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dc.contributor.authorKasner, James Henry.
dc.creatorKasner, James Henry.en_US
dc.date.accessioned2011-10-31T18:38:43Z
dc.date.available2011-10-31T18:38:43Z
dc.date.issued1995en_US
dc.identifier.urihttp://hdl.handle.net/10150/187390
dc.description.abstractThe discrete wavelet transform has emerged as a powerful tool for the lossy compression of imagery. In this work, two adaptive still image wavelet coders are presented, each based on a variant of trellis coded quantization (TCQ). TCQ is an effective technique for quantizing memoryless sources with moderate complexity. Entropy-constrained trellis coded quantization (ECTCQ) and a newly developed quantizer, universal trellis coded quantization (UTCQ) form the basis of each system. UTCQ offers several advantages over ECTCQ. It requires no computationally expensive training algorithm, no stored codebooks, and performs "on-the-fly" training on a subset of the reconstruction levels. It is shown that the performance of eight state UTCQ is within ≈0.1 dB of eight state ECTCQ for most encoding rates. Unlike most systems present in the literature, these coders are complete end-to-end image compression/decompression systems. Adaptive arithmetic coding is used to generate output bitstreams. Several wavelet subblock classification schemes are investigated to improve the PSNR performance of the coders. The 3-map classification technique was seen to improve SNR performance by ≈0.8 dB for the Lenna image above 0.5 bpp. Perceptual coding improvements were developed for the UTCQ system as well. The image coders proved to be quite robust, performing well on a wide array of imagery. The quality of the encoded imagery from these systems is found to be very competitive with other coders in the literature and significantly better than JPEG-based coding.
dc.language.isoenen_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.titleAdaptive wavelet compression of imagery.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.contributor.chairMarcellin, Michael W.en_US
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.leveldoctoralen_US
dc.contributor.committeememberHunt, Bobby R.en_US
dc.contributor.committeememberRodriguez, Jeffrey J.en_US
dc.identifier.proquest9620447en_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.namePh.D.en_US
dc.description.noteThis item was digitized from a paper original and/or a microfilm copy. If you need higher-resolution images for any content in this item, please contact us at repository@u.library.arizona.edu.
dc.description.admin-noteOriginal file replaced with corrected file October 2023.
refterms.dateFOA2018-08-23T22:09:51Z
html.description.abstractThe discrete wavelet transform has emerged as a powerful tool for the lossy compression of imagery. In this work, two adaptive still image wavelet coders are presented, each based on a variant of trellis coded quantization (TCQ). TCQ is an effective technique for quantizing memoryless sources with moderate complexity. Entropy-constrained trellis coded quantization (ECTCQ) and a newly developed quantizer, universal trellis coded quantization (UTCQ) form the basis of each system. UTCQ offers several advantages over ECTCQ. It requires no computationally expensive training algorithm, no stored codebooks, and performs "on-the-fly" training on a subset of the reconstruction levels. It is shown that the performance of eight state UTCQ is within ≈0.1 dB of eight state ECTCQ for most encoding rates. Unlike most systems present in the literature, these coders are complete end-to-end image compression/decompression systems. Adaptive arithmetic coding is used to generate output bitstreams. Several wavelet subblock classification schemes are investigated to improve the PSNR performance of the coders. The 3-map classification technique was seen to improve SNR performance by ≈0.8 dB for the Lenna image above 0.5 bpp. Perceptual coding improvements were developed for the UTCQ system as well. The image coders proved to be quite robust, performing well on a wide array of imagery. The quality of the encoded imagery from these systems is found to be very competitive with other coders in the literature and significantly better than JPEG-based coding.


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