The selection process of x265 intra prediction mode is shown in the following figure:
The compressIntraCU function performs the operation of recursively dividing the CU. For each divided CU, the CheckIntra function is called to perform the RDO process of the intra prediction mode.
CheckIntra mainly calls estIntraPredQT and estIntraPredChromaQT to select the brightness optimal prediction mode and chroma optimal prediction mode of the current CU, and then calculates the RD Cost used to encode the current CU. The code and comments are as follows:
void Search::checkIntra(Mode& intraMode, const CUGeom& cuGeom, PartSize partSize)
{
CUData& cu = intraMode.cu;
cu.setPartSizeSubParts(partSize);
cu.setPredModeSubParts(MODE_INTRA);
uint32_t tuDepthRange[2];
cu.getIntraTUQtDepthRange(tuDepthRange, 0);
intraMode.initCosts();
// 选出帧内的亮度最佳模式并保存SSE失真
intraMode.lumaDistortion += estIntraPredQT(intraMode, cuGeom, tuDepthRange);
if (m_csp != X265_CSP_I400)
{
intraMode.chromaDistortion += estIntraPredChromaQT(intraMode, cuGeom);//选出最佳的色度模式并返回失真
intraMode.distortion += intraMode.lumaDistortion + intraMode.chromaDistortion;//计算亮度和色度总失真
}
else
intraMode.distortion += intraMode.lumaDistortion;
cu.m_distortion[0] = intraMode.distortion;
m_entropyCoder.resetBits();
if (m_slice->m_pps->bTransquantBypassEnabled)
m_entropyCoder.codeCUTransquantBypassFlag(cu.m_tqBypass[0]);
int skipFlagBits = 0;
if (!m_slice->isIntra())
{
m_entropyCoder.codeSkipFlag(cu, 0);
skipFlagBits = m_entropyCoder.getNumberOfWrittenBits();
m_entropyCoder.codePredMode(cu.m_predMode[0]);
}
m_entropyCoder.codePartSize(cu, 0, cuGeom.depth);
m_entropyCoder.codePredInfo(cu, 0);//编码预测模式
intraMode.mvBits = m_entropyCoder.getNumberOfWrittenBits() - skipFlagBits;
bool bCodeDQP = m_slice->m_pps->bUseDQP;
m_entropyCoder.codeCoeff(cu, 0, bCodeDQP, tuDepthRange); //编码整个CU系数
m_entropyCoder.store(intraMode.contexts);
intraMode.totalBits = m_entropyCoder.getNumberOfWrittenBits();//计算出总的比特
intraMode.coeffBits = intraMode.totalBits - intraMode.mvBits - skipFlagBits;
const Yuv* fencYuv = intraMode.fencYuv;
if (m_rdCost.m_psyRd)
intraMode.psyEnergy = m_rdCost.psyCost(cuGeom.log2CUSize - 2, fencYuv->m_buf[0], fencYuv->m_size, intraMode.reconYuv.m_buf[0], intraMode.reconYuv.m_size);
else if(m_rdCost.m_ssimRd)
intraMode.ssimEnergy = m_quant.ssimDistortion(cu, fencYuv->m_buf[0], fencYuv->m_size, intraMode.reconYuv.m_buf[0], intraMode.reconYuv.m_size, cuGeom.log2CUSize, TEXT_LUMA, 0);
intraMode.resEnergy = primitives.cu[cuGeom.log2CUSize - 2].sse_pp(intraMode.fencYuv->m_buf[0], intraMode.fencYuv->m_size, intraMode.predYuv.m_buf[0], intraMode.predYuv.m_size);
updateModeCost(intraMode);//更新RD Cost
checkDQP(intraMode, cuGeom);
}The estIntraPredQT function is mainly used for RDO selection of the brightness prediction mode. The brightness prediction modes of H.265 include DC, Planar and 33 angle modes, a total of 35 prediction modes.
