ICIP2016 image compression grand challenge

23rd InternationalConferenceonImageProcessing,September25–28,2016,Phoenix,Arizona

OVERVIEWANDBENCHMARKINGSUMMARYFORTHEICIP2016COMPRESSIONCHALLENGE

Evangelos Alexiou ,IreneViola,LukasKrasula,ThomasRichter,TimBruylants,AntonioPinheiro,KarelFliegel,MartinRerabek,Athanassios Skodras,PeterSchelkens andTouradjEbrahimi

AcontributionfromQualinet toJPEGcallforinformation

23rdInternationalConferenceonImageProcessingSeptember25– 28,2016,Phoenix,Arizona

Background

• Callforinformationonstillimagecoding– IssuedbyJPEG committeeonFebruary2015– Broadscopenotonlylimitedtocompressionefficiency• Newimagingmodalities (morethan8-bit,HDR,…)• Features (scalability,randomaccess,…)• Characteristics (complexity,latency,…)

– AfirstresponseproducedduringPCS2015– Bothlossy andlossless

• ICIP2016FeatureEvent– EvaluationofcurrentandfutureImagecompressiontechnologies

• Thiscontributiononlyfocusesoncompressionefficiencyofconventionalimagesinlossy and losslesswithouttakingintoaccountothercriteria(features,complexity,delay,etc.)• Objective andsubjective evaluations inlossy casecarriedoutbyQualinet– VUB/iMinds (Belgium)– UBI(Portugal)– CTU(CzechRepublic)– UniversityStuttgart(Germany)– UniversityPatras(Greece)– EPFL(Switzerland)

• Lossless evaluations carriedoutbyUniversityofStuttgart(Germany)

http://www.jpeg.org

http://www.qualinet.eu

Testmaterialinlossy evaluations• Contents:7 (1training+6test):– Resolutions - 800x1152or800x1280dependingoncontent– Subjectiveevaluationsoncroppedversionstofitdisplay– Objectivemetricsperformedonthecroppedversions

• Stimuli:– Originalimages– Compressed/decompressedimageswith10codecs• JPEG(default)• JPEG(PSNR)• JPEG (visual)• JPEG2000(PSNR)• JPEG2000(visual)• JPEGXR(444)• JPEGXR(420)• HEVC (SCCext.)• Daala• WebP

• 8bitratesforobjectivemetrics:– 0.25,0.5,0.75,1,1.25,1.5,1.75and2bpp

• 4bitratesforsubjectiveevaluations:– 0.25,0.5,0.75and1bpp or0.75,1,1.25and1.5bpp dependingoncontent

training

bike cafe honolulu

p08 p26 woman

Testmaterialinlosslessevaluations 4

RGB,444,24bpp

Croppedimagesusedinlossy evaluations

p08honolulu

bike cafe

Croppedimagesusedinlossy evaluations

p26 woman

Codecsusedinlosslessevaluations 7

1. JPEGXRLossless2. JPEG2000lossless3. FLIF (submittedtoICIP2016GrandChallenge)4. JPEGLSpart-1withoutcolortransformation5. JPEGLSpart-2withlosslesscolortransformation6. PNG7. WebP lossless8. JPEGXTpart8losslesswithresidualcoding9. JPEGXTpart8losslesswitharithmeticcoding10. JPEGXTpart8withlosslessDCT11. JPEG-1lossless12. JPEG-1losslesswitharithmeticcoding13. JPEGXTpart8,residualcodingwithoptimizedHuffmancoding,progressivemode14. JPEGXTpart8,losslessDCTwithoptimizedHuffmancoding,progressivemode.15. HierarchicalJPEGwiththeinitialpassaDCTcodingandthesecondlevelofthepyramidlosslesscodingand

optimizedHuffmancoding16. Sameas15,butwitharithmeticcodinginsteadofHuffmancoding17. Sameas15,exceptthatthefirstlevelofthepyramidisadownscaled¼andprocessedbytheDCT.compressed

withDCT,thenupscaled,andtheresidualiscompressedbythepredictivemodeofJPEG.18. Sameas17,butwitharithmeticcoding.

