HEVC代码学习42：estIntraPredLumaQT函数

在之前的 HEVC代码学习37：帧内预测代码整体学习中已经提到，estIntraPredLumaQT是亮度帧内预测的入口函数，下面将对该函数进行详细学习。

estIntraPredLumaQT中完成了亮度分量的帧内预测，其主要流程如下：
一、初始化各种参数。
二、为了减少率失真优化次数，HEVC中默认使用帧内快速搜索算法，将分粗选和细选两个阶段进行。如果不使用快速搜索，将对所有帧内预测模式进行率失真优化。
1.粗选阶段：遍历35种帧内预测模式使用predIntraAng计算预测值，计算比较哈达玛失真，调用xUpdateCandList构建全率失真优化候选列表。全率失真优化候选列表长度numModesForFullRD由块宽度决定。注意在粗选阶段全部使用哈达玛变换计算失真，提高速度。
2.细选阶段：调用getIntraDirPredictor构建MPM列表，加入全率失真优化候选列表中。遍历全率失真优化候选列表，调用xRecurIntraCodingLumaQT进行变换量化重构，计算率失真代价，注意在这里该函数倒数第二个参数是bCheckFirst=true，表示会按照四叉树的方式继续向下划分。比较率失真代价，找到最优模式。
三、对获得的最优模式再次调用xRecurIntraCodingLumaQT，此时倒数第二个参数设置为false，检测同一模式下，bCheckFirst为true和false的情况下，选出最优模式为最终的亮度帧内预测模式。
四、收尾工作，记录信息：设置重建块、Cbf、上下文模型、总失真等。

其中调用了重要函数：predIntraAng（计算帧内预测模式的预测值）、getIntraDirPredictor（利用临近PU构建MPM列表）、xRecurIntraCodingLumaQT（进行帧内亮度分量的变换量化重构，计算率失真代价）。

代码分析：

//亮度分量预测
VoidTEncSearch::estIntraPredLumaQT(TComDataCU* pcCU,TComYuv*    pcOrgYuv,TComYuv*    pcPredYuv,TComYuv*    pcResiYuv,TComYuv*    pcRecoYuv,Pel         resiLuma[NUMBER_OF_STORED_RESIDUAL_TYPES][MAX_CU_SIZE * MAX_CU_SIZE]
DEBUG_STRING_FN_DECLARE(sDebug))
{const UInt         uiDepth               = pcCU->getDepth(0);       //划分深度const UInt         uiInitTrDepth         = pcCU->getPartitionSize(0) == SIZE_2Nx2N ? 0 : 1;     //当CU为2Nx2N时，初始变换深度为0；否则为1。const UInt         uiNumPU               = 1<<(2*uiInitTrDepth);        //PU分块数const UInt         uiQNumParts           = pcCU->getTotalNumPart() >> 2;        const UInt         uiWidthBit            = pcCU->getIntraSizeIdx(0);const ChromaFormat chFmt                 = pcCU->getPic()->getChromaFormat();       //颜色格式const UInt         numberValidComponents = getNumberValidComponents(chFmt);const TComSPS     &sps                   = *(pcCU->getSlice()->getSPS());           //SPSconst TComPPS     &pps                   = *(pcCU->getSlice()->getPPS());           //PPSDistortion   uiOverallDistY        = 0;UInt         CandNum;           //候选数Double       CandCostList[ FAST_UDI_MAX_RDMODE_NUM ];       //候选代价列表Pel          resiLumaPU[NUMBER_OF_STORED_RESIDUAL_TYPES][MAX_CU_SIZE * MAX_CU_SIZE];        //亮度残差Bool    bMaintainResidual[NUMBER_OF_STORED_RESIDUAL_TYPES];for (UInt residualTypeIndex = 0; residualTypeIndex < NUMBER_OF_STORED_RESIDUAL_TYPES; residualTypeIndex++){bMaintainResidual[residualTypeIndex] = true; //assume true unless specified otherwise}bMaintainResidual[RESIDUAL_ENCODER_SIDE] = !(m_pcEncCfg->getUseReconBasedCrossCPredictionEstimate());// Lambda calculation at equivalent Qp of 4 is recommended because at that Qp, the quantisation divisor is 1.
