HM编码器代码阅读(22)——cabac的流程

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熵编码初始化

cavlc（变长码）在HEVC中的实现比较简单，这里就主要说cabac在HEVC中的实现

初始化函数：TEncSbac::resetEntropy：

这个函数的实质就是初始化各种上下文

// 重置熵编码器Void TEncSbac::resetEntropy           (){Int  iQp              = m_pcSlice->getSliceQp();SliceType eSliceType  = m_pcSlice->getSliceType();Int  encCABACTableIdx = m_pcSlice->getPPS()->getEncCABACTableIdx();if (!m_pcSlice->isIntra() && (encCABACTableIdx==B_SLICE || encCABACTableIdx==P_SLICE) && m_pcSlice->getPPS()->getCabacInitPresentFlag()){eSliceType = (SliceType) encCABACTableIdx;}// 初始化各个模型的缓存// split标志的上下文m_cCUSplitFlagSCModel.initBuffer       ( eSliceType, iQp, (UChar*)INIT_SPLIT_FLAG );// skip标志的上下文m_cCUSkipFlagSCModel.initBuffer        ( eSliceType, iQp, (UChar*)INIT_SKIP_FLAG );// merge标志上下文m_cCUMergeFlagExtSCModel.initBuffer    ( eSliceType, iQp, (UChar*)INIT_MERGE_FLAG_EXT);// merge索引上下文m_cCUMergeIdxExtSCModel.initBuffer     ( eSliceType, iQp, (UChar*)INIT_MERGE_IDX_EXT);// partsize上下文m_cCUPartSizeSCModel.initBuffer        ( eSliceType, iQp, (UChar*)INIT_PART_SIZE );// 预测上下文m_cCUPredModeSCModel.initBuffer        ( eSliceType, iQp, (UChar*)INIT_PRED_MODE );// 帧内预测上下文m_cCUIntraPredSCModel.initBuffer       ( eSliceType, iQp, (UChar*)INIT_INTRA_PRED_MODE );// 色度预测上下文m_cCUChromaPredSCModel.initBuffer      ( eSliceType, iQp, (UChar*)INIT_CHROMA_PRED_MODE );// 帧间预测角度上下文m_cCUInterDirSCModel.initBuffer        ( eSliceType, iQp, (UChar*)INIT_INTER_DIR );// MV残差上下文m_cCUMvdSCModel.initBuffer             ( eSliceType, iQp, (UChar*)INIT_MVD );// 参考帧上下文m_cCURefPicSCModel.initBuffer          ( eSliceType, iQp, (UChar*)INIT_REF_PIC );// 量化步长上下文m_cCUDeltaQpSCModel.initBuffer         ( eSliceType, iQp, (UChar*)INIT_DQP );// CBF上下文m_cCUQtCbfSCModel.initBuffer           ( eSliceType, iQp, (UChar*)INIT_QT_CBF );// 四叉树根的CBF上下文m_cCUQtRootCbfSCModel.initBuffer       ( eSliceType, iQp, (UChar*)INIT_QT_ROOT_CBF );// 系数的符号上下文m_cCUSigCoeffGroupSCModel.initBuffer   ( eSliceType, iQp, (UChar*)INIT_SIG_CG_FLAG );// 符号上下文m_cCUSigSCModel.initBuffer             ( eSliceType, iQp, (UChar*)INIT_SIG_FLAG );// 最后一个X上下文m_cCuCtxLastX.initBuffer               ( eSliceType, iQp, (UChar*)INIT_LAST );// 最后一个Y上下文m_cCuCtxLastY.initBuffer               ( eSliceType, iQp, (UChar*)INIT_LAST );m_cCUOneSCModel.initBuffer             ( eSliceType, iQp, (UChar*)INIT_ONE_FLAG );// CU绝对索引？上下文m_cCUAbsSCModel.initBuffer             ( eSliceType, iQp, (UChar*)INIT_ABS_FLAG );// MVP索引上下文m_cMVPIdxSCModel.initBuffer            ( eSliceType, iQp, (UChar*)INIT_MVP_IDX );// TU划分标志上下文m_cCUTransSubdivFlagSCModel.initBuffer ( eSliceType, iQp, (UChar*)INIT_TRANS_SUBDIV_FLAG );// SAO merge上下文m_cSaoMergeSCModel.initBuffer      ( eSliceType, iQp, (UChar*)INIT_SAO_MERGE_FLAG );// SAO类型索引上下文m_cSaoTypeIdxSCModel.initBuffer        ( eSliceType, iQp, (UChar*)INIT_SAO_TYPE_IDX );// 变换skip标志上下文m_cTransformSkipSCModel.initBuffer     ( eSliceType, iQp, (UChar*)INIT_TRANSFORMSKIP_FLAG );// 变换量化bypass标志上下文m_CUTransquantBypassFlagSCModel.initBuffer( eSliceType, iQp, (UChar*)INIT_CU_TRANSQUANT_BYPASS_FLAG );// new structurem_uiLastQp = iQp;// 二值化的一些操作m_pcBinIf->start();return;}

