《信息工程专业英语》课件第6章

Unit6DigitalImageProcessing6.1Text6.2ReadingMaterials

6.1Text

FundamentalStepsinDigitalImageProcessing

Itishelpfultodividethematerialcoveredinthefollowingchaptersintothe

twobroadcategories:methodswhoseinputandoutputareimages,andmethodswhoseinputsmaybeimages,butwhoseoutputsareattributesextractedfromthoseimages.ThisorganizationissummarizedinFig6.1.Thediagramdoesnotimplythateveryprocessisappliedtoanimage.Rather,theintentionistoconveyanideaofallthemethodologiesthatcanbeappliedtoimagesfordifferentpurposesandpossiblywithdifferentobjectives.

ImageacquisitionisthefirstprocessshowninFig6.1.Notethatacquisitioncouldbeassimpleasbeinggivenanimagethatisalreadyindigitalform.Generally,theimageacquisitionstageinvolvespreprocessing,suchasscaling.

Fig6.1FundamentalstepsinDigitalImageProcessing

Imageenhancementisamongthesimplestandmostappealingareasofdigitalimageprocessing.Basically,theideabehindenhancementtechniquesistobringoutdetailthatisobscured,orsimplytohighlightcertainfeaturesofinterestinanimage.Afamiliarexampleofenhancementiswhenweincreasethecontrastofanimagebecause“itlooksbetter”.Itisimportanttokeepinmindthatenhancementisaverysubjectiveareaofimageprocessing.

Imagerestorationisanareathatalsodealswithimprovingtheappearanceofanimage.AnexampleofimagerestorationisshowninFig6.2.However,unlikeenhancement,whichissubjective,imagerestorationisobjective,inthesensethatrestorationtechniquestendtobebasedonmathematicalorprobabilisticmodelsofimagedegradation.Enhancement,ontheotherhand,isbasedonhumansubjectivepreferencesregardingwhatconstitutesa“good”enhancementresult.

Fig6.2Exampleofimagerestoration

ColorimageprocessingisanareathathasbeengaininginimportancebecauseofthesignificantincreaseintheuseofdigitalimagesovertheInternet.Waveletsarethefoundationforrepresentingimagesinvariousdegreesofresolution.

Compression,asthenameimplies,dealswithtechniquesforreducingthestoragerequiredtosaveanimage,orthebandwidthrequiredtotransmitit.Althoughstoragetechnologyhasimprovedsignificantlyoverthepastdecade,thesamecannotbesaidfortransmissioncapacity.ThisistrueparticularlyinusesoftheInternet,whicharecharacterizedbysignificantpictorialcontent.Imagecompressionisfamiliar(perhapsinadvertently)tomostusersofcomputersintheformofimagefileextensions,suchasthejpgfileextensionusedintheJPEG(JointPhotographicExpertsGroup)imagecompressionstandard.

Morphologicalprocessingdealswithtoolsforextractingimagecomponentsthatareusefulintherepresentationanddescriptionofshape.Thematerialin

thischapterbeginsatransitionfromprocessesthatoutputimagestoprocesses

thatoutputimageattributes.

Segmentationprocedurespartitionanimageintoitsconstituentpartsorobjects.AnexampleofimagesegmentationisshowninFig6.3.Ingeneral,autonomoussegmentationisoneofthemostdifficulttasksindigitalimageprocessing.Aruggedsegmentationprocedurebringstheprocessalongwaytowardsuccessfulsolutionofimagingproblemsthatrequireobjectstobeidentifiedindividually.Ontheotherhand,weakorerraticsegmentationalgorithmsalmostalwaysguaranteeeventualfailure.Ingeneral,themoreaccurate

thesegmentation,themorelikelyrecognitionistosucceed.

Fig6.3Exampleofimagesegmentation

Representationanddescriptionalmostalwaysfollowtheoutputofasegmentationstage,whichusuallyisrawpixeldata,constitutingeithertheboundaryofaregion(i.e.,thesetofpixelsseparatingoneimageregionfromanother)orallthepointsintheregionitself.Ineithercase,convertingthedatatoaformsuitableforcomputerprocessingisnecessary.Thefirstdecisionthatmustbemadeiswhetherthedatashouldberepresentedasaboundaryorasacompleteregion.

