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1、JournalofIntelligentandRoboticSystems28:39–59,2000.39©2000KluwerAcademicPublishers.PrintedintheNetherlands.LossyImageCompressionUsingWaveletsNICKD.PANAGIOTACOPULOS,KENFRIESENandSUKITLERTSUNTIVITDepartmentofElectricalEngineering,CollegeofEngineering,CaliforniaStateUniversity,LongBeach,CA90840,U.S.
2、A.(Received:15June1998;accepted:19July1999)Abstract.Inthispaper,wereporttheresultsoftheapplicationoftransformcodingimagedatacompressiontechniquesusingDaubechiesandCoifmanwavelets.Morespecifically,D2,D4,D8,D16,andC6,C12waveletswereused.Theresultsfromthesewaveletswerecomparedwiththoseofdiscretecosin
3、etransform.Theyclearlydemonstratedthesuperiorityofthewavelet-basedtechniquesbothincompressionratiosandimagequality,aswellasincomputationalspeed.Twoquantiza-tionmethodswereused:non-uniformscalarquantizationandpseudo-quantization.Bothproducedsatisfactoryresults(86–88%compressionratio,andacceptablei
4、magequality).Keywords:wavelet,waveletfunction,multiresolutionanalysis(MRA),filterbanks,lossycompres-sion,scalingfunctions,dilationequation,pseudo-quantization.1.IntroductionTheaimofimagecompressionistoreducethesizeofanimagewithorwithoutlossofinformation(lossyorlosslesscompression).Oneofthemostcomm
5、onlyusedlossycompressionmethodsisthatoftransformcodingusingoneofthemanyimagetransformsavailable.Inapreviouspaper[4],weexaminedtheperformanceofthethreemostcommontransformmethods:theHadamard,theWalsh,andthediscretecosinetransform[9].Theideahereistotransformtheimageintoanewdomain,wheretheimageisnowr
6、epresented(approximated)byasetofdecorrelatedcoefficients.Clearly,thelargerthenumberofcoefficients,themoreaccuratetheapproximationis.However,inordertoachieve(lossy)compression,thecoefficientscontaininglessimportantinformationareremovedwhilethosewiththemostimportantinformationarekept.Atthispoint,wecan
7、inspectqualita-tively(visually)andquantitatively(signaltonoiseratio)thecompressedimagebyreconstructingitviatheinversetransformation.Itwasobservedthatbyusingtheappropriatetransformmethod(choosethebestbasis)only,wecanach
