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dc.contributor.advisorZeng, Wenjun, 1967-eng
dc.contributor.authorLiu, Weieng
dc.date.issued2008eng
dc.date.submitted2008 Falleng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on November 10, 2010).eng
dc.descriptionThe 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.eng
dc.descriptionDissertation advisor: Dr. Wenjun Zengeng
dc.descriptionVita.eng
dc.descriptionIncludes bibliographical references (p. 127-137).eng
dc.descriptionPh. D. University of Missouri--Columbia 2008.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Computer science.eng
dc.description.abstractConventional multimedia compression leverages the source statistics at the encoder side. This is not suitable for some emerging applications such as wireless sensor networks, where the encoders usually have limited functionalities and power supplies, therefore it is desired to shift the bulk of computational burden to the decoder side. The resulting new coding paradigm is called distributed source coding (DSC). Most practical DSC schemes only achieve good results when a priori knowledge about the source statistics is assumed. For DSC of real-world sources such as images and videos, such knowledge is not really available. In this dissertation, we focus on designing decoder-side learning schemes for better understanding of the source statistics, based on which practical DSC systems can be built for high-efficiency, low-cost, and secure multimedia communications. We have studied distributed video coding and compression of encrypted images and videos. We propose to enable partial access to the current source through progressive decoding, such that the decoder's knowledge about the source statistics can be progressively refined. The resulting schemes have achieved significant improvement in coding efficiency. We also studied the rate allocation problem to optimize the power consumption in transmitting multiple correlated sources over a wireless sensor network. The framework developed in this dissertation will provide significant insights and become important building blocks in distributed video applications, including those that are of significant importance to the national security, agriculture, economy, and healthcare.eng
dc.format.extent138 pageseng
dc.identifier.oclc681912333eng
dc.identifier.urihttps://hdl.handle.net/10355/9091
dc.identifier.urihttps://doi.org/10.32469/10355/9091eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartof2008 Freely available dissertations (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Graduate School. Theses and Dissertations. Dissertations. 2008 Dissertationseng
dc.subject.lcshVideo compressioneng
dc.subject.lcshMultimedia communicationseng
dc.subject.lcshData compression (Computer science)eng
dc.subject.lcshDecoders (Electronics)eng
dc.titleDecoder-learning based distributed source coding for high-efficiency, low-cost and secure multimedia communicationseng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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