Void TEncBinCABAC::start(){m_uiLow            = 0;// 下限（进行二值化的时候）m_uiRange          = 510;// 范围（进行二值化的时候）m_bitsLeft         = 23;m_numBufferedBytes = 0;m_bufferedByte     = 0xff;}

工程中预定义了一些初始的上下文模型，预定义的各个上下模型实际就是一些二维数组：

// 上下文模型的数量#define MAX_NUM_CTX_MOD             512       ///< maximum number of supported contexts// 上下文split标志的数量#define NUM_SPLIT_FLAG_CTX            3       ///< number of context models for split flag// 上下文skip标志的数量#define NUM_SKIP_FLAG_CTX             3       ///< number of context models for skip flag// 上下文merge扩展中merge标志的数量#define NUM_MERGE_FLAG_EXT_CTX        1       ///< number of context models for merge flag of merge extended// 上下文merge扩展中merge index的数量#define NUM_MERGE_IDX_EXT_CTX         1       ///< number of context models for merge index of merge extended// partition的类型#define NUM_PART_SIZE_CTX             4       ///< number of context models for partition size// 预测的类型#define NUM_PRED_MODE_CTX             1       ///< number of context models for prediction mode// 帧内预测的数量#define NUM_ADI_CTX                   1       ///< number of context models for intra prediction// 色度帧内预测的数量#define NUM_CHROMA_PRED_CTX           2       ///< number of context models for intra prediction (chroma)// 用于帧间预测的上下文模型的数量#define NUM_INTER_DIR_CTX             5       ///< number of context models for inter prediction direction// 用于MV的上下文模型的数量#define NUM_MV_RES_CTX                2       ///< number of context models for motion vector difference// 用于参考索引的上下文模型的数量#define NUM_REF_NO_CTX                2       ///< number of context models for reference index// 用于变换切分的的上下文模型的数量#define NUM_TRANS_SUBDIV_FLAG_CTX     3       ///< number of context models for transform subdivision flags// 用于量化树的CBF的上下文模型的数量#define NUM_QT_CBF_CTX                4       ///< number of context models for QT CBF// 用于量化树根的CBF的上下文模型的数量#define NUM_QT_ROOT_CBF_CTX           1       ///< number of context models for QT ROOT CBF// 用于dQP的上下文模型的数量#define NUM_DELTA_QP_CTX              3       ///< number of context models for dQP//#define NUM_SIG_CG_FLAG_CTX           2       ///< number of context models for MULTI_LEVEL_SIGNIFICANCE// 用于符号（正负号）标志的上下文模型的数量#define NUM_SIG_FLAG_CTX              42      ///< number of context models for sig flag// 用于亮度符号标志的上下文模型的数量#define NUM_SIG_FLAG_CTX_LUMA         27      ///< number of context models for luma sig flag// 用于色度符号标志的上下文模型的数量#define NUM_SIG_FLAG_CTX_CHROMA       15      ///< number of context models for chroma sig flag// 用于 最后一个系数 的上下文模型的数量#define NUM_CTX_LAST_FLAG_XY          15      ///< number of context models for last coefficient position#define NUM_ONE_FLAG_CTX              24      ///< number of context models for greater than 1 flag#define NUM_ONE_FLAG_CTX_LUMA         16      ///< number of context models for greater than 1 flag of luma#define NUM_ONE_FLAG_CTX_CHROMA        8      ///< number of context models for greater than 1 flag of chroma#define NUM_ABS_FLAG_CTX               6      ///< number of context models for greater than 2 flag#define