博客
关于我
强烈建议你试试无所不能的chatGPT,快点击我
x265-1.7版本-common/quant.cpp注释
阅读量:2189 次
发布时间:2019-05-02

本文共 62197 字,大约阅读时间需要 207 分钟。

注:问号以及未注释部分 会在x265-1.8版本内更新 

/***************************************************************************** * Copyright (C) 2015 x265 project * * Authors: Steve Borho 
* * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02111, USA. * * This program is also available under a commercial proprietary license. * For more information, contact us at license @ x265.com. *****************************************************************************/#include "common.h"#include "primitives.h"#include "quant.h"#include "framedata.h"#include "entropy.h"#include "yuv.h"#include "cudata.h"#include "contexts.h"using namespace x265;#define SIGN(x,y) ((x^(y >> 31))-(y >> 31))namespace {struct coeffGroupRDStats{ int nnzBeforePos0; /* indicates coeff other than pos 0 are coded */ int64_t codedLevelAndDist; /* distortion and level cost of coded coefficients */ int64_t uncodedDist; /* uncoded distortion cost of coded coefficients */ int64_t sigCost; /* cost of signaling significant coeff bitmap */ int64_t sigCost0; /* cost of signaling sig coeff bit of coeff 0 */};inline int fastMin(int x, int y){ return y + ((x - y) & ((x - y) >> (sizeof(int) * CHAR_BIT - 1))); // min(x, y)}inline int getICRate(uint32_t absLevel, int32_t diffLevel, const int* greaterOneBits, const int* levelAbsBits, const uint32_t absGoRice, const uint32_t maxVlc, uint32_t c1c2Idx){ X265_CHECK(c1c2Idx <= 3, "c1c2Idx check failure\n"); X265_CHECK(absGoRice <= 4, "absGoRice check failure\n"); if (!absLevel) { X265_CHECK(diffLevel < 0, "diffLevel check failure\n"); return 0; } int rate = 0; if (diffLevel < 0) { X265_CHECK(absLevel <= 2, "absLevel check failure\n"); rate += greaterOneBits[(absLevel == 2)]; if (absLevel == 2) rate += levelAbsBits[0]; } else { uint32_t symbol = diffLevel; bool expGolomb = (symbol > maxVlc); if (expGolomb) { absLevel = symbol - maxVlc; // NOTE: mapping to x86 hardware instruction BSR unsigned long size; CLZ(size, absLevel); int egs = size * 2 + 1; rate += egs << 15; // NOTE: in here, expGolomb=true means (symbol >= maxVlc + 1) X265_CHECK(fastMin(symbol, (maxVlc + 1)) == (int)maxVlc + 1, "min check failure\n"); symbol = maxVlc + 1; } uint32_t prefLen = (symbol >> absGoRice) + 1; uint32_t numBins = fastMin(prefLen + absGoRice, 8 /* g_goRicePrefixLen[absGoRice] + absGoRice */); rate += numBins << 15; if (c1c2Idx & 1) rate += greaterOneBits[1]; if (c1c2Idx == 3) rate += levelAbsBits[1]; } return rate;}#if CHECKED_BUILD || _DEBUGinline int getICRateNegDiff(uint32_t absLevel, const int* greaterOneBits, const int* levelAbsBits){ X265_CHECK(absLevel <= 2, "absLevel check failure\n"); int rate; if (absLevel == 0) rate = 0; else if (absLevel == 2) rate = greaterOneBits[1] + levelAbsBits[0]; else rate = greaterOneBits[0]; return rate;}#endifinline int