#if FULL_NBITconst Double sqrtLambdaForFirstPass= (m_pcEncCfg->getCostMode()==COST_MIXED_LOSSLESS_LOSSY_CODING && pcCU->getCUTransquantBypass(0)) ?sqrt(0.57 * pow(2.0, ((LOSSLESS_AND_MIXED_LOSSLESS_RD_COST_TEST_QP_PRIME - 12) / 3.0))): m_pcRdCost->getSqrtLambda();
#else//计算Lambdaconst Double sqrtLambdaForFirstPass= (m_pcEncCfg->getCostMode()==COST_MIXED_LOSSLESS_LOSSY_CODING && pcCU->getCUTransquantBypass(0)) ?sqrt(0.57 * pow(2.0, ((LOSSLESS_AND_MIXED_LOSSLESS_RD_COST_TEST_QP_PRIME - 12 - 6 * (sps.getBitDepth(CHANNEL_TYPE_LUMA) - 8)) / 3.0))): m_pcRdCost->getSqrtLambda();
#endif//===== set QP and clear Cbf =====//设置QP，清除Cbfif ( pps.getUseDQP() == true){pcCU->setQPSubParts( pcCU->getQP(0), 0, uiDepth );}else{pcCU->setQPSubParts( pcCU->getSlice()->getSliceQp(), 0, uiDepth );}//===== loop over partitions =====//迭代分块TComTURecurse tuRecurseCU(pcCU, 0);TComTURecurse tuRecurseWithPU(tuRecurseCU, false, (uiInitTrDepth==0)?TComTU::DONT_SPLIT : TComTU::QUAD_SPLIT);do      //遍历一个CU的所有PU{const UInt uiPartOffset=tuRecurseWithPU.GetAbsPartIdxTU();      //当前PU的偏移//  for( UInt uiPU = 0, uiPartOffset=0; uiPU < uiNumPU; uiPU++, uiPartOffset += uiQNumParts )//{//===== init pattern for luma prediction =====DEBUG_STRING_NEW(sTemp2)//===== determine set of modes to be tested (using prediction signal only) =====Int numModesAvailable     = 35; //total number of Intra modes       可用模式总数UInt uiRdModeList[FAST_UDI_MAX_RDMODE_NUM];     //根据块宽度设定全率失真优化的模式数Int numModesForFullRD = m_pcEncCfg->getFastUDIUseMPMEnabled()?g_aucIntraModeNumFast_UseMPM[ uiWidthBit ] : g_aucIntraModeNumFast_NotUseMPM[ uiWidthBit ];// this should always be trueassert (tuRecurseWithPU.ProcessComponentSection(COMPONENT_Y));initIntraPatternChType( tuRecurseWithPU, COMPONENT_Y, true DEBUG_STRING_PASS_INTO(sTemp2) );        //初始化/**************************************************快速搜索粗选阶段**************************************************/Bool doFastSearch = (numModesForFullRD != numModesAvailable);       //默认开启快速搜索if (doFastSearch)           //快速搜索  {assert(numModesForFullRD < numModesAvailable);for( Int i=0; i < numModesForFullRD; i++ )      //对numModesForFullRD个候选设置代价为最大{CandCostList[ i ] = MAX_DOUBLE;}CandNum = 0;const TComRectangle &puRect=tuRecurseWithPU.getRect(COMPONENT_Y);       //获取Y分量PUconst UInt uiAbsPartIdx=tuRecurseWithPU.GetAbsPartIdxTU();          //PU地址Pel* piOrg         = pcOrgYuv ->getAddr( COMPONENT_Y, uiAbsPartIdx );       //原始图像Pel* piPred        = pcPredYuv->getAddr( COMPONENT_Y, uiAbsPartIdx );       //预测图像UInt uiStride      = pcPredYuv->getStride( COMPONENT_Y );           //跨度DistParam distParam;        //失真参数const Bool