Boundaryrepresentationisappropriatewhenthefocusisonexternalshapecharacteristics,suchascornersandinflections.Regionalrepresentationisappropriatewhenthefocusisoninternalproperties,suchastextureorskeletalshape.Insomeapplications,theserepresentationscomplementeachother.Choosingarepresentationisonlypartofthesolutionfortransformingrawdataintoaformsuitableforsubsequentcomputerprocessing.Amethodmustalsobespecifiedfordescribingthedatasothatfeaturesofinterestarehighlighted.Description,alsocalledfeatureselection,dealswithextractingattributesthatresultinsomequantitativeinformationofinterestorarebasicfordifferentiatingoneclassofobjectsfromanother.

Recognitionistheprocessthatassignsalabel(e.g.,“vehicle”)toanobjectbasedonitsdescriptors.Weconcludeourcoverageofdigitalimageprocessingwiththedevelopmentofmethodsforrecognitionofindividualobjects.

Knowledgeaboutaproblemdomainiscodedintoanimageprocessingsystemintheformofaknowledgedatabase.Thisknowledgemaybeassimpleasdetailingregionsofanimagewheretheinformationofinterestisknowntobelocated,thuslimitingthesearchthathastobeconductedinseekingthatinformation.Theknowledgebasealsocanbequitecomplex,suchasaninterrelatedlistofallmajorpossibledefectsinamaterialsinspectionproblemoranimagedatabasecontaininghigh-resolutionsatelliteimagesofaregioninconnectionwithchange-detectionapplications.Inadditiontoguidingtheoperationofeachprocessingmodule,theknowledgebasealsocontrolstheinteractionbetweenmodules.ThisdistinctionismadeinFig6.1bytheuseofdouble-headedarrowsbetweentheprocessingmodulesandtheknowledgebase,asopposedtosingle-headedarrowslinkingtheprocessingmodules.

Althoughwedonotdiscussimagedisplayexplicitlyatthispoint,itisimportanttokeepinmindthatviewingtheresultsofimageprocessingcantakeplaceattheoutputofanystageinFig6.1.WealsonotethatnotallimageprocessingapplicationsrequirethecomplexityofinteractionsimpliedbyFig6.1.Infact,notevenallthosemodulesareneededinsomecases.Forexample,imageenhancementforhumanvisualinterpretationseldomrequiresuseofanyoftheotherstagesinFig6.1.Ingeneral,however,asthecomplexityofanimageprocessingtaskincreases,sodoesthenumberofprocessesrequiredtosolvetheproblem.

Technicalwordsandphrases

broad adj.显著的;宽的，辽阔的

attribute n.属性；特质

scaling

n.缩放比例

obscured

v.使含混；变得模糊

restoration n.恢复；复原；归还

degradation n.退化；降格，降级

resolution n.分辨率

pictorial adj.图像的；绘画的

inadvertently adv.非故意地；不注意地

morphological adj.形态学的

rugged adj.崎岖的；坚固的；高低不平的

erratic

adj.不稳定的

raw adj.生的；未加工的

inflection n.曲线，弯曲，变形

texture n.纹理；质地

skeletal

adj.骨骼的，像骨骼的

complement vt.补足，补助

quantitative adj.定量的；量的，数量的

differentiate vi.区分，区别

inspection

n.视察，检查

defect

n.缺点，缺陷；不足之处

interaction

n.相互作用；互动

distinction

n.特性；区别；差别；荣誉、勋章

explicit

adj.明确的；清楚的；直率的；详述的

interpretation n.解释；翻译；演出

beextractedfrom

从……中提取

contrastofanimage

图像对比度

mathematicalmodels 数学模型

probabilisticmodels 概率模型

autonomoussegmentation 自动分割

partitioninto

划分

high-resolutionsatelliteimages 高分辨度卫星图像

double-headedarrows

双箭头

asopposedto

与……相反

JPEG

(JointPhotographicExpertsGroup)联合图片专家组

6.1.1Exercises

1.PutthePhrasesintoEnglish

(1)数码图像处理； (2)彩色图像；

(3)存储技术； (4)传输能力；

(5)文件扩展名； (6)分割算法；

(7)原始数据； (8)后续处理；

(9)特征选择； (10)个别目标。

2.PutthePhrasesintoChinese

(1)digitalimageprocessing;

(2)imageacquisition;

(3)imageenhancement;

(4)highlightcertainfeatures;

(5)knowledgedatabase;

(6)descriptionofshape;

(7)assignsalabelto;

(8)aproblemdomain.