getICRateLessVlc(uint32_t absLevel, int32_t diffLevel, const uint32_t absGoRice){ X265_CHECK(absGoRice <= 4, "absGoRice check failure\n"); if (!absLevel) { X265_CHECK(diffLevel < 0, "diffLevel check failure\n"); return 0; } int rate; uint32_t symbol = diffLevel; uint32_t prefLen = (symbol >> absGoRice) + 1; uint32_t numBins = fastMin(prefLen + absGoRice, 8 /* g_goRicePrefixLen[absGoRice] + absGoRice */); rate = numBins << 15; return rate;}/* Calculates the cost for specific absolute transform level */inline uint32_t getICRateCost(uint32_t absLevel, int32_t diffLevel, const int* greaterOneBits, const int* levelAbsBits, uint32_t absGoRice, uint32_t c1c2Idx){ X265_CHECK(absLevel, "absLevel should not be zero\n"); if (diffLevel < 0) { X265_CHECK((absLevel == 1) || (absLevel == 2), "absLevel range check failure\n"); uint32_t rate = greaterOneBits[(absLevel == 2)]; if (absLevel == 2) rate += levelAbsBits[0]; return rate; } else { uint32_t rate; uint32_t symbol = diffLevel; if ((symbol >> absGoRice) < COEF_REMAIN_BIN_REDUCTION) { uint32_t length = symbol >> absGoRice; rate = (length + 1 + absGoRice) << 15; } else { uint32_t length = 0; symbol = (symbol >> absGoRice) - COEF_REMAIN_BIN_REDUCTION; if (symbol) { unsigned long idx; CLZ(idx, symbol + 1); length = idx; } rate = (COEF_REMAIN_BIN_REDUCTION + length + absGoRice + 1 + length) << 15; } if (c1c2Idx & 1) rate += greaterOneBits[1]; if (c1c2Idx == 3) rate += levelAbsBits[1]; return rate; }}}Quant::Quant(){ m_resiDctCoeff = NULL; m_fencDctCoeff = NULL; m_fencShortBuf = NULL; m_frameNr = NULL; m_nr = NULL;}bool Quant::init(int rdoqLevel, double psyScale, const ScalingList& scalingList, Entropy& entropy){ m_entropyCoder = &entropy; // 初始化熵编码器 m_rdoqLevel = rdoqLevel; // 初始化rdoq级别 m_psyRdoqScale = (int32_t)(psyScale * 256.0); // 针对RDOQ的心理视觉优化 X265_CHECK((psyScale * 256.0) < (double)MAX_INT, "psyScale value too large\n"); m_scalingList = &scalingList; m_resiDctCoeff = X265_MALLOC(int16_t, MAX_TR_SIZE * MAX_TR_SIZE * 2); m_fencDctCoeff = m_resiDctCoeff + (MAX_TR_SIZE * MAX_TR_SIZE); m_fencShortBuf = X265_MALLOC(int16_t, MAX_TR_SIZE * MAX_TR_SIZE); m_tqBypass = false; return m_resiDctCoeff && m_fencShortBuf;}bool Quant::allocNoiseReduction(const x265_param& param){ m_frameNr = X265_MALLOC(NoiseReduction, param.frameNumThreads); if (m_frameNr) memset(m_frameNr, 0, sizeof(NoiseReduction) * param.frameNumThreads); else return false; return true;}Quant::~Quant(){ X265_FREE(m_frameNr); X265_FREE(m_resiDctCoeff); X265_FREE(m_fencShortBuf);}void Quant::setQPforQuant(const CUData& ctu, int qp){ m_tqBypass = !!ctu.m_tqBypass[0]; if (m_tqBypass) return; m_nr = m_frameNr ? &m_frameNr[ctu.m_encData->m_frameEncoderID] : NULL; m_qpParam[TEXT_LUMA].setQpParam(qp + QP_BD_OFFSET); setChromaQP(qp + ctu.m_slice->m_pps->chromaQpOffset[0], TEXT_CHROMA_U, ctu.m_chromaFormat); setChromaQP(qp + ctu.m_slice->m_pps->chromaQpOffset[1], TEXT_CHROMA_V, ctu.m_chromaFormat);}void Quant::setChromaQP(int qpin, TextType ttype, int chFmt){ int qp = x265_clip3(-QP_BD_OFFSET, 57, qpin); if (qp >= 30) { if (chFmt == X265_CSP_I420) qp = g_chromaScale[qp]; else qp = X265_MIN(qp, QP_MAX_SPEC); } m_qpParam[ttype].setQpParam(qp + QP_BD_OFFSET);}/* To minimize the distortion only. No rate is considered */uint32_t Quant::signBitHidingHDQ(int16_t* coeff, int32_t* deltaU, uint32_t numSig, const TUEntropyCodingParameters &codeParams){ const uint32_t log2TrSizeCG = codeParams.log2TrSizeCG; const uint16_t* scan = codeParams.scan; bool lastCG = true; for (int cg = (1 << (log2TrSizeCG * 2)) - 1; cg >= 0; cg--) { int cgStartPos = cg << LOG2_SCAN_SET_SIZE; int n; for (n = SCAN_SET_SIZE - 1; n >= 0; --n) if (coeff[scan[n + cgStartPos]]) break; if (n < 0) continue; int lastNZPosInCG = n; for (n = 0;; n++) if (coeff[scan[n + cgStartPos]]) break; int firstNZPosInCG = n; if (lastNZPosInCG - firstNZPosInCG >= SBH_THRESHOLD) { uint32_t signbit = coeff[scan[cgStartPos + firstNZPosInCG]] > 0 ? 0 : 1; uint32_t absSum = 0; for (n = firstNZPosInCG; n <= lastNZPosInCG; n++) absSum += coeff[scan[n + cgStartPos]]; if (signbit != (absSum & 0x1)) // compare signbit with sum_parity { int minCostInc = MAX_INT, minPos = -1, curCost = MAX_INT; int16_t finalChange = 0, curChange = 0; for (n = (lastCG ? lastNZPosInCG : SCAN_SET_SIZE - 1); n >= 0; --n) { uint32_t blkPos = scan[n + cgStartPos]; if (coeff[blkPos]) { if (deltaU[blkPos] > 0) { curCost = -deltaU[blkPos]; curChange = 1; } else { if (n == firstNZPosInCG && abs(coeff[blkPos]) == 1) curCost = MAX_INT; else { curCost = deltaU[blkPos]; curChange = -1; } } } else { if (n < firstNZPosInCG) { uint32_t thisSignBit = m_resiDctCoeff[blkPos] >= 0 ? 0 : 1; if (thisSignBit != signbit) curCost = MAX_INT; else { curCost = -deltaU[blkPos]; curChange = 1; } } else { curCost = -deltaU[blkPos]; curChange = 1; } } if (curCost < minCostInc) { minCostInc = curCost; finalChange = curChange; minPos = blkPos; } } /* do not allow change to violate coeff clamp */ if (coeff[minPos] == 32767 || coeff[minPos] == -32768) finalChange = -1; if (!coeff[minPos]) numSig++; else if (finalChange == -1 && abs(coeff[minPos]) == 1) numSig--; if (m_resiDctCoeff[minPos] >= 0) coeff[minPos] += finalChange; else coeff[minPos] -= finalChange; } } lastCG = false; } return numSig;}/** 函数功能 : 对残差块进行变换、量化* \参数 cu :CUData对象* \参数 fenc :原始图像* \参数 fencStride :原始图像块的步长* \参数 residual :残差数据* \参数 resiStride :残差数据的步长* \参数 coeff :存储残差经过变换、量化后的系数* \参数 log2TrSize :TU尺寸* \参数 ttype :数据分量类型(亮度/色度)* \参数 absPartIdx :CU地址* \参数 useTransformSkip :是否使用变换跳过模式* \返回 :量化后非零系数的个数**/uint32_t Quant::transformNxN(const CUData& cu, const pixel* fenc, uint32_t fencStride, const int16_t* residual, uint32_t resiStride, coeff_t* coeff, uint32_t log2TrSize, TextType ttype, uint32_t absPartIdx, bool useTransformSkip){ const uint32_t sizeIdx = log2TrSize - 2; if (m_tqBypass) // 如果使用 变换/量化的bypass模式,即跳过变换/量化,则直接将残差块拷贝到变换系数块 { X265_CHECK(log2TrSize >= 2 && log2TrSize <= 5, "Block size mistake!