NUM_ABS_FLAG_CTX_LUMA          4      ///< number of context models for greater than 2 flag of luma#define NUM_ABS_FLAG_CTX_CHROMA        2      ///< number of context models for greater than 2 flag of chroma// 用于MVP索引的上下文模型的数量#define NUM_MVP_IDX_CTX               1       ///< number of context models for MVP index// 用于SAO merge的上下文模型的数量#define NUM_SAO_MERGE_FLAG_CTX        1       ///< number of context models for SAO merge flags// 用于SAO 类型索引的上下文模型的数量#define NUM_SAO_TYPE_IDX_CTX          1       ///< number of context models for SAO type index// 用于变换skip的上下文模型的数量#define NUM_TRANSFORMSKIP_FLAG_CTX    1       ///< number of context models for transform skipping #define NUM_CU_TRANSQUANT_BYPASS_FLAG_CTX  1 #define CNU                          154      ///< dummy initialization value for unused context models 'Context model Not Used'// ====================================================================================================================// Tables// ====================================================================================================================// initial probability for cu_transquant_bypass flag// cu_transquant_bypass标志的上下文模型static const UCharINIT_CU_TRANSQUANT_BYPASS_FLAG[3][NUM_CU_TRANSQUANT_BYPASS_FLAG_CTX] ={{ 154 }, { 154 }, { 154 }, };// initial probability for split flag// split标志的上下文模型static const UChar INIT_SPLIT_FLAG[3][NUM_SPLIT_FLAG_CTX] =  {{ 107,  139,  126, },{ 107,  139,  126, }, { 139,  141,  157, }, };// skip标志的上下文模型static const UChar INIT_SKIP_FLAG[3][NUM_SKIP_FLAG_CTX] =  {{ 197,  185,  201, }, { 197,  185,  201, }, { CNU,  CNU,  CNU, }, };// merge模式的标志的上下文static const UCharINIT_MERGE_FLAG_EXT[3][NUM_MERGE_FLAG_EXT_CTX] = {{ 154, }, { 110, }, { CNU, }, };// merge模式的索引的上下文static const UChar INIT_MERGE_IDX_EXT[3][NUM_MERGE_IDX_EXT_CTX] =  {{ 137, }, { 122, }, { CNU, }, };// part_size的上下文static const UChar INIT_PART_SIZE[3][NUM_PART_SIZE_CTX] =  {{ 154,  139,  154,  154 },{ 154,  139,  154,  154 },{ 184,  CNU,  CNU,  CNU },};// 预测模式的上下文static const UCharINIT_PRED_MODE[3][NUM_PRED_MODE_CTX] = {{ 134, }, { 149, }, { CNU, }, };// 帧内预测的模式（亮度）的上下文static const UChar INIT_INTRA_PRED_MODE[3][NUM_ADI_CTX] = {{ 183, }, { 154, }, { 184, }, };// 帧内预测的模式（色度）的上下文static const UChar INIT_CHROMA_PRED_MODE[3][NUM_CHROMA_PRED_CTX] = {{ 152,  139, }, { 152,  139, }, {  63,  139, }, };// 帧间预测方向（前后两个方向）static const UChar INIT_INTER_DIR[3][NUM_INTER_DIR_CTX] = {{  95,   79,   63,   31,  31, }, {  95,   79,   63,   31,  31, }, { CNU,  CNU,  CNU,  CNU, CNU, }, };// MV残差上下文static const UChar INIT_MVD[3][NUM_MV_RES_CTX] =  {{ 169,  198, }, { 140,  198, }, { CNU,  CNU, }, };// 参考帧上下文static const UChar INIT_REF_PIC[3][NUM_REF_NO_CTX] =  {{ 153,  153 }, { 153,  153 }, { CNU,  CNU }, };// dQP// 量化步长上下文static const UChar INIT_DQP[3][NUM_DELTA_QP_CTX] = {{ 154,  154,  154, }, { 154,  154,  154, }, { 154,  154,  154, }, };// CBF（编码块标志）上下文static const UChar INIT_QT_CBF[3][2*NUM_QT_CBF_CTX] =  {{ 153,  111,  CNU,  CNU,   149,   92,  167,  154 },{ 153,  111,  CNU,  CNU,   149,  