The main process is as follows:
- Traverse all sub-PUs and select the best prediction mode for each sub-PU. The selection process is divided into the following steps:
- (1) Rough selection, traverse 35 prediction modes, use the SAD of the difference between the predicted pixel and the original pixel as the distortion D, encode the prediction mode bit as R, calculate the SAD Cost, and select the best maxCandCount modes;
- (2) Use the maxCandCount prediction modes selected from the previous step, call codeIntraLumaQT for transformation, quantization, inverse transformation and entropy coding, calculate the RD Cost, and select the best mode (simple RDO) according to the RD Cost, and prohibit TU during transformation. Divided)
- (3) Use the best mode selected in the previous step, perform transformation, quantization, inverse transformation, etc. to calculate the final RD Cost (at this time allow TU division), and save the brightness distortion
- (4) Calculate the sum of distortion of all PUs
- Returns the total distortion
Note: The number of modes selected during rough selection maxCandCount is related to the RDO level and the current division depth:
int maxCandCount = 2 + m_param->rdLevel + ((depth + initTuDepth) >> 1);Where rdLevel is a value between 1 and 6 (including 1 and 6), used to determine the rate-distortion optimization level to be performed during mode and depth decision-making. The more RDO, the higher the compression efficiency, but it takes a lot of effort. Large performance cost, the default value is 3. This parameter can be controlled by the command line parameter -rd <1…6>; depth refers to the division depth of the current CU; initTuDepth indicates whether the current PU type is SIZE_2Nx2N or SIZE_NxN
The code and comments are as follows:
sse_t Search::estIntraPredQT(Mode &intraMode, const CUGeom& cuGeom, const uint32_t depthRange[2])
{
CUData& cu = intraMode.cu;
Yuv* reconYuv = &intraMode.reconYuv;
Yuv* predYuv = &intraMode.predYuv;
const Yuv* fencYuv = intraMode.fencYuv;
uint32_t depth = cuGeom.depth;
//如果当前m_partSize是2Nx2N，表示PU没有进一步划分；如果当前m_partSize是NxN，表示PU进一步划分为4个小PU
uint32_t initTuDepth = cu.m_partSize[0] != SIZE_2Nx2N;
uint32_t numPU = 1 << (2 * initTuDepth);
uint32_t log2TrSize = cuGeom.log2CUSize - initTuDepth;
uint32_t tuSize = 1 << log2TrSize;
uint32_t qNumParts = cuGeom.numPartitions >> 2;
uint32_t sizeIdx = log2TrSize - 2;
uint32_t absPartIdx = 0;
sse_t totalDistortion = 0;
int checkTransformSkip = m_slice->m_pps->bTransformSkipEnabled && !cu.m_tqBypass[0] && cu.m_partSize[0] != SIZE_2Nx2N;
// loop over partitions 循环分区
for (uint32_t puIdx = 0; puIdx < numPU; puIdx++, absPartIdx += qNumParts)
{
uint32_t bmode = 0;
// 选出最佳预测模式
if (intraMode.cu.m_lumaIntraDir[puIdx] != (uint8_t)ALL_IDX)
bmode = intraMode.cu.m_lumaIntraDir[puIdx];
else
{
uint64_t candCostList[MAX_RD_INTRA_MODES];
uint32_t rdModeList[MAX_RD_INTRA_MODES];
uint64_t bcost;
int maxCandCount = 2 + m_param->rdLevel + ((depth + initTuDepth) >> 1);
