{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/10659"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/10659","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Significance-linked connected component analysis+ for wavelet image coding","abstract":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] The Significance-Linked Connected Component Analysis+ (SLCCA+) is a efficient wavelet image coding algorithm that extends SLCCA by using new data organization and representation (DOR) and SLCCA+ use a definition of context model for the adaptive arithmetic coding. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA+ outperforms SLCCA. For example, for the Lena image, at 0.1 bit/pixel, SLCCA+ outperforms SLCCA by 0.1 dB in PSNR. This outstanding performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.","abstract_html":"[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] The Significance-Linked Connected Component Analysis+ (SLCCA+) is a efficient wavelet image coding algorithm that extends SLCCA by using new data organization and representation (DOR) and SLCCA+ use a definition of context model for the adaptive arithmetic coding. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA+ outperforms SLCCA. For example, for the Lena image, at 0.1 bit/pixel, SLCCA+ outperforms SLCCA by 0.1 dB in PSNR. This outstanding performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.","abstract_has_math":false,"creators":["Zhang, Cheng, 1985-"],"institution":"University of Missouri--Columbia","degree_name":"M.S.","degree_level":"Masters","degree_discipline":"Electrical and computer engineering (MU)","degree_department":null,"school":null,"contributors":[],"advisors":["Zhuang, Xinhua","He, Zhihai, 1973-"],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010","date_published":"2010","updated_at":"2026-07-24T03:07:46Z","subjects":[],"languages":["eng","English"],"rights":["Access to files is limited to the University of Missouri--Columbia with SSO login."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.32469/10355/10659"],"render_values":[{"text":"https://doi.org/10.32469/10355/10659","href":"https://doi.org/10.32469/10355/10659","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10355/10659","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zhuang, Xinhua","He, Zhihai, 1973-"]},{"key":"dc:creator","label":"Author","values":["Zhang, Cheng, 1985-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2011-05-06T14:29:43Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2011-05-06T14:29:43Z"]},{"key":"dc:date.issued","label":"Date","values":["2010"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Columbia"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and computer engineering (MU)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Columbia"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Access to files is limited to the University of Missouri--Columbia with SSO login."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.32469/10355/10659"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/10659"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.","Title from PDF of title page (University of Missouri--Columbia, viewed on April 29, 2011).","Thesis advisors: Dr. Xinhua Zhuang and Dr. Zhihai He.","M. 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Thus both the encoding and decoding procedures are fast."]},{"key":"dc:title","label":"Title","values":["Significance-linked connected component analysis+ for wavelet image coding"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zhuang, Xinhua","He, Zhihai, 1973-"],"dc:creator":["Zhang, Cheng, 1985-"],"dc:date.accessioned":["2011-05-06T14:29:43Z"],"dc:date.available":["2011-05-06T14:29:43Z"],"dc:date.issued":["2010"],"dc:description":["The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.","Title from PDF of title page (University of Missouri--Columbia, viewed on April 29, 2011).","Thesis advisors: Dr. Xinhua Zhuang and Dr. Zhihai He.","M. S. University of Missouri-Columbia 2010."],"dc:description.abstract":["[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] The Significance-Linked Connected Component Analysis+ (SLCCA+) is a efficient wavelet image coding algorithm that extends SLCCA by using new data organization and representation (DOR) and SLCCA+ use a definition of context model for the adaptive arithmetic coding. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA+ outperforms SLCCA. For example, for the Lena image, at 0.1 bit/pixel, SLCCA+ outperforms SLCCA by 0.1 dB in PSNR. This outstanding performance is achieved without using any optimal bit allocation procedure. 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