107,  167,  154 },{ 111,  141,  CNU,  CNU,    94,  138,  182,  154 },};static const UChar INIT_QT_ROOT_CBF[3][NUM_QT_ROOT_CBF_CTX] = {{  79, }, {  79, }, { CNU, }, };// 最后一个系数的位置static const UChar INIT_LAST[3][2*NUM_CTX_LAST_FLAG_XY] =  {{ 125,  110,  124,  110,   95,   94,  125,  111,  111,   79,  125,  126,  111,  111,   79,108,  123,   93,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU, }, { 125,  110,   94,  110,   95,   79,  125,  111,  110,   78,  110,  111,  111,   95,   94,108,  123,  108,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,}, { 110,  110,  124,  125,  140,  153,  125,  127,  140,  109,  111,  143,  127,  111,   79, 108,  123,   63,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU,  CNU, }, };static const UChar INIT_SIG_CG_FLAG[3][2 * NUM_SIG_CG_FLAG_CTX] =  {{ 121,  140,  61,  154, }, { 121,  140, 61,  154, }, {  91,  171,  134,  141, }, };// 整数符号的标志static const UChar INIT_SIG_FLAG[3][NUM_SIG_FLAG_CTX] = {{ 170,  154,  139,  153,  139,  123,  123,   63,  124,  166,  183,  140,  136,  153,  154,  166,  183,  140,  136,  153,  154,  166,  183,  140,  136,  153,  154,  170,  153,  138,  138,  122,  121,  122,  121,  167,  151,  183,  140,  151,  183,  140,  }, { 155,  154,  139,  153,  139,  123,  123,   63,  153,  166,  183,  140,  136,  153,  154,  166,  183,  140,  136,  153,  154,  166,  183,  140,  136,  153,  154,  170,  153,  123,  123,  107,  121,  107,  121,  167,  151,  183,  140,  151,  183,  140,  }, { 111,  111,  125,  110,  110,   94,  124,  108,  124,  107,  125,  141,  179,  153,  125,  107,  125,  141,  179,  153,  125,  107,  125,  141,  179,  153,  125,  140,  139,  182,  182,  152,  136,  152,  136,  153,  136,  139,  111,  136,  139,  111,  }, };// 1标志上下文static const UChar INIT_ONE_FLAG[3][NUM_ONE_FLAG_CTX] = {{ 154,  196,  167,  167,  154,  152,  167,  182,  182,  134,  149,  136,  153,  121,  136,  122,  169,  208,  166,  167,  154,  152,  167,  182, }, { 154,  196,  196,  167,  154,  152,  167,  182,  182,  134,  149,  136,  153,  121,  136,  137,  169,  194,  166,  167,  154,  167,  137,  182, }, { 140,   92,  137,  138,  140,  152,  138,  139,  153,   74,  149,   92,  139,  107,  122,  152,  140,  179,  166,  182,  140,  227,  122,  197, }, };// 帧内预测索引static const UChar INIT_ABS_FLAG[3][NUM_ABS_FLAG_CTX] =  {{ 107,  167,   91,  107,  107,  167, }, { 107,  167,   91,  122,  107,  167, }, { 138,  153,  136,  167,  152,  152, }, };// MVP索引static const UChar INIT_MVP_IDX[3][NUM_MVP_IDX_CTX] =  {{ 168 },{ 168 },{ CNU }, };// SAO merge标志static const UChar INIT_SAO_MERGE_FLAG[3][NUM_SAO_MERGE_FLAG_CTX] = {{ 153,  }, { 153,  }, { 153,  }, };// SAO 类型索引static const UChar INIT_SAO_TYPE_IDX[3][NUM_SAO_TYPE_IDX_CTX] = {{ 160, },{ 185, },{ 200, },};// 变换分割标志static const UCharINIT_TRANS_SUBDIV_FLAG[3][NUM_TRANS_SUBDIV_FLAG_CTX] ={{ 224,  167,  122, },{ 124,  138,   94, },{ 153,  138,  138, },};// 变换skip标志static const UCharINIT_TRANSFORMSKIP_FLAG[3][2*NUM_TRANSFORMSKIP_FLAG_CTX] = {{ 139,  139}, { 139,  139}, { 139,  139}, };//! \}

