Table Of ContentLuís Filipe Rosário Lucas
Eduardo Antônio Barros da Silva
Sérgio Manuel Maciel de Faria
Nuno Miguel Morais Rodrigues
Carla Liberal Pagliari
Effi cient Predictive
Algorithms
for Image
Compression
Efficient Predictive Algorithms for Image
Compression
Luís Filipe Rosário Lucas
Eduardo Antônio Barros da Silva
Sérgio Manuel Maciel de Faria
Nuno Miguel Morais Rodrigues
Carla Liberal Pagliari
Efficient Predictive
Algorithms for Image
Compression
123
LuísFilipeRosárioLucas EduardoAntônioBarrosdaSilva
InstitutodeTelecomunicações ProgramadeEngenhariaElétrica
ESTG–InstitutoPolitécnicodeLeiria UniversidadeFederaldoRiodeJaneiro
Leiria,Portugal RiodeJaneiro,Brazil
SérgioManuelMacieldeFaria NunoMiguelMoraisRodrigues
InstitutodeTelecomunicações InstitutodeTelecomunicações
ESTG–InstitutoPolitécnicodeLeiria ESTG–InstitutoPolitécnicodeLeiria
Leiria,Portugal Leiria,Portugal
CarlaLiberalPagliari
DepartamentodeEngenhariaElétrica(SE/3)
InstitutoMilitardeEngenharia
Urca-RiodeJaneiro,Brazil
ISBN978-3-319-51179-5 ISBN978-3-319-51180-1 (eBook)
DOI10.1007/978-3-319-51180-1
LibraryofCongressControlNumber:2016961282
©SpringerInternationalPublishingSwitzerland2017
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Contents
1 Introduction .................................................................. 1
1.1 Motivation .............................................................. 1
1.2 ObjectivesandContributionsoftheBook ............................ 3
1.3 OutlineoftheBook .................................................... 5
2 PredictionTechniquesforImageandVideoCoding..................... 7
2.1 DigitalVideoRepresentation .......................................... 7
2.2 ImagePredictionOverview............................................ 9
2.3 State-of-the-ArtPredictionMethods .................................. 13
2.3.1 Intra-FramePrediction......................................... 13
2.3.2 Inter-FramePrediction......................................... 17
2.4 Least-SquaresPredictionMethods .................................... 20
2.4.1 LinearPredictionofImagesandVideoUsingLSP .......... 21
2.4.2 Context-BasedAdaptiveLSP.................................. 22
2.4.3 Block-BasedLSP .............................................. 23
2.4.4 Spatio-TemporalLSP.......................................... 24
2.5 SparseRepresentationforImagePrediction .......................... 25
2.5.1 SparsePredictionProblemFormulation ...................... 26
2.5.2 MatchingPursuitMethods..................................... 28
2.5.3 TemplateMatchingAlgorithm ................................ 29
2.5.4 NeighbourEmbeddingMethods............................... 30
2.6 Conclusions............................................................. 33
3 ImageandVideoCodingStandards....................................... 35
3.1 HybridVideoCompression............................................ 35
3.2 Compressionof2DVideo.............................................. 37
3.2.1 H.265/HEVCStandard......................................... 38
3.2.2 ExperimentalResults .......................................... 46
3.3 Compressionof3DVideo.............................................. 49
3.3.1 3DVideoSystems.............................................. 49
v
vi Contents
3.3.2 3DVideoCodingStandards................................... 58
3.3.3 ExperimentalResults .......................................... 61
3.4 Conclusions............................................................. 64
4 CompressionofDepthMapsUsingPredictiveCoding .................. 65
4.1 OverviewofIntraTechniquesforDepthMapCoding................ 66
4.1.1 DirectionalIntraPrediction.................................... 67
4.1.2 DepthModellingModes....................................... 68
4.1.3 DepthLookupTable ........................................... 69
4.1.4 Segment-WiseDCCoding..................................... 69
4.1.5 SingleDepthIntraMode....................................... 70
4.1.6 ViewSynthesisOptimisation.................................. 70
4.2 OverviewofPredictiveDepthCoding................................. 71
4.3 CodingTechniquesofPDCAlgorithm................................ 73
4.3.1 FlexibleBlockPartitioning.................................... 73
4.3.2 DirectionalIntraPredictionFramework ...................... 75
4.3.3 ConstrainedDepthModellingMode.......................... 77
4.3.4 ResidualSignalCoding........................................ 79
4.3.5 BitstreamSyntaxandContextModelling..................... 81
4.4 PDCEncoderControl.................................................. 83
4.5 ExperimentalResults................................................... 84
4.5.1 EvaluationofPDCAlgorithmforIntraCoding .............. 85