Codecsusedinlossy evaluations

• JPEG(default):IJGimplementationofJPEGwithdefaultoptions(i.e.AnnexKquantizationsettings,420colorsubsampling,baselineconfiguration) – “cjpeg” withoutfurtheroptions.

• JPEG(PSNR):JPEGXTreferencesoftwareusingJPEGXTpart1baselinecompressorcompatiblewith10918-1.PNSRoptimizedversion:-oz-h-qt 1–v– Enabling:444subsampling,flatquantization matrix,optimized Huffman coding,progressive scan order.

• JPEG(visual): JPEGXTreferencesoftwareusingJPEGXTpart1baselinecompressorcompatiblewith10918-1.Visuallyoptimizedversion: -oz-h-qt 3-v-s1x1,2x2,2x2– Enabling: 420subsampling,ImageMagick quantization matrix,optimized Huffman coding,progressivecoding.

• JPEG2000(PSNR):JPEG2000compressorusingAccusoft softwarewiththefollowingcommandlineoptions: -lo -as-cn 1– Enabling: strict rateallocator,5decomposition levels,one layer,noprecincts,notiles. lossy 5/3and not9/7wavelet.

• JPEG2000(visual):JPEG2000compressorusingAccusoft software.Visuallyoptimizedversion:-lo -as-cn 1-w 1000– Enabling: features identical to JPEG2000(PSNR),but with visualweighting.

• JPEGXR(444):JPEGXRreferencesoftwarewiththefollowingoptions: -fYUV444-l1–d– Enabling: 444chromasubsampling,oneleveloverlap,derivedchromaquantization.

• JPEGXR(420):JPEGXRreferencesoftwarewiththefollowingoptions:-fYUV420-l2–d– Enabling: 420chromasubsampling,twoleveloverlap,derivedchromaquantization.

• HEVC(SCCext.):HEVCwithScreenContentCodingextensionconfiguredtothemainintra-profilefor420chroma input,withthefollowingcommandlineoptions: -ccfg/encoder_intra_main_scc.cfg --InputChromaFormat=420--ProgressiveSource --FrameOnly -cf 420--FrameRate=30--FramesToBeEncoded=1--QuadtreeTULog2MaxSize=5--GOPSize=1--IntraPeriod=1--ConformanceWindowMode=1--AdaptiveQP=1--RateControl=0--TransquantBypassEnable=1--CrossComponentPrediction=0--ColourTransform=0

• Daala:mozilla Daala withdefaultconfiguration.Nofurtheroptions.

•WebP:GoogleWebP (partofWebM)withthefollowingcommandlineoptions:-m6-partition_limit 50-af

Subjectiveevaluationmethodology

• SubjectiveevaluationmethodologybasedonITU-TP.910• ACR-HR:AbsoluteCategoryRatingwithHiddenReference• Randomization ofpresentationorder• 5-level discretescale:bad,poor,fair,good,excellent• 10codecstestedfortheirsubjectivequality–10(codecs)x6(images)x4(bitrates)+6(originals)=246stimuli

• 21naïvesubjects participatedinVUB,UBIandEPFL labs• Eachsubject completed 3sessions of80stimuli(circa15minpersession,30minbreak)• Shorttraining forbad,fairandexcellentqualityillustrations• Display:AppleMacBookProRetina15inchorequivalent• Typicalofficeenvironment

Stimulus1

Stimulus80

Vote80

Training

Objectiveevaluationmetrics

• PSNR–Widelyusedqualitymetricinimageprocessingcommunity.–PerformedforbothYchannelandRGB.

• SSIM:StructuralSimilarityIndex–Meanofsimilaritybetweenanimageundertestanditsreferencebasedonstructuralinformation.

•MSSIM:MultiscaleStructuralSimilarityIndex–MultiscaleversionofSSIM.

• FSIM:FeatureSimilarityIndex–BasedonSSIM.–Addsacomparisonoflow-levelfeaturesetsbetweenthereferenceandthedistortedimages.– analyzesthehighphasecongruencyextractinghighlyinformativefeaturesandthegradientmagnitude,toencodethecontrastinformation.– ThisanalysisiscomplementaryandreflectsdifferentaspectsoftheHVSinassessingthelocalqualityofanimage.–PerformedforbothYandCchannels.