{
ProfileCUScope(intraMode.cu, intraAnalysisElapsedTime, countIntraAnalysis);
// Reference sample smoothing 参考像素滤波
IntraNeighbors intraNeighbors;
initIntraNeighbors(cu, absPartIdx, initTuDepth, true, &intraNeighbors);
initAdiPattern(cu, cuGeom, absPartIdx, intraNeighbors, ALL_IDX);
// determine set of modes to be tested (using prediction signal only)
// 确定要测试的模式集（仅使用预测信号）
const pixel* fenc = fencYuv->getLumaAddr(absPartIdx);//原始信号
uint32_t stride = predYuv->m_size;
int scaleTuSize = tuSize;
int scaleStride = stride;
int costShift = 0;
m_entropyCoder.loadIntraDirModeLuma(m_rqt[depth].cur);
/* there are three cost tiers for intra modes:帧内模式有三个成本等级：
* pred[0] - mode probable, least cost
* pred[1], pred[2] - less probable, slightly more cost
* non-mpm modes - all cost the same (rbits) */
uint64_t mpms;
uint32_t mpmModes[3];
// 返回发出非最可能模式信号所需的位数。返回时，mpms包含最可能模式的位图
uint32_t rbits = getIntraRemModeBits(cu, absPartIdx, mpmModes, mpms);
pixelcmp_t sa8d = primitives.cu[sizeIdx].sa8d;
uint64_t modeCosts[35];
// DC 计算DC模式的预测值，并根据原始像素和预测像素计算SAD，根据SAD计算RD Cost
primitives.cu[sizeIdx].intra_pred[DC_IDX](m_intraPred, scaleStride, intraNeighbourBuf[0], 0, (scaleTuSize <= 16));
uint32_t bits = (mpms & ((uint64_t)1 << DC_IDX)) ? m_entropyCoder.bitsIntraModeMPM(mpmModes, DC_IDX) : rbits;
uint32_t sad = sa8d(fenc, scaleStride, m_intraPred, scaleStride) << costShift;
modeCosts[DC_IDX] = bcost = m_rdCost.calcRdSADCost(sad, bits);
// PLANAR
pixel* planar = intraNeighbourBuf[0];
if (tuSize >= 8 && tuSize <= 32)
planar = intraNeighbourBuf[1];
primitives.cu[sizeIdx].intra_pred[PLANAR_IDX](m_intraPred, scaleStride, planar, 0, 0);
bits = (mpms & ((uint64_t)1 << PLANAR_IDX)) ? m_entropyCoder.bitsIntraModeMPM(mpmModes, PLANAR_IDX) : rbits;
sad = sa8d(fenc, scaleStride, m_intraPred, scaleStride) << costShift;
modeCosts[PLANAR_IDX] = m_rdCost.calcRdSADCost(sad, bits);
COPY1_IF_LT(bcost, modeCosts[PLANAR_IDX]);
// angular predictions 角度预测模式
if (primitives.cu[sizeIdx].intra_pred_allangs)
{ //遍历全部33种角度模式
primitives.cu[sizeIdx].transpose(m_fencTransposed, fenc, scaleStride);
primitives.cu[sizeIdx].intra_pred_allangs(m_intraPredAngs, intraNeighbourBuf[0], intraNeighbourBuf[1], (scaleTuSize <= 16));
for (int mode = 2; mode < 35; mode++)
{
bits = (mpms & ((uint64_t)1 << mode)) ? m_entropyCoder.bitsIntraModeMPM(mpmModes, mode) : rbits;
if (mode < 18)
sad = sa8d(m_fencTransposed, scaleTuSize, &m_intraPredAngs[(mode - 2) * (scaleTuSize * scaleTuSize)], scaleTuSize) << costShift;
else
sad = sa8d(fenc, scaleStride, &m_intraPredAngs[(mode - 2) * (scaleTuSize * scaleTuSize)], scaleTuSize) << costShift;
modeCosts[mode] = m_rdCost.calcRdSADCost(sad, bits);
COPY1_IF_LT(bcost, modeCosts[mode]);
}
}
else
{
for (int mode = 2; mode < 35; mode++)
{
bits = (mpms & ((uint64_t)1 << mode)) ? m_entropyCoder.bitsIntraModeMPM(mpmModes, mode) : rbits;
int filter = !!(g_intraFilterFlags[mode] & scaleTuSize);
primitives.cu[sizeIdx].intra_pred[mode](m_intraPred, scaleTuSize, intraNeighbourBuf[filter], mode, scaleTuSize <= 16);