bUseHadamard=pcCU->getCUTransquantBypass(0) == 0;        //是否使用哈达玛变换m_pcRdCost->setDistParam(distParam, sps.getBitDepth(CHANNEL_TYPE_LUMA), piOrg, uiStride, piPred, uiStride, puRect.width, puRect.height, bUseHadamard);      //使用哈达玛变换初始化失真distParam.bApplyWeight = false;for( Int modeIdx = 0; modeIdx < numModesAvailable; modeIdx++ )      //遍历35种帧内预测模式{UInt       uiMode = modeIdx;        //当前模式Distortion uiSad  = 0;      //失真//参考采样滤波const Bool bUseFilter=TComPrediction::filteringIntraReferenceSamples(COMPONENT_Y, uiMode, puRect.width, puRect.height, chFmt, sps.getSpsRangeExtension().getIntraSmoothingDisabledFlag());//计算相应模式的预测值predIntraAng( COMPONENT_Y, uiMode, piOrg, uiStride, piPred, uiStride, tuRecurseWithPU, bUseFilter, TComPrediction::UseDPCMForFirstPassIntraEstimation(tuRecurseWithPU, uiMode) );// use hadamard transform hereuiSad+=distParam.DistFunc(&distParam);      //计算哈达玛失真UInt   iModeBits = 0;           //bit数// NB xModeBitsIntra will not affect the mode for chroma that may have already been pre-estimated.iModeBits+=xModeBitsIntra( pcCU, uiMode, uiPartOffset, uiDepth, CHANNEL_TYPE_LUMA );        //计算bit数Double cost      = (Double)uiSad + (Double)iModeBits * sqrtLambdaForFirstPass;      //使用哈达玛失真计算率失真代价#if DEBUG_INTRA_SEARCH_COSTSstd::cout << "1st pass mode " << uiMode << " SAD = " << uiSad << ", mode bits = " << iModeBits << ", cost = " << cost << "\n";
#endif//比较代价更新候选列表CandNum += xUpdateCandList( uiMode, cost, numModesForFullRD, uiRdModeList, CandCostList );}/**************************************************快速搜索细选阶段**************************************************/if (m_pcEncCfg->getFastUDIUseMPMEnabled())          {Int uiPreds[NUM_MOST_PROBABLE_MODES] = {-1, -1, -1};        //初始化MPM列表，长度为3Int iMode = -1;//利用临近PU构建MPMpcCU->getIntraDirPredictor( uiPartOffset, uiPreds, COMPONENT_Y, &iMode );//当有可用MPM时，numCand就等于MPM对应的模式，否则为NUM_MOST_PROBABLE_MODESconst Int numCand = ( iMode >= 0 ) ? iMode : Int(NUM_MOST_PROBABLE_MODES);//把MPM加入全率失真优化列表中for( Int j=0; j < numCand; j++){Bool mostProbableModeIncluded = false;Int mostProbableMode = uiPreds[j];for( Int i=0; i < numModesForFullRD; i++){mostProbableModeIncluded |= (mostProbableMode == uiRdModeList[i]);}if (!mostProbableModeIncluded){uiRdModeList[numModesForFullRD++] = mostProbableMode;}}}}else        //不启用快速搜索，将所有模式都加入全率失真优化列表中{for( Int i=0; i < numModesForFullRD; i++){uiRdModeList[i] = i;}}//===== check modes (using r-d costs) =====
#if HHI_RQT_INTRA_SPEEDUP_MODUInt   uiSecondBestMode  = MAX_UINT;Double dSecondBestPUCost = MAX_DOUBLE;