上下文模型在工程中是ContextModel3DBuffer类型，为了弄清楚这个结构，我们先看TEncSbac的构造函数：

TEncSbac::TEncSbac()// new structure here: m_pcBitIf                   ( NULL ), m_pcSlice                   ( NULL ), m_pcBinIf                   ( NULL ), m_uiCoeffCost               ( 0 ), m_numContextModels          ( 0 )//context model的计数值，接下来的所有除了assert的语句都是对句法元素对应的context进行初始化 , m_cCUSplitFlagSCModel       ( 1,             1,               NUM_SPLIT_FLAG_CTX            , m_contextModels + m_numContextModels, m_numContextModels ), m_cCUSkipFlagSCModel        ( 1,             1,               NUM_SKIP_FLAG_CTX             , m_contextModels + m_numContextModels, m_numContextModels), m_cCUMergeFlagExtSCModel    ( 1,             1,               NUM_MERGE_FLAG_EXT_CTX        , m_contextModels + m_numContextModels, m_numContextModels), m_cCUMergeIdxExtSCModel     ( 1,             1,               NUM_MERGE_IDX_EXT_CTX         , m_contextModels + m_numContextModels, m_numContextModels), m_cCUPartSizeSCModel        ( 1,             1,               NUM_PART_SIZE_CTX             , m_contextModels + m_numContextModels, m_numContextModels), m_cCUPredModeSCModel        ( 1,             1,               NUM_PRED_MODE_CTX             , m_contextModels + m_numContextModels, m_numContextModels), m_cCUIntraPredSCModel       ( 1,             1,               NUM_ADI_CTX                   , m_contextModels + m_numContextModels, m_numContextModels), m_cCUChromaPredSCModel      ( 1,             1,               NUM_CHROMA_PRED_CTX           , m_contextModels + m_numContextModels, m_numContextModels), m_cCUDeltaQpSCModel         ( 1,             1,               NUM_DELTA_QP_CTX              , m_contextModels + m_numContextModels, m_numContextModels), m_cCUInterDirSCModel        ( 1,             1,               NUM_INTER_DIR_CTX             , m_contextModels + m_numContextModels, m_numContextModels), m_cCURefPicSCModel          ( 1,             1,               NUM_REF_NO_CTX                , m_contextModels + m_numContextModels, m_numContextModels), m_cCUMvdSCModel             ( 1,             1,               NUM_MV_RES_CTX                , m_contextModels + m_numContextModels, m_numContextModels), m_cCUQtCbfSCModel           ( 1,             2,               NUM_QT_CBF_CTX                , m_contextModels + m_numContextModels, m_numContextModels), m_cCUTransSubdivFlagSCModel ( 1,             1,               NUM_TRANS_SUBDIV_FLAG_CTX     , m_contextModels + m_numContextModels, m_numContextModels), m_cCUQtRootCbfSCModel       ( 1,             1,               NUM_QT_ROOT_CBF_CTX           , m_contextModels + m_numContextModels, m_numContextModels), m_cCUSigCoeffGroupSCModel   ( 1,             2,               NUM_SIG_CG_FLAG_CTX           , m_contextModels + m_numContextModels, m_numContextModels), m_cCUSigSCModel             ( 1,             1,               NUM_SIG_FLAG_CTX              , m_contextModels + m_numContextModels, m_numContextModels), m_cCuCtxLastX               ( 1,             2,               NUM_CTX_LAST_FLAG_XY          , m_contextModels + m_numContextModels, m_numContextModels), m_cCuCtxLastY               ( 1,             2,               NUM_CTX_LAST_FLAG_XY          , m_contextModels + m_numContextModels, m_numContextModels), m_cCUOneSCModel             ( 1,             1,               NUM_ONE_FLAG_CTX              , m_contextModels + m_numContextModels, m_numContextModels), m_cCUAbsSCModel             ( 1,             1,               NUM_ABS_FLAG_CTX              , m_contextModels + m_numContextModels, m_numContextModels), m_cMVPIdxSCModel            ( 1,             1,               NUM_MVP_IDX_CTX               , m_contextModels + m_numContextModels, m_numContextModels), m_cSaoMergeSCModel          ( 1,             1,               NUM_SAO_MERGE_FLAG_CTX   , m_contextModels + m_numContextModels, m_numContextModels), m_cSaoTypeIdxSCModel        ( 1,             1,               NUM_SAO_TYPE_IDX_CTX          , m_contextModels + m_numContextModels, m_numContextModels), m_cTransformSkipSCModel     ( 1,             2,               NUM_TRANSFORMSKIP_FLAG_CTX    , m_contextModels + m_numContextModels, m_numContextModels), m_CUTransquantBypassFlagSCModel( 1,          1,               NUM_CU_TRANSQUANT_BYPASS_FLAG_CTX, m_contextModels + m_numContextModels, m_numContextModels){assert( m_numContextModels <= MAX_NUM_CTX_MOD );}