4.5.2 EvaluationofPDCAlgorithmUsingVSOMetric ........... 91
4.5.3 Evaluation of PDC Algorithm Combined
with3D-HEVCStandard ...................................... 93
4.6 Conclusions............................................................. 95
5 SparseRepresentationMethodsforImagePrediction................... 97
5.1 3DHoloscopicImageCodingUsingLLE-BasedPrediction......... 98
5.1.1 ProposedHEVCEncoderUsingLLE-BasedPrediction..... 99
5.1.2 ExperimentalResults .......................................... 101
5.2 TheSparse-LSPMethodforIntraPrediction ......................... 104
5.2.1 AlgorithmDescription......................................... 105
5.2.2 MathematicalInterpretation ................................... 107
5.3 ApplicationofSparse-LSPtoHEVCStandard....................... 109
5.3.1 ImplementationDetails........................................ 110
5.3.2 ExperimentalResults .......................................... 111
5.4 Conclusions............................................................. 115
6 GeneralisedOptimalSparsePredictors................................... 117
6.1 Two-StageInterpretationofDirectionalPrediction................... 118
6.2 GeneralisingDirectionalPrediction................................... 122
6.3 SparseModelEstimationAlgorithms ................................. 124
6.3.1 MatchingPursuitAlgorithms.................................. 125
6.3.2 LeastAngleRegression........................................ 125
6.3.3 LASSORegression ............................................ 126
6.3.4 ElasticNetRegression......................................... 127
Contents vii
6.4 Proposed Algorithm Based on Adaptive Sparse
PredictorsforHEVC................................................... 128
6.5 ExperimentalResults................................................... 130
6.5.1 EffectofSparsityConstraints ................................. 131
6.5.2 RegularisationParametersforOptimalRDPerformance.... 133
6.5.3 RD Performance Relative to Other Intra
PredictionMethods ............................................ 140
6.6 Conclusions............................................................. 143
7 ConclusionsandOtherResearchDirections.............................. 145
A TestSignals ................................................................... 149
A.1 TestImages ............................................................. 149
A.2 HoloscopicImages ..................................................... 153
References......................................................................... 157
Index............................................................................... 165
List of Figures
Fig.2.1 DPCMblockdiagramforlossyimagecompression ............... 10
Fig.2.2 Spatialpredictioninarasterscanorderencoder,e.g.
DPCM ................................................................ 10
Fig.2.3 Blockdiagramforagenerictransform-basedhybrid
videocompressionsystem .......................................... 12
Fig.2.4 Available neighbouring regions used by intra
predictionmodesinHEVCstandard ............................... 14
Fig.2.5 IntrapredictionangularmodesusedinHEVCstandard ........... 15
Fig.2.6 SpatialcandidatesofmotioninformationforMerge
mode ................................................................. 19
Fig.2.7 LSPfiltercontext(pixelsrepresentedbywhitecircles)
andassociatedtrainingwindow ..................................... 22
Fig.2.8 Exampleofa13thorderspatio-temporalfiltercontext
usingcausalneighboursfromthecurrentframe(spatial
neighbours)andpreviousencodedframe(temporal
neighbours) .......................................................... 25
Fig.2.9 Approximationsupport(template)andsearchwindow
used in sparse coding and neighbour-embedding
methods .............................................................. 26
Fig.3.1 ArchitectureofHEVCalgorithm ................................... 38
Fig.3.2 Partitioningmodesofaninter-codedCBintoPBs ................ 40
Fig.3.3 SearchwindowusedbyIBC(representedbygray
CTUs) ................................................................ 45
Fig.3.4 Usedtestimages:Barbara(512(cid:2)512)(left,a)and
Poznan Street, camera 4, frame 0 (1920(cid:2)1088)
(right,b) ............................................................. 47
Fig.3.5 Rate-distortionperformanceofHEVCandH.264for
testimages(a)Barbaraand(b)PoznanStreet,camera
4,frame0 ............................................................ 48
ix
x ListofFigures
Fig.3.6 Rate-distortionperformanceofHEVCandH.264for
testvideosequences(a)RaceHorses(classC)and(b)
BasketballDrive(classB) .......................................... 50
Fig.3.7 Frame-compatibleformatsforstereovideo:topimages