3.Translation

(1)Basically,theideabehindenhancementtechniquesistobringoutdetailthatisobscured,orsimplytohighlightcertainfeaturesofinterestinanimage.

(2)Inthesensethatrestorationtechniquestendtobebasedonmathematicalorprobabilisticmodelsofimagedegradation.

(3)Aruggedsegmentationprocedurebringstheprocessalongwaytowardsuccessfulsolutionofimagingproblemsthatrequireobjectstobeidentifiedindividually.

(4)Thefirstdecisionthatmustbemadeiswhetherthedatashouldberepresentedasaboundaryorasacompleteregion.

(5)Theknowledgebasealsocanbequitecomplex,suchasaninterrelatedlistofallmajorpossibledefectsinamaterialsinspectionproblemoranimagedatabasecontaininghigh-resolutionsatelliteimagesofaregioninconnectionwithchange-detectionapplications.

(6)Ingeneral,however,asthecomplexityofanimageprocessingtaskincreases,sodoesthenumberofprocessesrequiredtosolvetheproblem.

6.1.2参考译文

将图像处理划分为两个主要类别：一类是其输入和输出都是图像，另一类，输入可能是图像，但是输出是从图像中提取的特征属性。这一结构在图6.1中做了概括。该图并不意味着每一个处理步骤都应用于一张图片，其意图是要说明针对不同目标的图像处理，这些方法都可应用其中。

图像获取是图6.1的第一步处理。注意图像获取与给出一幅数字形式的图像一样简单。通常，图像获取包括预处理，如图像的缩放。

图像增强是数码图像处理最简洁和最有吸引力的环节。基本上，增强技术的基本思路就是显现那些被模糊了的细节，或通过简化来突出图像中某个感兴趣的特征。一个关于图像增强熟悉的例子：当我们增加图像的对比度，它就会更好看。重要的是要记住，图像增强是图像处理中非常主观的领域。

图像复原是改进图像外观的环节。图像复原的例子如图6.2所示。然而，不像图像增强是主观的，图像复原是客观的。从某种意义上说，复原技术是基于图像退化的数学模型或概率模型。而图像增强从另一方面来看，是以人的主观偏爱为基础来得到“好”的增强效果。

彩色图像处理已经变得越来越重要，是因为互联网上数码图像的应用有着显著的增长。微波就是在各种分辨率下描述图像的基础。

压缩，顾名思义，即减少图像的存储量，或者降低传输图像的带宽。虽然存储技术在过去的10年内有了显著提升，但传输容量并非如此。以大量图片内容为特征的互联网更是如此。图像压缩技术所对应的图像文件扩展名对大多数计算机用户来说是很熟悉的(有的也许并不注意)，如在JPEG(联合图片专家组)图像压缩标准中使用JFG文件扩展名。

形态学处理会涉及到提取图像元素，这些图像元素在表现和描述形状方面非常有用。这一章的材料将从输出图像处理到输出图像特征处理的转换开始。

分割过程是将一幅图像划分为组成图像的各部分或目标物，图像分割的例子如图6.3所示。通常，自动分割是数字图像处理中最为困难的任务之一。复杂的分割过程导致成功解决分割问题变得很难，这要求物体被分别识别出来，这种分割成像的问题需要大量的处理工作。另一方面，不健全且缺乏稳定的分割算法几乎总是会导致最终的失败。通常，分割越准确，识别越成功。

表示与描述几乎总是跟随在分割步骤的输出后边，通常这样的输出是未加工的像素数据，其构成不是区域的边缘(也就是分隔一个图像区域和另一个区域的像素集)就是其区域本身所有点的集合。无论哪种情况，把数据转换成适合计算机处理的形式都是必要的。首先，必须确定数据是被表现为边界还是整个区城。当注意的焦点是外部形状特性时，边界表示是合适的，如拐角和曲线。当注意的焦点是内部特性时，则区域表示是合适的，如纹理或骨骼形状。某些应用中，这些表示相辅相成的。选择某种表现方式仅是把原始数据转换为适合计算机后续处理形式过程中的一部分。为了通过描述数据使感兴趣的特征表现得更明显，还必须确定一种方法。描述也叫特征选择，涉及提取特征，该特征是某些感兴趣的定量信息或是区分一组目标与其他目标的基础。