#endifDEBUG_STRING_NEW(sPU)UInt       uiBestPUMode  = 0;       //最优模式Distortion uiBestPUDistY = 0;           //最优失真Double     dBestPUCost   = MAX_DOUBLE;      //最优代价#if ENVIRONMENT_VARIABLE_DEBUG_AND_TESTUInt max=numModesForFullRD;if (DebugOptionList::ForceLumaMode.isSet()){max=0;  // we are forcing a direction, so don't bother with mode check}for ( UInt uiMode = 0; uiMode < max; uiMode++)
#else//遍历全率失真优化列表for( UInt uiMode = 0; uiMode < numModesForFullRD; uiMode++ )
#endif{// set luma prediction modeUInt uiOrgMode = uiRdModeList[uiMode];      //原始模式//设置子块的帧内预测模式pcCU->setIntraDirSubParts ( CHANNEL_TYPE_LUMA, uiOrgMode, uiPartOffset, uiDepth + uiInitTrDepth );DEBUG_STRING_NEW(sMode)// set context models//设置上下文模型m_pcRDGoOnSbacCoder->load( m_pppcRDSbacCoder[uiDepth][CI_CURR_BEST] );// determine residual for partitionDistortion uiPUDistY = 0;       //初始化当前失真Double     dPUCost   = 0.0; //初始化当前代价
#if HHI_RQT_INTRA_SPEEDUP//重构帧内亮度分量，计算率失真代价//注意倒数第二个参数bCheckFirst是true，表示会继续按照四叉树的方式向下划分xRecurIntraCodingLumaQT( pcOrgYuv, pcPredYuv, pcResiYuv, resiLumaPU, uiPUDistY, true, dPUCost, tuRecurseWithPU DEBUG_STRING_PASS_INTO(sMode) );
#elsexRecurIntraCodingLumaQT( pcOrgYuv, pcPredYuv, pcResiYuv, resiLumaPU, uiPUDistY, dPUCost, tuRecurseWithPU DEBUG_STRING_PASS_INTO(sMode) );
#endif#if DEBUG_INTRA_SEARCH_COSTSstd::cout << "2nd pass [luma,chroma] mode [" << Int(pcCU->getIntraDir(CHANNEL_TYPE_LUMA, uiPartOffset)) << "," << Int(pcCU->getIntraDir(CHANNEL_TYPE_CHROMA, uiPartOffset)) << "] cost = " << dPUCost << "\n";
#endif// check r-d cost//如果当前代价小于最优代价，将当前模式置为最优模式if( dPUCost < dBestPUCost ){DEBUG_STRING_SWAP(sPU, sMode)
#if HHI_RQT_INTRA_SPEEDUP_MODuiSecondBestMode  = uiBestPUMode;dSecondBestPUCost = dBestPUCost;
#endifuiBestPUMode  = uiOrgMode;uiBestPUDistY = uiPUDistY;dBestPUCost   = dPUCost;xSetIntraResultLumaQT( pcRecoYuv, tuRecurseWithPU );if (pps.getPpsRangeExtension().getCrossComponentPredictionEnabledFlag()){const Int xOffset = tuRecurseWithPU.getRect( COMPONENT_Y ).x0;const Int yOffset = tuRecurseWithPU.getRect( COMPONENT_Y ).y0;for (UInt storedResidualIndex = 0; storedResidualIndex < NUMBER_OF_STORED_RESIDUAL_TYPES; storedResidualIndex++){if (bMaintainResidual[storedResidualIndex]){xStoreCrossComponentPredictionResult(resiLuma[storedResidualIndex], resiLumaPU[storedResidualIndex], tuRecurseWithPU, xOffset, yOffset, MAX_CU_SIZE, MAX_CU_SIZE );}}}UInt uiQPartNum = tuRecurseWithPU.GetAbsPartIdxNumParts();::memcpy( m_puhQTTempTrIdx,  pcCU->getTransformIdx()       + uiPartOffset, uiQPartNum * sizeof( UChar ) );for (UInt component = 0; component < numberValidComponents; component++){const ComponentID compID = ComponentID(component);::memcpy( m_puhQTTempCbf[compID], pcCU->getCbf( compID  ) + uiPartOffset, uiQPartNum * sizeof( UChar ) );::memcpy( m_puhQTTempTransformSkipFlag[compID],  pcCU->getTransformSkip(compID)  + uiPartOffset, uiQPartNum * sizeof( UChar ) );}}