sad = sa8d(fenc, scaleStride, m_intraPred, scaleTuSize) << costShift;
modeCosts[mode] = m_rdCost.calcRdSADCost(sad, bits);
COPY1_IF_LT(bcost, modeCosts[mode]);
}
}
/* Find the top maxCandCount candidate modes with cost within 25% of best
* or among the most probable modes. maxCandCount is derived from the
* rdLevel and depth. In general we want to try more modes at slower RD
* levels and at higher depths */
// 找到成本在最佳模式或最可能模式25%以内的最大候选模式。
// maxCandCount是从rdLevel和depth派生的。
// 一般来说，我们想尝试更多的模式在较慢的RD水平和更高的深度
for (int i = 0; i < maxCandCount; i++)
candCostList[i] = MAX_INT64;
uint64_t paddedBcost = bcost + (bcost >> 2); // 1.25%
for (int mode = 0; mode < 35; mode++)
if ((modeCosts[mode] < paddedBcost) || ((uint32_t)mode == mpmModes[0]))
/* choose for R-D analysis only if this mode passes cost threshold or matches MPM[0] */
// 仅当此模式通过成本阈值或与MPM[0]匹配时，才选择进行R-D分析
updateCandList(mode, modeCosts[mode], maxCandCount, rdModeList, candCostList);
}
/* measure best candidates using simple RDO (no TU splits) */
// 使用简单RDO测量最佳候选对象（无TU拆分）
bcost = MAX_INT64;
for (int i = 0; i < maxCandCount; i++)
{
if (candCostList[i] == MAX_INT64)
break;
ProfileCUScope(intraMode.cu, intraRDOElapsedTime[cuGeom.depth], countIntraRDO[cuGeom.depth]);
m_entropyCoder.load(m_rqt[depth].cur);
cu.setLumaIntraDirSubParts(rdModeList[i], absPartIdx, depth + initTuDepth);
Cost icosts;
if (checkTransformSkip)
codeIntraLumaTSkip(intraMode, cuGeom, initTuDepth, absPartIdx, icosts);
else
codeIntraLumaQT(intraMode, cuGeom, initTuDepth, absPartIdx, false, icosts, depthRange);
// 进行变换量化细选
COPY2_IF_LT(bcost, icosts.rdcost, bmode, rdModeList[i]);//更新最佳模式和最佳Cost
}
}
ProfileCUScope(intraMode.cu, intraRDOElapsedTime[cuGeom.depth], countIntraRDO[cuGeom.depth]);
/* remeasure best mode, allowing TU splits */
// 重新测量最佳模式，允许TU划分
cu.setLumaIntraDirSubParts(bmode, absPartIdx, depth + initTuDepth);
m_entropyCoder.load(m_rqt[depth].cur);
Cost icosts;
if (checkTransformSkip)
codeIntraLumaTSkip(intraMode, cuGeom, initTuDepth, absPartIdx, icosts);
else
codeIntraLumaQT(intraMode, cuGeom, initTuDepth, absPartIdx, true, icosts, depthRange);
// 计算所选预测模式的最终失真
totalDistortion += icosts.distortion;
extractIntraResultQT(cu, *reconYuv, initTuDepth, absPartIdx);
// set reconstruction for next intra prediction blocks 为下一预测块准备重建值
if (puIdx != numPU - 1)
{
/* This has important implications for parallelism and RDO. It is writing intermediate results into the
* output recon picture, so it cannot proceed in parallel with anything else when doing INTRA_NXN. Also
* it is not updating m_rdContexts[depth].cur for the later PUs which I suspect is slightly wrong. I think
* that the contexts should be tracked through each PU */
PicYuv* reconPic = m_frame->m_reconPic;
pixel* dst = reconPic->getLumaAddr(cu.m_cuAddr, cuGeom.absPartIdx + absPartIdx);
uint32_t dststride = reconPic->m_stride;
const pixel* src = reconYuv->getLumaAddr(absPartIdx);
uint32_t srcstride = reconYuv->m_size;