识别是在目标描述基础上给目标赋予标签(例如“车辆”)的过程。我们用个别目标识别的先进方法来推导数字图像处理的覆盖范围。

问题域是将问题以知识库的形式编码并装入某个图像处理系统。这一过程就如详述感兴趣的信息位于某个图像区域那样简单，这样限制性的搜索就被引导到要寻找的信息处。知识库也可能相当复杂，如材料检测问题或者图像数据库中所有主要缺陷的相关列表，数据库包含变化检测应用相关区域的高分辨卫星图像。除了引导每个处理模块的操作，知识库还要控制模块之间的交互。这种特性由图6.1中的处理模块和知识库之间的双向箭头表示，这与单向箭头连接处理模块截然相反。

此时此刻我们虽然没有明确地讨论图像显示，但要记住观察图6.1中任何阶段输出处的图像处理结果是很重要的。我们还注意到，不是所有的图像处理都需要图6.1所给出的复杂交互。事实上，在某些情况下并不是所有模块都需要。例如，为了人的视觉解释，图像增强很少需要使用图6.1中的其他任何步骤。然而，通常随着图像处理任务复杂度的增加，则需要做更多处理才能使问题得到解决。

6.2ReadingMaterials

6.2.1PyramidMethodsinImageProcessing

Digitalimageprocessingisbeingusedinmanydomainstoday.Inimageenhancement,forexample,avarietyofmethodsnowexistforremovingimagedegradationsandemphasizingimportantimageinformation,andincomputergraphics,digitalimagescanbegenerated,modified,andcombinedforawidevarietyofvisualeffects.Indatacompression,imagesmaybeefficientlystoredandtransmittediftranslatedintoacompactdigitalcode.Inmachinevision,automaticinspectionsystemsandrobotscanmakesimpledecisionsbasedonthedigitizedinputfromatelevisioncamera.

Thetaskofdetectingatargetpatternthatmayappearatanyscalecanbeapproachedinseveralways.Twoofthese,whichinvolveonlysimpleconvolutions,areillustratedinFig6.4.Severalcopiesofthepatterncanbeconstructedatincreasingscales,andtheneachisconvolvedwiththeimage.Alternatively,apatternoffixedsizecanbeconvolvedwithseveralcopiesoftheimagerepresentedatcorrespondinglyreducedresolutions.Thetwoapproachesyieldequivalentresults,providedcriticalinformationinthetargetpatternisadequatelyrepresented.However,thesecondapproachismuchmoreefficient:

agivenconvolutionwiththetargetpatternexpandedinscalebyafactorswillrequires4morearithmeticoperationsthanthecorrespondingconvolutionwiththeimagereducedinscalebyafactorofs.Thiscanbesubstantialforscalefactorsintherange2to32,acommonlyusedrangeinimageanalysis.

Fig6.4Twomethodsofsearchingforatargetpatternovermanyscales

Theimagepyramidisadatastructuredesignedtosupportefficientscaledconvolutionthroughreducedimagerepresentation.Itconsistsofasequenceofcopiesofanoriginalimageinwhichbothsampledensityandresolutionaredecreasedinregularsteps.AnexampleisshowninFig6.5a.Thesereducedresolutionlevelsofthepyramidarethemselvesobtainedthroughahighlyefficientiterativealgorithm.Thebottom,orzerolevelofthepyramid,G0,isequaltotheoriginalimage.

Thisislowpassfilteredandsubsampledbyafactoroftwotoobtainthenextpyramidlevel,Gl.GlisthenfilteredinthesamewayandsubsampledtoobtainG2.Furtherrepetitionsofthefilter/subsamplestepsgeneratetheremainingpyramidlevels.Tobeprecise,thelevelsofthepyramidareobtainediterativelyasfollows.For0<l<N:

(6-2-1)

However,itisconvenienttorefertothisprocessasastandardREDUCEoperation,andsimplywrite:Gl=REDUCE[Gl-1].