#if HHI_RQT_INTRA_SPEEDUP_MODelse if( dPUCost < dSecondBestPUCost ){uiSecondBestMode  = uiOrgMode;dSecondBestPUCost = dPUCost;}
#endif} // Mode loop#if HHI_RQT_INTRA_SPEEDUP
#if HHI_RQT_INTRA_SPEEDUP_MODfor( UInt ui =0; ui < 2; ++ui )
#endif{
#if HHI_RQT_INTRA_SPEEDUP_MODUInt uiOrgMode   = ui ? uiSecondBestMode  : uiBestPUMode;if( uiOrgMode == MAX_UINT ){break;}
#else/****************************************************已获得最优模式，使用最优模式进行预测变换量化重构等，计算最终的率失真代价*********************************************************///取最优模式UInt uiOrgMode = uiBestPUMode;
#endif#if ENVIRONMENT_VARIABLE_DEBUG_AND_TESTif (DebugOptionList::ForceLumaMode.isSet()){uiOrgMode = DebugOptionList::ForceLumaMode.getInt();}
#endif//设置子块的帧内预测模式pcCU->setIntraDirSubParts ( CHANNEL_TYPE_LUMA, uiOrgMode, uiPartOffset, uiDepth + uiInitTrDepth );DEBUG_STRING_NEW(sModeTree)//加载上下文模型// set context modelsm_pcRDGoOnSbacCoder->load( m_pppcRDSbacCoder[uiDepth][CI_CURR_BEST] );// determine residual for partitionDistortion uiPUDistY = 0;Double     dPUCost   = 0.0;//重构亮度分量//注意倒数第二个参数bCheckFirst是false，表示当前PU不再进行划分，即只处理当前深度的PU  xRecurIntraCodingLumaQT( pcOrgYuv, pcPredYuv, pcResiYuv, resiLumaPU, uiPUDistY, false, dPUCost, tuRecurseWithPU DEBUG_STRING_PASS_INTO(sModeTree));// check r-d cost//检测同一模式下，bCheckFirst为true和false的情况下，选最优  if( dPUCost < dBestPUCost ){DEBUG_STRING_SWAP(sPU, sModeTree)uiBestPUMode  = uiOrgMode;uiBestPUDistY = uiPUDistY;dBestPUCost   = dPUCost;xSetIntraResultLumaQT( pcRecoYuv, tuRecurseWithPU );if (pps.getPpsRangeExtension().getCrossComponentPredictionEnabledFlag()){const Int xOffset = tuRecurseWithPU.getRect( COMPONENT_Y ).x0;const Int yOffset = tuRecurseWithPU.getRect( COMPONENT_Y ).y0;for (UInt storedResidualIndex = 0; storedResidualIndex < NUMBER_OF_STORED_RESIDUAL_TYPES; storedResidualIndex++){if (bMaintainResidual[storedResidualIndex]){xStoreCrossComponentPredictionResult(resiLuma[storedResidualIndex], resiLumaPU[storedResidualIndex], tuRecurseWithPU, xOffset, yOffset, MAX_CU_SIZE, MAX_CU_SIZE );}}}const UInt uiQPartNum = tuRecurseWithPU.GetAbsPartIdxNumParts();::memcpy( m_puhQTTempTrIdx,  pcCU->getTransformIdx()       + uiPartOffset, uiQPartNum * sizeof( UChar ) );for (UInt component = 0; component < numberValidComponents; component++){const ComponentID compID = ComponentID(component);::memcpy( m_puhQTTempCbf[compID], pcCU->getCbf( compID  ) + uiPartOffset, uiQPartNum * sizeof( UChar ) );::memcpy( m_puhQTTempTransformSkipFlag[compID],  pcCU->getTransformSkip(compID)  + uiPartOffset, uiQPartNum * sizeof( UChar ) );}}} // Mode loop
