A new nonlinear quantizer for image processing within nonextensive statistics

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dc.contributor.authorKilic I.
dc.contributor.authorKayacan O.
dc.date.accessioned2024-07-22T08:22:50Z
dc.date.available2024-07-22T08:22:50Z
dc.date.issued2007
dc.description.abstractIn this study, we introduce a new nonlinear quantizer for image processing by using Tsallis entropy. Lloyd-Max quantizer is commonly used in minimizing the quantization errors. We report that the new introduced technique works better than Lloyd-Max one for selected standard images and could be an alternative way to minimize the quantization errors for image processing. We, therefore, hopefully expect that the new quantizer could be a useful tool for all the remaining process after image quantization, such as coding (lossy and lossless compression). © 2007 Elsevier B.V. All rights reserved.
dc.identifier.DOI-ID10.1016/j.physa.2007.03.028
dc.identifier.issn03784371
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/19265
dc.language.isoEnglish
dc.subjectError analysis
dc.subjectStatistical mechanics
dc.subjectVector quantization
dc.subjectImage quantization
dc.subjectNonlinear quantization
dc.subjectQuantization errors
dc.subjectTsallis statistics
dc.subjectImage processing
dc.titleA new nonlinear quantizer for image processing within nonextensive statistics
dc.typeArticle

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