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dc.contributor.advisorTummala, Murali
dc.contributor.authorWatkins, Bruce E.
dc.dateSeptember 1991
dc.date.accessioned2013-01-23T21:54:07Z
dc.date.available2013-01-23T21:54:07Z
dc.date.issued1991-09
dc.identifier.urihttp://hdl.handle.net/10945/25801
dc.description.abstractThis thesis investigates the application of artificial neural networks for the compression of image data. An algorithm is developed using the competitive learning paradigm which takes advantage of the parallel processing and classification capability of neural networks to produce an efficient implementation of vector quantization. Multi-Stage, tree searched, and classification vector quantization codebook design techniques are adapted to the neural network design to reduce the computational cost and hardware requirements. The results show that the new algorithm provides a substantial reduction in computational costs and an improvement in performance.en_US
dc.description.urihttp://archive.org/details/datcompressionus1094525801
dc.format.extent84 p.;28 cm.en_US
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.titleData compression using artificial neural networks.en_US
dc.typeThesisen_US
dc.contributor.secondreaderTherrien, Charles W.
dc.subject.authorNeural Networksen_US
dc.subject.authorVector Quantizationen_US
dc.subject.authorImage Codingen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameDegree of Electrical Engineeren_US
etd.thesisdegree.levelProfessional Degreeen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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