Wecalltheweightingfunctionω(m,n)the“generatingkernel”.Forreasonsofcomputationalefficiencythisshouldbesmallandseparable.Afive-tapfilterwasusedtogeneratethepyramidinFig6.5.

Fig6.5TheGaussianPyramidexpandedtothesizeoftheoriginalimage

PyramidconstructionisequivalenttoconvolvingtheoriginalimagewithasetofGaussian-likeweightingfunctions.These“equivalentweightingfunctions”forthreesuccessivepyramidlevelsareshowninFig6.6.Notethatthefunctionsdoubleinwidthwitheachlevel.Theconvolutionactsasalowpassfilterwiththebandlimitreducedcorrespondinglybyoneoctavewitheachlevel.BecauseofthisresemblancetotheGaussiandensityfunctionwerefertothepyramidoflowpassimagesasthe“Gaussianpyramid.”

Fig6.6Equivalentweightingfunctions

Bandpass,ratherthanlowpass,imagesarerequiredformanypurposes.ThesemaybeobtainedbysubtractingeachGaussian(lowpass)pyramidlevelfromthenextlowerlevelinthepyramid.Becausetheselevelsdifferintheirsampledensityitisnecessarytointerpolatenewsamplevaluesbetweenthoseinagivenlevelbeforethatlevelissubtractedfromthenext-lowerlevel.InterpolationcanbeachievedbyreversingtheREDUCEprocess.WecallthisanEXPANDoperation.LetGl,kbetheimageobtainedbyexpandingGlktimes.ThenGl,k=EXPAND[Gl,k-1]or,tobeprecise,Gl,0=Gl,andfork >

0,

(6-2-2)

Hereonlytermsforwhich(2i+m)/2and(2j+n)/2areintegerscontributetothesum.Theexpandoperationdoublesthesizeoftheimagewitheachiteration,sothatGl,1,isthesizeofGl,1,andGl,1isthesamesizeasthatoftheoriginalimage.ExamplesofexpandedGaussianpyramidlevelsareshowninFig6.7.

Fig6.7LevelsoftheGaussianpyramidexpandedtothesizeoftheoriginalimage

Thelevelsofthebandpasspyramid,L0,L1,...,LN,maynowbespecifiedintermsofthelowpasspyramidlevelsasfollows:

(6-2-3)

ThefirstfourlevelsareshowninFig6.8.

Fig6.8Thelevelsofthebandpasspyramid,L0,L1,L2,L3

JustasthevalueofeachnodeintheGaussianpyramidcouldhavebeenobtaineddirectlybyconvolvingaGaussianlikeequivalentweightingfunctionwiththeoriginalimage,eachvalueofthisbandpasspyramidcouldbeobtainedbyconvolvingadifferenceoftwoGaussianswiththeoriginalimage.ThesefunctionscloselyresembletheLaplacianoperatorscommonlyusedinimageprocessing.Forthisreasonwerefertothebandpasspyramidasa“Laplacianpyramid.”

AnimportantpropertyoftheLaplacianpyramidisthatitisacompleteimagerepresentation:thestepsusedtoconstructthepyramidmaybereversedtorecovertheoriginalimageexactly.Thetoppyramidlevel,LN,isfirstexpandedandaddedtoLN-1toformGN-1thenthisarrayisexpandedandaddedtoLN-2torecoverGN-2,andsoon.Alternatively,wemaywrite:

(6-2-4)

Thepyramidhasbeenintroducedhereasadatastructureforsupportingscaledimageanalysis.Thesamestructureiswellsuitedforavarietyofotherimageprocessingtasks.Itcanbeshownthatthepyramid-buildingproceduresdescribedherehavesignificantadvantagesoverotherapproachestoscaledanalysisintermsofbothcomputationcostandcomplexity.ThepyramidslevelsareobtainedwithfewerstepsthroughrepeatedREDUCEandEXPANDoperationsthanispossiblewiththestandardFFT.Furthermore,directconvolutionwithlargeequivalentweightingfunctionsrequires20to30bitarithmetictomaintainthesameaccuracyasthecascadeofconvolutionswiththesmallgeneratingkernelusingjust8-bitarithmetic.

