.chairHunt, Bobby R.en_US
dc.contributor.committeememberSchowengerdt, Roberten_US
dc.identifier.proquest9426576en_US
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