representanexampleofastereopair;bottomimages
representtheside-by-sideandtop-bottomformatsof
thegivenstereopair ................................................. 52
Fig.3.8 ExampleofMVDformatusingthreetexturevideo
views(left)andthreedepthmapviews(right) ..................... 54
Fig.3.9 A3Dholoscopicimagingsystem [57]:(a)acquisition
side;(b)displayside ................................................ 57
Fig.3.10 Holoscopic image captured with a 250(cid:2)m pitch
micro-lensarray:(a)fullimagewithresolutionof
1920(cid:2)1088;(b)enlargementof196(cid:2)140pixels
showingthemicro-images .......................................... 57
Fig.3.11 Prediction dependencies in 3D-HEVC standard
for multiview video coding: inter-viewprediction
is represented solid arrows and inter-component
predictionisrepresentedbydottedarrows ......................... 59
Fig.3.12 Rate-distortionperformanceofsharktestsequence
usingthe3D-HEVC,3D-HEVCwithoutVSOand
theMV-HEVC configurations: (a)average PSNR
oftextureviewsversusbitrateoftextureviews(b)
averagePSNRofsynthesisedviewsversusbitrateof
depthmaps ........................................................... 63
Fig.4.1 ExampleofWedgelet(left)andContour(right)block
partitions ............................................................. 68
Fig.4.2 SVDC procedure for the distorted depth block
identified in bottom depth map (labelled by
testedblock) ......................................................... 70
Fig.4.3 Blockdiagramoftheintra-basedPDCalgorithm .................. 72
Fig.4.4 Possible block sizes in PDC and respective label
numbers .............................................................. 74
Fig.4.5 Example of an optimal block partitioning and
correspondingsegmentationtreeinPDCalgorithm
usingquadtreeplusrecursiveflexiblepartitioning ................. 74
Fig.4.6 Exampleofeasytopredictedges(leftandmiddle)and
adifficulttopredictedge(right) .................................... 77
Fig.4.7 BlockpartitionexamplesusingCDMM ............................ 78
Fig.4.8 DifferentCDMMpartitionslopesprovidedbyflexible
partitioning ........................................................... 78
Fig.4.9 DetailedschematicofthePDCresiduecodingmethod ........... 80
ListofFigures xi
Fig.4.10 Detail of the third virtual view (vv3) of Poznan
Street (frame 0) synthesised using (a) reference
uncompresseddepthmaps,(b)3D-HEVC(RefHTM)
encodeddepthmapsand(c)PDCencodeddepthmaps,
forrate-distortionpoint3(p3) ...................................... 89
Fig.4.11 AveragepredictionmodeusageinPDCalgorithmfor
alltestedlambdasandencodedviewsofeachtest
sequence ............................................................. 91
Fig.4.12 AverageBDRATEvaluesofPDCalgorithmusing
SSEandVSOdistortionmetricsrelativeto3D-HEVC
usingRefHTM andRefHTMCVSOconfigurations,
respectively .......................................................... 92
Fig.4.13 AverageRDperformanceofsharksequenceusing
PDC and 3D-HEVC algorithms under various
configurations ........................................................ 92
Fig.4.14 AverageBDRATEresultsofthemodified3D-HEVC
(using the PDC encoded I-frames) relative to the
3D-HEVC RefCTC and RefCTC_withoutContour
configurations ........................................................ 94
Fig.5.1 Set of HEVC prediction modes replaced by the
LLE-basedpredictionmodes,representedbydashed
arrowsandboldnumbers ........................................... 100
Fig.5.2 SearchwindowusedbytheproposedLLE-basedintra
prediction ............................................................ 101
Fig.5.3 Rate-distortionresultsforPlaneandToy(frame0)
holoscopicimage .................................................... 102
Fig.5.4 Rate-distortionresultsforDemichelisCut(frame0)
holoscopicimage .................................................... 102
Fig.5.5 Rate-distortionresultsforDemichelisSpark(frame0)
holoscopicimage .................................................... 103
Fig.5.6 Rate-distortionresultsforLauraholoscopicimage ................ 103
Fig.5.7 Proposed training window and filter context for
sparse-LSPalgorithm ............................................... 105
Fig.5.8 Selectionofnon-nullcoefficients’positionsusingTM
algorithm(equivalenttok-NNmethod)withinacausal
searchwindow ....................................................... 107
Fig.5.9 Rate-distortioncurvescomparingSLSP,LLE,TMand
LSPmethodsusingHEVCstandard,forBarbaraimage .......... 113
Fig.5.10 Rate-distortioncurvescomparingSLSP,LLE,TMand
LSPmethodsusingHEVCstandard,forWoolimage ............. 114
Fig.6.1 Samplepropagationusingafirst-orderlinearfilter
contextforAngular10,18and26modes.(a)Angular
10—F1.(b)Angular18—F1.(c)Angular26—F1 ............... 119
H D V