#endifDEBUG_STRING_APPEND(sDebug, sPU)//--- update overall distortion ---uiOverallDistY += uiBestPUDistY;//--- update transform index and cbf ---const UInt uiQPartNum = tuRecurseWithPU.GetAbsPartIdxNumParts();::memcpy( pcCU->getTransformIdx()       + uiPartOffset, m_puhQTTempTrIdx,  uiQPartNum * sizeof( UChar ) );for (UInt component = 0; component < numberValidComponents; component++){const ComponentID compID = ComponentID(component);::memcpy( pcCU->getCbf( compID  ) + uiPartOffset, m_puhQTTempCbf[compID], uiQPartNum * sizeof( UChar ) );::memcpy( pcCU->getTransformSkip( compID  ) + uiPartOffset, m_puhQTTempTransformSkipFlag[compID ], uiQPartNum * sizeof( UChar ) );}//--- set reconstruction for next intra prediction blocks ---//设置重建块if( !tuRecurseWithPU.IsLastSection() ){const TComRectangle &puRect=tuRecurseWithPU.getRect(COMPONENT_Y);const UInt  uiCompWidth   = puRect.width;const UInt  uiCompHeight  = puRect.height;const UInt  uiZOrder      = pcCU->getZorderIdxInCtu() + uiPartOffset;Pel*  piDes         = pcCU->getPic()->getPicYuvRec()->getAddr( COMPONENT_Y, pcCU->getCtuRsAddr(), uiZOrder );const UInt  uiDesStride   = pcCU->getPic()->getPicYuvRec()->getStride( COMPONENT_Y);const Pel*  piSrc         = pcRecoYuv->getAddr( COMPONENT_Y, uiPartOffset );const UInt  uiSrcStride   = pcRecoYuv->getStride( COMPONENT_Y);for( UInt uiY = 0; uiY < uiCompHeight; uiY++, piSrc += uiSrcStride, piDes += uiDesStride ){for( UInt uiX = 0; uiX < uiCompWidth; uiX++ ){piDes[ uiX ] = piSrc[ uiX ];}}}//=== update PU data ====pcCU->setIntraDirSubParts     ( CHANNEL_TYPE_LUMA, uiBestPUMode, uiPartOffset, uiDepth + uiInitTrDepth );} while (tuRecurseWithPU.nextSection(tuRecurseCU));//设置Cbfif( uiNumPU > 1 ){ // set Cbf for all blocksUInt uiCombCbfY = 0;UInt uiCombCbfU = 0;UInt uiCombCbfV = 0;UInt uiPartIdx  = 0;for( UInt uiPart = 0; uiPart < 4; uiPart++, uiPartIdx += uiQNumParts ){uiCombCbfY |= pcCU->getCbf( uiPartIdx, COMPONENT_Y,  1 );uiCombCbfU |= pcCU->getCbf( uiPartIdx, COMPONENT_Cb, 1 );uiCombCbfV |= pcCU->getCbf( uiPartIdx, COMPONENT_Cr, 1 );}for( UInt uiOffs = 0; uiOffs < 4 * uiQNumParts; uiOffs++ ){pcCU->getCbf( COMPONENT_Y  )[ uiOffs ] |= uiCombCbfY;pcCU->getCbf( COMPONENT_Cb )[ uiOffs ] |= uiCombCbfU;pcCU->getCbf( COMPONENT_Cr )[ uiOffs ] |= uiCombCbfV;}}//===== reset context models =====//重置上下文模型m_pcRDGoOnSbacCoder->load(m_pppcRDSbacCoder[uiDepth][CI_CURR_BEST]);//===== set distortion (rate and r-d costs are determined later) =====//设置总失真pcCU->getTotalDistortion() = uiOverallDistY;
}

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