primitives.cu[log2TrSize - 2].copy_pp(dst, dststride, src, srcstride);
}
}// for(PU)
if (numPU > 1)
{
uint32_t combCbfY = 0;
for (uint32_t qIdx = 0, qPartIdx = 0; qIdx < 4; ++qIdx, qPartIdx += qNumParts)
combCbfY |= cu.getCbf(qPartIdx, TEXT_LUMA, 1);
cu.m_cbf[0][0] |= combCbfY;
}
// TODO: remove this
m_entropyCoder.load(m_rqt[depth].cur);
return totalDistortion;
}The codeIntraLumaQT function mainly uses the prediction mode passed in from the upper layer to make predictions, and then performs transformation, quantization, inverse transformation, inverse quantization, and reconstruction to calculate RD Cost
Mainly divided into the following steps:
- If the current CU can be further divided, the function is executed recursively to divide until it can no longer be divided
- If the current CU cannot be further divided, call the predIntraLumaAng function for prediction, call the transformNxN function for transformation and quantization, and then call the invtransformNxN function for inverse quantization, inverse transformation, and addition of the predicted value to obtain the reconstructed pixel, and calculate the SSE of the original pixel and the reconstructed pixel As distortion, calculate the bits required for the coding mode and coefficients to calculate RD Cost
The code and comments are as follows:
void Search::codeIntraLumaQT(Mode& mode, const CUGeom& cuGeom, uint32_t tuDepth, uint32_t absPartIdx, bool bAllowSplit, Cost& outCost, const uint32_t depthRange[2])
{
CUData& cu = mode.cu;
uint32_t fullDepth = cuGeom.depth + tuDepth;
uint32_t log2TrSize = cuGeom.log2CUSize - tuDepth;
uint32_t qtLayer = log2TrSize - 2;
uint32_t sizeIdx = log2TrSize - 2;
bool mightNotSplit = log2TrSize <= depthRange[1]; //可能不会划分
// 如果当前变换块尺寸大于最小的变换块尺寸且允许划分，则可能会划分
bool mightSplit = (log2TrSize > depthRange[0]) && (bAllowSplit || !mightNotSplit);
bool bEnableRDOQ = !!m_param->rdoqLevel;
/* If maximum RD penalty, force spits at TU size 32x32 if SPS allows TUs of 16x16 */
// 如果最大RD惩罚，如果SPS允许TUs为16x16，则尺寸为32x32的TU进行划分
if (m_param->rdPenalty == 2 && m_slice->m_sliceType != I_SLICE && log2TrSize == 5 && depthRange[0] <= 4)
{
mightNotSplit = false;
mightSplit = true;
}
Cost fullCost;
uint32_t bCBF = 0;
pixel* reconQt = m_rqt[qtLayer].reconQtYuv.getLumaAddr(absPartIdx);
uint32_t reconQtStride = m_rqt[qtLayer].reconQtYuv.m_size;
if (mightNotSplit)
{
if (mightSplit)
m_entropyCoder.store(m_rqt[fullDepth].rqtRoot);
const pixel* fenc = mode.fencYuv->getLumaAddr(absPartIdx);//原始YUV
pixel* pred = mode.predYuv.getLumaAddr(absPartIdx);//预测YUV
int16_t* residual = m_rqt[cuGeom.depth].tmpResiYuv.getLumaAddr(absPartIdx);//残差
uint32_t stride = mode.fencYuv->m_size;
// init availability pattern
uint32_t lumaPredMode = cu.m_lumaIntraDir[absPartIdx];//初始化帧内模式
IntraNeighbors intraNeighbors;
initIntraNeighbors(cu, absPartIdx, tuDepth, true, &intraNeighbors);
initAdiPattern(cu, cuGeom, absPartIdx, intraNeighbors, lumaPredMode);
// get prediction signal