Objectiveevaluationmetrics

• HDR-VDP2.2: HighDynamicRangeVisibleDifferencePredictor–CalibratedmetricdevelopedforHDRimages–Considersalight-adaptivecontrastsensitivityfunction,astherangesoflightadaptationcanvarysubstantially.– Includesaspecificmodelofthepointspreadfunction(PSF)oftheeyeoptics,ashumanopticallensflarecanbeverystronginhighcontrastHDRcontent.– Thefront-endamplitudenon-linearityisbasedonintegrationoftheWeber-Fechnerlaw.– Takesintoaccounttheangularresolution.–Usesamulti-scaledecomposition.–Aneuralnoiseblockisdefinedtocalculateper-pixelprobabilitiesmapsofvisibilityandthepredictedqualitymetric.

• CIEDE2000:Colordifferencemetric– Includesweightingfactorsforlightness,chroma,andhue(liketheCIE1976L*a*b*perceptualspace).–Alsoincludesfactorstohandletherelationshipbetweenchroma andhue.

• VIF:VisualInformationFidelity–Analysesthenaturalscenestatistics.–UsesanimagedegradationmodelandtheHVSmodel.–BasedonthequantificationoftheShannoninformationpresentinboththereferenceandthedistortedimages.

Thankyouforyourattention!Resultsofevaluationswillbepresentedattheendofthissession!!!

SUMMARYANDNEXTSTEPSFORTHEIMAGECOMPRESSIONCHALLENGE

Evangelos Alexiou,IreneViola,LukasKrasula,ThomasRichter,TimBruylants,AntonioPinheiro,Karel Fliegel,MartinRerabek,Athanassios Skodras,PeterSchelkens andTouradjEbrahimi

AcontributionfromQualinet toJPEGcallforinformation

23rdInternationalConferenceonImageProcessingSeptember25– 28,2016,Phoenix,Arizona

Overview

• ICIP2016FeatureEvent– EvaluationofcurrentandfutureImagecompressiontechnologies

• 7Contents usedinlossy evaluations(1training+6tests)– Resolutions800x1152or800x1280

• 10codecs testedinlossy evaluations(anchorsandproponents)• 8 bitrates forobjectivemetrics– 0.25,0.5.0.75,1,1.25,1.5,1.75,2bpp

• 4bitrates forsubjectiveevaluations– 0.25,0.5,0.75,1bpp or0.75,1,1.25and1.5bpp

• SubjectiveassessmentprotocolbasedonITU-TP.910forlossy evaluations– ACR-HR:AbsoluteCategoryRatingwithHiddenReference– 21naïvesubjects inVUB,UBIandEPFL– 3sessionsof80stimuli– 5-level discretescale–Outliersdetectionbasedon boxplotalgorithm– Display: AppleMacBookProRetina15inch– Typicalofficeenvironment

• Objectiveevaluation inlossy caseusing9metrics– Cross-checked between CTU,UniversityStuttgartandUniversityPatras

http://www.jpeg.org

http://www.qualinet.eu

Testimagesinlossy evaluations 17

training

bike cafe honolulu

p08 p26 woman

Objectiveevaluation:PSNRY results18

Objectiveevaluation:PSNRRGB results19

Objectiveevaluation:SSIMresults 20

Objectiveevaluation:MSSIMresults 21

Objectiveevaluation:FSIMY results22

Objectiveevaluation:FSIMC results23

Objectiveevaluation:HDR-VDP-2.2results 24

Objectiveevaluation:CIEDE2000results 25

Objectiveevaluation:VIFresults 26

Outlierdetection

• StatisticalanalysisusingBoxplot method.

• Foreachtestcondition,anoutlier isdefinedasadatapointthatislocatedoutsidetheinterquartilerange i.e.abovetheupperquartileorbelowthelowerquartileofthedistributionofthescoresmultipliedby1.5.

• Ifthesamesubjectisidentifiedasoutlierinmorethan20% ofthetestconditions,thecorrespondingscoresarediscarded.

• Nooutlierswerefound.