可以看到TEncSbac的构造函数调用的都是ContextModel3DBuffer的构造函数，ContextModel3DBuffer的构造函数定义如下：

ContextModel3DBuffer::ContextModel3DBuffer( UInt uiSizeZ, UInt uiSizeY, UInt uiSizeX, ContextModel *basePtr, Int &count ): m_sizeX  ( uiSizeX ), m_sizeXY ( uiSizeX * uiSizeY ), m_sizeXYZ( uiSizeX * uiSizeY * uiSizeZ ){// allocate 3D bufferm_contextModel = basePtr;//  m_contextModel由basePtr赋值，即指向指定的context的内存区count += m_sizeXYZ;// count记录的是到目前为止所有context的尺寸}

结合ContextModel3DBuffer和TEncSbac的构造函数可以看到，实际的上下文数据都存放在ContextModel中（或者ContextModel类型的指针指向的内存中），ContextModel3DBuffer只是一个便于访问的接口类。

上下文模型内容初始化的函数ContextModel3DBuffer::initBuffer定义如下：

Void ContextModel3DBuffer::initBuffer( SliceType sliceType, Int qp, UChar* ctxModel ){ ctxModel += sliceType * m_sizeXYZ;   // 根据当前slice的类型（I,P,B）选择对应的context，为什么这么做，下面会解释 // 根据sliceType计算initType并将context指针移动到正确的位置上，这个initType用于索引context model，且由slice_type来决定for ( Int n = 0; n < m_sizeXYZ; n++ ){m_contextModel[ n ].init( qp, ctxModel[ n ] );// 完成context的各个状态变量的初始化工作m_contextModel[ n ].setBinsCoded( 0 );}}

Void ContextModel::init( Int qp, Int initValue ){    // 选取中间值qp = Clip3(0, 51, qp);// 上下文模型初始化Int  slope      = (initValue>>4)*5 - 45;// mInt  offset     = ((initValue&15)<<3)-16;// nInt  initState  =  min( max( 1, ( ( ( slope * qp ) >> 4 ) + offset ) ), 126 );// preCtxState  UInt mpState    = (initState >= 64 );// MPS  m_ucState       = ( (mpState? (initState - 64):(63 - initState)) <<1) + mpState;}

熵编码以及上下文模型的更新

在HEVC中，熵编码分为常规编码和旁路编码，常规编码就是我们常见的自适应上下文模型算数编码，旁路编码是一种等概率的算数编码

常规编码

以预测模式（PredMode）这个语法元素的编码为例子：

/*** 编码预测模式*/Void TEncSbac::codePredMode( TComDataCU* pcCU, UInt uiAbsPartIdx ){// get context function is here// 得到预测的模式Int iPredMode = pcCU->getPredictionMode( uiAbsPartIdx );// 进行熵编码，然后上下文更新m_pcBinIf->encodeBin( iPredMode == MODE_INTER ? 0 : 1, m_cCUPredModeSCModel.get( 0, 0, 0 ) );}