predIntraLumaAng(lumaPredMode, pred, stride, log2TrSize);//预测，获得预测像素
cu.setTransformSkipSubParts(0, TEXT_LUMA, absPartIdx, fullDepth);
cu.setTUDepthSubParts(tuDepth, absPartIdx, fullDepth);
uint32_t coeffOffsetY = absPartIdx << (LOG2_UNIT_SIZE * 2);
coeff_t* coeffY = m_rqt[qtLayer].coeffRQT[0] + coeffOffsetY;
// store original entropy coding status
if (bEnableRDOQ)
m_entropyCoder.estBit(m_entropyCoder.m_estBitsSbac, log2TrSize, true);
// 计算残差
primitives.cu[sizeIdx].calcresidual[stride % 64 == 0](fenc, pred, residual, stride);
//变换量化
uint32_t numSig = m_quant.transformNxN(cu, fenc, stride, residual, stride, coeffY, log2TrSize, TEXT_LUMA, absPartIdx, false);
if (numSig)
{ //如果存在残差，则进行反变换
m_quant.invtransformNxN(cu, residual, stride, coeffY, log2TrSize, TEXT_LUMA, true, false, numSig);
bool reconQtYuvAlign = m_rqt[qtLayer].reconQtYuv.getAddrOffset(absPartIdx, mode.predYuv.m_size) % 64 == 0;
bool predAlign = mode.predYuv.getAddrOffset(absPartIdx, mode.predYuv.m_size) % 64 == 0;
bool residualAlign = m_rqt[cuGeom.depth].tmpResiYuv.getAddrOffset(absPartIdx, mode.predYuv.m_size) % 64 == 0;
bool bufferAlignCheck = (reconQtStride % 64 == 0) && (stride % 64 == 0) && reconQtYuvAlign && predAlign && residualAlign;
// 获得重建像素
primitives.cu[sizeIdx].add_ps[bufferAlignCheck](reconQt, reconQtStride, pred, residual, stride, stride);
}
else
// no coded residual, recon = pred 没有残差，重建像素等于预测像素
primitives.cu[sizeIdx].copy_pp(reconQt, reconQtStride, pred, stride);
bCBF = !!numSig << tuDepth;
cu.setCbfSubParts(bCBF, TEXT_LUMA, absPartIdx, fullDepth);
// 计算重建像素和原始像素的SSE平方误差和
fullCost.distortion = primitives.cu[sizeIdx].sse_pp(reconQt, reconQtStride, fenc, stride);
m_entropyCoder.resetBits();
// 编码模式
if (!absPartIdx)
{
if (!cu.m_slice->isIntra())
{
if (cu.m_slice->m_pps->bTransquantBypassEnabled)
m_entropyCoder.codeCUTransquantBypassFlag(cu.m_tqBypass[0]);
m_entropyCoder.codeSkipFlag(cu, 0);
m_entropyCoder.codePredMode(cu.m_predMode[0]);
}
m_entropyCoder.codePartSize(cu, 0, cuGeom.depth);
}
if (cu.m_partSize[0] == SIZE_2Nx2N)
{
if (!absPartIdx)
m_entropyCoder.codeIntraDirLumaAng(cu, 0, false);
}
else
{
uint32_t qNumParts = cuGeom.numPartitions >> 2;
if (!tuDepth)
{
for (uint32_t qIdx = 0; qIdx < 4; ++qIdx)
m_entropyCoder.codeIntraDirLumaAng(cu, qIdx * qNumParts, false);
}
else if (!(absPartIdx & (qNumParts - 1)))
m_entropyCoder.codeIntraDirLumaAng(cu, absPartIdx, false);
}
if (log2TrSize != depthRange[0])
m_entropyCoder.codeTransformSubdivFlag(0, 5 - log2TrSize);
m_entropyCoder.codeQtCbfLuma(!!numSig, tuDepth);
//编码系数
if (cu.getCbf(absPartIdx, TEXT_LUMA, tuDepth))
m_entropyCoder.codeCoeffNxN(cu, coeffY, absPartIdx, log2TrSize, TEXT_LUMA);
fullCost.bits = m_entropyCoder.getNumberOfWrittenBits();
if (m_param->rdPenalty && log2TrSize == 5 && m_slice->m_sliceType != I_SLICE)
fullCost.bits *= 4;
//计算 RD Cost
if (m_rdCost.m_psyRd)
{
fullCost.energy = m_rdCost.psyCost(sizeIdx, fenc, mode.fencYuv->m_size, reconQt, reconQtStride);
fullCost.rdcost = m_rdCost.calcPsyRdCost(fullCost.distortion, fullCost.bits, fullCost.energy);