Subjectiveevaluationresults 28

bike.ppm

0 0.5 1 1.5bitrate [bpp]

JPEG (default)HEVC (SCC ext.)JP2K (PSNR)JP2K (visual)DaalaJPEG XR (444)WebPJPEG (PSNR)JPEG (visual)JPEG XR (420)

cafe.ppm

0.5 1 1.5 2bitrate [bpp]

bike cafe

honolulu.ppm

p08.ppm

honolulu p08

p26.ppm

woman.ppm

p26 woman

Statisticalsignificancetest

•One-tailedWelch’st-testwithnullhypothesis at5%significancelevel.• andareMeanOpinionScoresforaspecificcontentcompressedataspecificbitratewithcodecsand•Whenthenullhypothesisisrejected,thealternativehypothesisindicatesthat,accordingtoMOS,thefirstcodecissignificantlybetteratthe5%level.• Thesetestsareperformedconsideringallcombinationsofcodecs:10x10matrix.• Foreachpairofcodecs,wesumuptheresultsforeverycontentatagivenqualitylevel:scale0-6.•Onematrixforeachqualitylevel:4figures.

H0 :m1 ≤m2

m1 m2c2c1

Codecassessmentbasedonsubjectivetests 32

• Bitrate B1 correspondstothelowestquality• Bitrate B4 correspondstothehighestquality

Significant difference matrix for bitrate B1

JPEG (default)

HEVC (SCC ext.)

JP2K (PSNR)

JP2K (visual)

JPEG XR (444)

JPEG (PSNR)

JPEG (visual)

JPEG XR (420)

6Significant difference matrix for bitrate B2

JPEG (default)

HEVC (SCC ext.)

JP2K (PSNR)

JP2K (visual)

JPEG XR (444)

JPEG (PSNR)

JPEG (visual)

JPEG XR (420)

Codecassessmentbasedonsubjectivetests 33

• Bitrate B1 correspondstothelowestquality• Bitrate B4 correspondstothehighestquality

Significant difference matrix for bitrate B3

JPEG (default)

HEVC (SCC ext.)

JP2K (PSNR)

JP2K (visual)

JPEG XR (444)

JPEG (PSNR)

JPEG (visual)

JPEG XR (420)

6Significant difference matrix for bitrate B4

JPEG (default)

HEVC (SCC ext.)

JP2K (PSNR)

JP2K (visual)

JPEG XR (444)

JPEG (PSNR)

JPEG (visual)

JPEG XR (420)

Testmaterialinlosslessevaluations 34

RGB,444,24bpp

Losslessevaluationresults 35

Lossless compression

JPEG-XR

JPEG-2000 FL

JPEG-LS

JPEG-LS2PNGWebP

JPEG-LL

JPEG-LLA

JPG-SRM

JPG-PRED

JPG-ACPRD

JPG-ACP

JPG-ACPLJPG-H

JPG-ACH

JPG-H1

JPG-ACH1

Conclusions 36

• HEVC andDaala oftenoutperformothercodecsinbothobjectivemetricsandsubjectiveevaluationsinlossy case–Daala performs bestinimagescontainingfaces

• JPEG,JPEG2000and JPEGXRperformwellin higherbitratesbasedonPSNRRGB metricinsometestedimagesinlossy case

• JPEG2000(PSNR)exhibitsgoodcolorrenditionbasedonCIEDE2000metricinseveraltestedimagesinlossy case

• JPEG(visual)performwellinhigherbitratesinlossy case

• FLIF inaverageproducesbestlosslesscompression performancewhencomparedtoallalternativestestedfortheimagestested

Nextsteps

• Thereisevidencethatsignificantimprovementsincompressionefficiencycanbeobtainedusinglateststateoftheartinlossy andlossless cases

• Furtherevaluationsareneededtobetterquantifythecostofsuchhigherefficiencyincompressionintermsofrequiredresourcesandotherfeatures(delay,etc.)

• Conclusionsregardingcompressionefficiencyneedtobeverifiedusingalargerdatasetandthroughmoreextensiveevaluationscampaignssuchascrowdsourcing

• Keepinmindtheseresultscompareencodersandnotcodingalgorithms(inparticulardecoders!)

•Manyofthealgorithmsundertestareindevelopmentandtheirperformancecanstillimprove

Thankyouforyourattention!

ICIP2016 image compression grand challenge

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