基于统计建模的HEVC码率与失真估计算法

基于统计建模的HEVC码率与失真估计算法摘要：高效视频编码（HEVC）已成为最新的视频编码标准之一，广泛应用于数字媒体的压缩及传输领域。对于视频编码标准而言，码率和失真是两个至关重要的因素。因此，本文基于统计建模理论，提出了一种计算HEVC码率与失真估计的算法。该算法通过对视频序列中像素值的概率分布进行统计，并使用高斯混合模型对这些概率分布进行建模，然后运用算术编码技术得出码率。同时，采用熵编码技术将用于计算失真的误差图像压缩到比特流中，从而得出失真。为了准确地计算码率与失真，本文采用了一种自适应区域块大小选择算法，即通过动态计算区域块大小来进一步提高准确度。实验结果表明，本文提出的算法能够在保证视频质量的前提下减少码率，从而在视觉效果和传输效率方面取得了良好的表现。

关键词：统计建模，高效视频编码，码率，失真，高斯混合模型，算术编码，熵编码，自适应区域块大小选择。

Abstract:HighEfficiencyVideoCoding(HEVC)hasbecomeoneofthelatestvideocodingstandards,widelyusedinthefieldofcompressionandtransmissionofdigitalmedia.Forvideocodingstandards,bitrateanddistortionaretwocrucialfactors.Therefore,thispaperproposesanalgorithmforcalculatingHEVCbitrateanddistortionestimationbasedonstatisticalmodelingtheory.Thisalgorithmstatisticallymodelstheprobabilitydistributionofpixelvaluesinthevideosequence,usesGaussianmixturemodelstomodeltheseprobabilitydistributions,andthenusesarithmeticcodingtechnologytoobtainthebitrate.Atthesametime,entropycodingtechnologyisusedtocompresserrorimagesusedtocalculatedistortionintobitstreams,therebyobtainingdistortion.Inordertoaccuratelycalculatebitrateanddistortion,thispaperadoptsanadaptiveblocksizeselectionalgorithm,thatis,dynamicallycalculatestheblocksizetofurtherimproveaccuracy.Theexperimentalresultsshowthatthealgorithmproposedinthispapercanreducebitratewhileensuringvideoquality,andachievegoodperformanceintermsofvisualeffectsandtransmissionefficiency.

Keywords:statisticalmodeling,highefficiencyvideocoding,bitrate,distortion,Gaussianmixturemodel,arithmeticcoding,entropycoding,adaptiveblocksizeselectionVideocompressionisanessentialtechniqueforreducingthesizeofvideodataandimprovingtransmissionefficiency.However,traditionalvideocompressiontechniquesbasedonfixedblocksizesoftenresultininefficienciesinsomepartsofthevideo.Therefore,thereisaneedfordynamicblocksizeselectionalgorithmstooptimizethecompressionefficiency.

Inthispaper,weproposeablocksizeselectionalgorithmthatusesstatisticalmodelingtodynamicallycalculatetheblocksizesforeachframe.ThealgorithmisbasedontheGaussianmixturemodel(GMM)andusesarithmeticandentropycodingtoencodethevideodata.Theproposedalgorithmalsoadaptstothecomplexityofeachframe,resultinginbettercompressionefficiencywhilemaintainingthevideoquality.

Experimentalresultsshowthatouralgorithmcanreducethebitratewhileensuringthevideoquality.Moreover,ourproposedalgorithmachievesgoodperformanceintermsofvisualeffectsandtransmissionefficiency.

Insummary,ourproposedadaptiveblocksizeselectionalgorithmisapromisingsolutionforimprovingvideocompressionefficiencyandachievingbettervideoquality.Webelievethatourapproachcanbeappliedtovariousvideocodingstandards,suchastheupcominghighefficiencyvideocoding(HEVC)standardAnotheradvantageofourproposedalgorithmisitslowcomplexity.Itcanbeeasilyimplementedandexecutedonhardwarewithlimitedcomputationalcapacity,makingitidealforuseinlow-costdevices.Additionally,ouralgorithmdoesnotrequireanychangestothecompressionstandardordetractfromtheoverallcompressionefficiency.

Onepotentiallimitationofourapproachisitsdependenceonthepixelcharacteristicsofthevideoframes.Thus,inscenarioswherethepixelcharacteristicsvarysignificantlyoverthecourseofthevideo,suchasinsomeaction-packedscenes,ouralgorithmmaynotperformoptimally.However,webelievethatthislimitationcanbeaddressedbyfurtherrefinementofthealgorithm,suchasbyutilizingmachinelearningtechniquestoadapttochangingscenes.

Overall,weareconfidentthatourproposedadaptiveblocksizeselectionalgorithmcancontributetothedevelopmentofbettervideocodingstandardsandenhancetheviewingexperienceforconsumers.Withfurtherresearchanddevelopment,webelievethatourapproachcanbecomeanindustrystandardforvideocompressionandimprovetheefficiencyofvideostreamingandstorageInadditiontoutilizingmachinelearningtechniquesforrefinement,ourproposedadaptiveblocksizeselectionalgorithmcanalsobenefitfromintegrationwithothercompressiontechnologies.Forexample,hybridapproachesthatcombinetheuseofspatialandtemporalredundanciescanfurtherimprovetheefficiencyofvideocompression.

Furthermore,ouralgorithmcanalsobeextendedtosupportthecompressionandstreamingof360-degreevideos,whicharebecomingincreasinglypopularinvirtualrealityapplications.Theuseofadaptiveblocksizescanaidinreducingdistortionandimprovingthevisualqualityofsuchvideos.

Onepotentiallimitationofouralgorithmisthecomputationalcomplexityoftheblocksizeselectionprocess,asitrequiresanalyzingmultipleblocksizesandtheircorrespondingrate-distortiontradeoffs.However,thiscanbeaddressedthroughtheuseofparallelprocessingtechniquesandhardwareacceleration,whicharebecomingmoreaccessiblewithadvancementsintechnology.

Overall,thedevelopmentofeffectivevideocompressionalgorithmsisessentialforenablingtheefficientstorageandtransmissionofvideocontent,particularlyintheeraofhigh-resolutionandhigh-frameratevideo.Webelievethatourproposedadaptiveblocksizeselectionalgorithmisapromisingsteptowardsachieving