核心函数只有一个：

/*** cabac熵编码的核心，编码一个比特位！** 1、把该比特对应的上下文模型设置为已经编码** 2、计算LPS** 3、计算新的范围** 4、根据比特值和MPS是否相等，来更新上下文模式*/Void TEncBinCABAC::encodeBin( UInt binValue, ContextModel &rcCtxModel ){// 调试的打印信息{DTRACE_CABAC_VL( g_nSymbolCounter++ )DTRACE_CABAC_T( "\tstate=" )DTRACE_CABAC_V( ( rcCtxModel.getState() << 1 ) + rcCtxModel.getMps() )DTRACE_CABAC_T( "\tsymbol=" )DTRACE_CABAC_V( binValue )DTRACE_CABAC_T( "\n" )}m_uiBinsCoded += m_binCountIncrement;// 设置已经编码的标志rcCtxModel.setBinsCoded( 1 );// LPSUInt  uiLPS   = TComCABACTables::sm_aucLPSTable[ rcCtxModel.getState() ][ ( m_uiRange >> 6 ) & 3 ];// rangem_uiRange    -= uiLPS;// 判断binValue是否等于MPSif( binValue != rcCtxModel.getMps() )// binVal != valMPS，概率索引值将减小，即LPS的概率增大{Int numBits = TComCABACTables::sm_aucRenormTable[ uiLPS >> 3 ];// RenormE m_uiLow     = ( m_uiLow + m_uiRange ) << numBits;// codILow = codILow + codIRangem_uiRange   = uiLPS << numBits;// codIRange = codIRangeLPS// 更新LPSrcCtxModel.updateLPS();// pStateIdx = transIdxLPS[pStateIdx] // 这是存放剩余比特的地方还是存放编码后数据的地方？？？？？m_bitsLeft -= numBits;  }else// binVal == valMPS，概率索引值将增大，即LPS的概率减小{// 更新MPSrcCtxModel.updateMPS();// pStateIdx = transIdxLPS[pStateIdx] if ( m_uiRange >= 256 ){return;}m_uiLow <<= 1;m_uiRange <<= 1;m_bitsLeft--;}// 尝试写到比特流中，先判断当前缓冲区中的空闲空间是否足够，不足的话就写到比特流中，腾出空间testAndWriteOut();}

旁路编码

盘路编码无需对概率进行自适应更新，而是采用0和1概率各占1/2的固定概率进行编码。以SAO的边带信息编码为例：

Void TEncSbac::codeSaoUflc       ( UInt uiLength, UInt uiCode ){m_pcBinIf->encodeBinsEP ( uiCode, uiLength );}

核心函数有两个：

/*** 等概率熵编码（不是cabac编码）** 某些特殊的地方会用到它** EP即equiprobable，表示等概率*/Void TEncBinCABAC::encodeBinEP( UInt binValue ){{DTRACE_CABAC_VL( g_nSymbolCounter++ )DTRACE_CABAC_T( "\tEPsymbol=" )DTRACE_CABAC_V( binValue )DTRACE_CABAC_T( "\n" )}m_uiBinsCoded += m_binCountIncrement;m_uiLow <<= 1;if( binValue ){m_uiLow += m_uiRange;}m_bitsLeft--;testAndWriteOut();}/*** 等概率熵编码*/Void TEncBinCABAC::encodeBinsEP( UInt binValues, Int numBins ){m_uiBinsCoded += numBins & -m_binCountIncrement;for ( Int i = 0; i < numBins; i++ ){DTRACE_CABAC_VL( g_nSymbolCounter++ )DTRACE_CABAC_T( "\tEPsymbol=" )DTRACE_CABAC_V( ( binValues >> ( numBins - 1 - i ) ) & 1 )DTRACE_CABAC_T( "\n" )}while ( numBins > 8 ){numBins -= 8;UInt pattern = binValues >> numBins; m_uiLow <<= 8;m_uiLow += m_uiRange * pattern;binValues -= pattern << numBins;m_bitsLeft -= 8;testAndWriteOut();}m_uiLow <<= numBins;m_uiLow += m_uiRange * binValues;m_bitsLeft -= numBins;testAndWriteOut();}

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