6.2.2ImageCompressionandCoding

Wepresentanalgorithmforimagecompressionbasedonanimageinpaintingmethod.Firsttheimageregionsthatcanbeaccuratelyrecoveredarelocated.Then,toreducethedata,informationofsuchregionsisremoved.Theremainingdatabesidesessentialdetailsforrecoveringtheremovedregionsareencodedtoproduceoutputdata.Atthedecoder,aninpaintingmethodisappliedtoretrieveremovedregionsusinginformationextractedattheencoder.

Theimageinpaintingtechniqueutilizespartialdifferentialequations(PDEs)forrecoveringinformation.Itisdesignedtoachievehighperformanceintermsofimagecompressioncriteria.Thisalgorithmwasexaminedforvariousimages.Ahighcompressionratioof1:40wasachievedatanacceptablequality.ExperimentalresultsshowedattainablevisiblequalityimprovementatahighcompressionratiocomparedwithJPEG.

Compressionisacceptablefornaturalimagesasalargeamountofredundancyisincludedinsuchimages.Ahighcompressionratiocanbeachievedbyeliminatingtheseredundancies,butatthecostofsomeinformationloss.Uptonow,greatachievementshavebeenmadeinimagecompression.State-of-the-artmethodssuchasJPEG(PennebakerandMitchell,1992)andJPEG2000(TaubmanandMarcellin,2002)efficientlyexploitstatisticalredundanciesamongpixelsandachievehighcompressionratios.

Acom­monframeworkinalmostalllossyimagecompressionmethodsisimagetransformationfollowedbyquantizationandcoding.Incontrast,JPEGandJPEG2000usethediscretecosinetransform(DCT)andwavelettransform,respectively.Informationlosscausesdecreaseinthequalityofreconstructedimages,especiallyathighcompressionratios.Toimproveperceptualvisualquality,HontschandKaram(2000)andMaloetal.(2006)incorporatedthehumanvisionsystem(HVS)propertiesincompressionschemes.

Thedominanttypeofredundancywithinimagescomesfromtheirrepresentationprocedure.Eachdigitalimageiscomposedofdiscretepoints,calledpixels.Thevaluerelevanttoeachpixelistheresultofsamplingfromlightorcolorintensityintheoriginalimagedomain.Naturalimagesconsistofseparateareasindicatingtheobjectsurfacesorsceneries.Be­causethelightintensityandcolorinsuchareasareapproximatelyconstant,therelevantvaluesforpixelsarehighlycorrelated.Everypixelinsuchareasislikelytobeofthesameorveryclosevaluecomparedwiththeadjacentpixels.Inthiscase,imagessufferfromahighlevelofspatialcorrelation.

Themostsignificantinformationwithinanimageislocatedintheboundaryregionsoredges.Infact,theboundaryofaregionnotonlyspecifiesitsoverallshape,butalsoshowshowpixelvalueschangefromneighboringregionstotheinnerregionsofinterest.Asaresult,itispossibletoretrievetheinnerareasusingpixelslocatedontheboundaries.Therefore,boundariesoredgesarealltherequiredinformationfordisplayinganimage.Fig6.9

clarifiesthisconcept.Fig6.9(a)showsasyntheticimagecomposedofthreedifferentregions.

Fig6.9(b)showstheboundariesofregionsinFig6.9(a).Certainly,bydiffusingtheinformationofboundariesshowninFig6.9(b)intothecorrespondingregion,theimageinFig6.9(a)willbere­covered.InFig6.9(b),theboundaryregionsarepreservedwith9pixelswidth.Also,theinformationrelatedtootherregionsisremoved.Itisevidentthatonly2pixelsratherthan9areadequateforrepresentingboundaryregions.

(a)Animagewiththreeregions;

(b)Extractededgesofthesameimagewith9pixelswidthFig6.9

Thevariationinvaluesofpixelsorthogonaltotheedgesissignificant.Hence,areasintheneighbor­hoodofedgesmaybeconsideredasessentialimageinformation.Ontheotherhand,whilemovingalongtheedgedirection,nosignificantchangesinpixelvalueswillbeobserved.Movingfurthertotheinnerpointsoftheboundarieswillresultinaconsiderablecorrelationforpixelvalues.Edgesalsorepresentsomeothernecessaryinformationincludingshapes.Redundanciesrelatedtothecorrelationalongtheedgedirectionmayalsobeexploitedviaextractingshapeinformationfrompixelvaluesinboundaryregions.

