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Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/72869
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dc.titlePREDICTIVE CLASSIFIED VECTOR QUANTIZATION FOR IMAGE CODING.
dc.contributor.authorHui, Lucai
dc.contributor.authorNgan, King N.
dc.date.accessioned2014-06-19T05:12:54Z
dc.date.available2014-06-19T05:12:54Z
dc.date.issued1987
dc.identifier.citationHui, Lucai,Ngan, King N. (1987). PREDICTIVE CLASSIFIED VECTOR QUANTIZATION FOR IMAGE CODING. : 408-412. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72869
dc.description.abstractA novel method of vector quantization, called predictive classified vector quantization (PCVQ), is proposed for image coding. It utilizes spatial correlations across the boundaries of pixel blocks to achieve a high compression rate. The edge/shade and mean intensity information of each block can be predicted and classified. Each block is coded by a quantizer belonging to the same class. The PCVQ consists of compact subcodebooks and gives satisfactory performance at 0. 375 b/pixel.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.page408-412
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

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