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1、J.Vis.Commun.ImageR.23(2012)959–971ContentslistsavailableatSciVerseScienceDirectJ.Vis.Commun.ImageR.journalhomepage:www.elsevier.com/locate/jvciAdaptivequantizationusingpiecewisecompandingandscalingforGaussianmixtureLeiYang⇑,DapengWuDepartmentofElectr
2、icalandComputerEngineering,UniversityofFlorida,Gainesville,FL,UnitedStatesarticleinfoabstractArticlehistory:Quantizationisfundamentaltoanalog-to-digitalconverter(ADC)andsignalcompression.Inthispaper,Received1April2011weproposeanadaptivequantizerwithpi
3、ecewisecompandingandscalingforsignalsofGaussianmix-Accepted12June2012turemodel(GMM).Ouradaptivequantizeroperatesunderthreemodes,eachofwhichcorrespondstoAvailableonline4July2012differenttypesofGMM.Moreover,weproposeareconfigurablearchitecturetoimplement
4、ouradaptivequantizerinanADC.WealsouseittoquantizeimagesanddesignthetonemappingalgorithmforhighKeywords:dynamicrange(HDR)imagecompression.Ourexperimentalresultsshowthat(1)theproposedquantizerScalarquantizationisabletoachieveperformanceclosetotheoptimal
5、quantizer(i.e.,Lloyd–MaxquantizerforGMM)intheCompandingsenseofmeansquarederror(MSE),atmuchlowercomputationalcostthanit;(2)theproposedquantizerScalingLloyd–MaxquantizerisabletoachievemuchbetterMSEperformancethanauniformquantizer,atacostsimilartotheunif
6、ormGaussianmixturemodel(GMM)quantizer.TheproposedadaptivequantizerholdsgreatpotentialintheappilcationsoftheexistingADCAnalog-to-digitalconverter(ADC)andHDRimagecompression.Highdynamicrange(HDR)imagePublishedbyElsevierInc.Tonemapping1.Introductiontradi
7、ngoffquantizationperformancewithcomplexityfornonuni-formquantization.Especially,forhighratecompression,theper-Quantizationisacriticaltechniqueforanalog-to-digitalconver-formanceofcompandingcanapproachthatofLloyd–Maxsionandsignalcompression.Ononehand,m
8、anyinputsignalsarequantizationasymptotically.continuousanalogsignals,therefore,quantizationisindispensableLloyd–Maxquantizersandcompandersarealreadywelldevel-foranalog-to-digitalconverters(ADC)[1],whichareimportantopedforGaussiandistributionor