}
else if(m_rdCost.m_ssimRd)
{
fullCost.energy = m_quant.ssimDistortion(cu, fenc, stride, reconQt, reconQtStride, log2TrSize, TEXT_LUMA, absPartIdx);
fullCost.rdcost = m_rdCost.calcSsimRdCost(fullCost.distortion, fullCost.bits, fullCost.energy);
}
else
fullCost.rdcost = m_rdCost.calcRdCost(fullCost.distortion, fullCost.bits);
}
else
fullCost.rdcost = MAX_INT64;
if (mightSplit)
{
if (mightNotSplit)
{
m_entropyCoder.store(m_rqt[fullDepth].rqtTest); // save state after full TU encode
m_entropyCoder.load(m_rqt[fullDepth].rqtRoot); // prep state of split encode
}
/* code split block */
uint32_t qNumParts = 1 << (log2TrSize - 1 - LOG2_UNIT_SIZE) * 2;
int checkTransformSkip = m_slice->m_pps->bTransformSkipEnabled && (log2TrSize - 1) <= MAX_LOG2_TS_SIZE && !cu.m_tqBypass[0];
if (m_param->bEnableTSkipFast)
checkTransformSkip &= cu.m_partSize[0] != SIZE_2Nx2N;
Cost splitCost;
uint32_t cbf = 0;
for (uint32_t qIdx = 0, qPartIdx = absPartIdx; qIdx < 4; ++qIdx, qPartIdx += qNumParts)
{
if (checkTransformSkip)
codeIntraLumaTSkip(mode, cuGeom, tuDepth + 1, qPartIdx, splitCost);
else
codeIntraLumaQT(mode, cuGeom, tuDepth + 1, qPartIdx, bAllowSplit, splitCost, depthRange);
cbf |= cu.getCbf(qPartIdx, TEXT_LUMA, tuDepth + 1);
}
cu.m_cbf[0][absPartIdx] |= (cbf << tuDepth);
if (mightNotSplit && log2TrSize != depthRange[0])
{
/* If we could have coded this TU depth, include cost of subdiv flag */
m_entropyCoder.resetBits();
m_entropyCoder.codeTransformSubdivFlag(1, 5 - log2TrSize);
splitCost.bits += m_entropyCoder.getNumberOfWrittenBits();
if (m_rdCost.m_psyRd)
splitCost.rdcost = m_rdCost.calcPsyRdCost(splitCost.distortion, splitCost.bits, splitCost.energy);
else if(m_rdCost.m_ssimRd)
splitCost.rdcost = m_rdCost.calcSsimRdCost(splitCost.distortion, splitCost.bits, splitCost.energy);
else
splitCost.rdcost = m_rdCost.calcRdCost(splitCost.distortion, splitCost.bits);
}
// 比较划分TU使用的RD Cost和不划分TU使用的RD Cost
if (splitCost.rdcost < fullCost.rdcost)
{
outCost.rdcost += splitCost.rdcost;
outCost.distortion += splitCost.distortion;
outCost.bits += splitCost.bits;
outCost.energy += splitCost.energy;
return;
}
else
{
// recover entropy state of full-size TU encode
m_entropyCoder.load(m_rqt[fullDepth].rqtTest);
// recover transform index and Cbf values
cu.setTUDepthSubParts(tuDepth, absPartIdx, fullDepth);
cu.setCbfSubParts(bCBF, TEXT_LUMA, absPartIdx, fullDepth);
cu.setTransformSkipSubParts(0, TEXT_LUMA, absPartIdx, fullDepth);
}
}
// set reconstruction for next intra prediction blocks if full TU prediction won
// 如果full TU预测获胜，则为下一帧内预测块设置重建
PicYuv* reconPic = m_frame->m_reconPic;
pixel* picReconY = reconPic->getLumaAddr(cu.m_cuAddr, cuGeom.absPartIdx + absPartIdx);
intptr_t picStride = reconPic->m_stride;
primitives.cu[sizeIdx].copy_pp(picReconY, picStride, reconQt, reconQtStride);
outCost.rdcost += fullCost.rdcost;
outCost.distortion += fullCost.distortion;
outCost.bits += fullCost.bits;
outCost.energy += fullCost.energy;
}