Pixelvaluesattheendpointsofanedgewillbeusedforrecoveringtheentireedgeandboundaryregion.Ofcourse,itholdstrueifthelocationoftheedgepointsisknown.Inthispaper,theedgeendpointiscalledthe'sourcepoint'.Inordertorecoverboundaryregionsandpixelslocatedperpendiculartotheedgedirection,samplesofsourcepointsshouldbeprovided.Thesesamplesshouldcomefromdifferentareasateachsideoftheedge.Fig6.10indicatesedgelocationsandsourcepointsfortheimageshowninFig6.10(a),zoomedversionisshowninFig6.10(b).

(a)AsourcepointsandboundariesforFig6.9a(theblacklinesstandforedges)Fig6.10(b)ZoomedversionofFig6.9aFig6.10

ZoomedversionofaJPEG2000istoadapttothecontinuousdevelopmentofimagecompressionapplications,theemergenceofnewstillimagecompressionstandard.JPEG2000imagecodingsystemdescribestherealizationoftheprocess,ofwhichthebasicalgorithmusedisdescribedandkeytechnologies,introducedthenewstandardfeaturesandapplications,anditsperformanceisanalyzed.Withtherapidgrowthofmultimediaapplicationsandnetworkscontinuestodevelop,thetraditionalJPEGcompressiontechnologyhasbeenunabletomeetthepeople'sdigitalmultimediaimagedatarequirements,amorepowerfulandefficientsuperiorstillimagecompressionstandardhasbeenreferredtothedevelopmentagenda,thisisaJPEG2000.

JPEG(JointPhotographicExpertsGroup)istheInternationalOrganizationforStandardization(ISO)developedundertheleadershipofthecommitteestillimagecompressionstandard,thefirstsetofinternationalstillimagecompressionstandardISO10918-1(JPEG)isthatestablishedbytheCommittee.AstheexcellentqualityJPEG,sothatwithinafewyearshewasagreatsuccess,iswidelyusedinthefieldofInternetanddigitalcamerasonthesite80%haveadoptedtheJPEGimagecompressionstandard.

However,thecurrentJPEGstillimagecompressionstandard,withmid-rangeandhigh-bit-rateontheratedistortioncharacteristicsofagood,butinthecontextoflowbitrate,therewillbeobviousblockingeffects,itsqualityhasbecomeunacceptable.JPEGcannotbeprovidedinasinglebitstreamlossyandlosslesscompression,andcannotsupportmorethan64×64Koftheimagecompression.Atthesametime,despitethecurrentJPEGstandardhasarequirementtorestarttheinterval,butwhenbiterrorsencounteredwhentheimagequalitywillbeseriouslydamaged.

Tosolvetheseproblems,sinceMarch1997onwards,JPEGimagecompressionstandardscommitteestartedtodevelopanewgenerationofimagecompressionstandardtoaddresstheseproblems.InMarch2000theTokyoconferencetoidentifyanewgenerationofcolorstillimagecodingstandardJPEG2000imagecompressioncodingalgorithm.

JPEG2000advantagesofitsuniquemakeupfordeficienciesintheexistingJPEGstandard.Discretewavelettransformalgorithm,theimagecanbeconvertedintoaseriesofpixelscanbemoreeffectivememorymodulesub-band,therefore,JPEG2000imagecompressionformatthantheJPEGcanbebasedonthecurrentre-increasedby10%to30%,andthecompressedimageappearstomoredelicatesmooth.Inotherwords,onlineviewingofimagesusingJPEG2000compression,notonlytodownloadspeedsfasterthanusingJPEGformat,nearly30%,butthequalitywillbebetter.

ForthecurrentJPEGstandard,inthesamecompressedstreamcannotprovidelossyandlosslesscompression,whileinJPEG2000systems,byselectingtheparameters,canbelossyandlosslessimagecompression,imagequalitytomeetthedemandingmedicalimages,imagelibrary,etc.processingneeds.NowthenetworkisbasedonJPEGimagedownload“block”transfers,soitcanonlybedisplayedlinebyline,whiletheuseofJPEG2000imageformatsupportforprogressivetransmission(ProgressiveTransmission),whichallowstheimageresolutionorpixelsinaccordancewiththerequiredaccuracyofreconstruction,theuserneededforimagetransmissio