\n"); return primitives.cu[sizeIdx].copy_cnt(coeff, residual, resiStride); // 拷贝残差块到变换系数块coeff,返回非零系数的个数 } bool isLuma = ttype == TEXT_LUMA; bool usePsy = m_psyRdoqScale && isLuma && !useTransformSkip; int transformShift = MAX_TR_DYNAMIC_RANGE - X265_DEPTH - log2TrSize; // Represents scaling through forward transform X265_CHECK((cu.m_slice->m_sps->quadtreeTULog2MaxSize >= log2TrSize), "transform size too large\n"); if (useTransformSkip) // 如果应用"跳过变换"模式,则只需将残差进行相应的移位,无需进行其他操作 {#if X265_DEPTH <= 10 X265_CHECK(transformShift >= 0, "invalid transformShift\n"); primitives.cu[sizeIdx].cpy2Dto1D_shl(m_resiDctCoeff, residual, resiStride, transformShift); // 将残差数据进行左移操作#else if (transformShift >= 0) primitives.cu[sizeIdx].cpy2Dto1D_shl(m_resiDctCoeff, residual, resiStride, transformShift); else primitives.cu[sizeIdx].cpy2Dto1D_shr(m_resiDctCoeff, residual, resiStride, -transformShift);#endif } else // 进行常规变换 { bool isIntra = cu.isIntra(absPartIdx); if (!sizeIdx && isLuma && isIntra) // 如果变换块是4x4(sizeIdx=0),且是亮度块、intra预测模式,则使用4x4的dst变换 primitives.dst4x4(residual, m_resiDctCoeff, resiStride); else // 否则使用dct变换 primitives.cu[sizeIdx].dct(residual, m_resiDctCoeff, resiStride); // 对残差做DCT变换,得到的结果存储在m_resiDctCoeff中 /* NOTE: if RDOQ is disabled globally, psy-rdoq is also disabled, so * there is no risk of performing this DCT unnecessarily */ if (usePsy) { int trSize = 1 << log2TrSize; /* perform DCT on source pixels for psy-rdoq */ primitives.cu[sizeIdx].copy_ps(m_fencShortBuf, trSize, fenc, fencStride); primitives.cu[sizeIdx].dct(m_fencShortBuf, m_fencDctCoeff, trSize); } if (m_nr) { /* denoise is not applied to intra residual, so DST can be ignored */ int cat = sizeIdx + 4 * !isLuma + 8 * !isIntra; int numCoeff = 1 << (log2TrSize * 2); primitives.denoiseDct(m_resiDctCoeff, m_nr->residualSum[cat], m_nr->offsetDenoise[cat], numCoeff); m_nr->count[cat]++; } } if (m_rdoqLevel) // 如果RDOQ的级别大于0,才进行RDOQ量化,否则使用常规(均匀)量化 return rdoQuant(cu, coeff, log2TrSize, ttype, absPartIdx, usePsy); else // 常规量化(均匀量化或非均匀量化) { int deltaU[32 * 32]; // 用于存储量化误差矩阵,在常规量化中,deltaU只用于进行符号位隐藏的操作 int scalingListType = (cu.isIntra(absPartIdx) ? 0 : 3) + ttype; // 根据预测模式和亮度/色度分量得到 前向量化表的类型,用于选择不同的前向量化表 int rem = m_qpParam[ttype].rem; // 得到Qp的余数部分,实际上 rem = Qp%6 int per = m_qpParam[ttype].per; // 得到Qp的倍数部分,实际上 per = Qp/6 const int32_t* quantCoeff = m_scalingList->m_quantCoef[log2TrSize - 2][scalingListType][rem]; // 根据TU的尺寸、前向量化类型和Qp余数部分,选择对应的量化表 int qbits = QUANT_SHIFT + per + transformShift; // 量化右移的位数,由3部分组成:1.量化带来的位数增加 2.Qp/6部分带来的位数增加 3.前向变换所带来的位数增加 int add = (cu.m_slice->m_sliceType == I_SLICE ? 171 : 85) << (qbits - 9); // 量化后右移可能会带来低位上的损失,这里对右移可能带来的损失进行补偿,HEVC规定I_SLICE补偿1/3,其他类型SLICE补偿1/6 // 结合量化公式,I_SLICE中的add实际相当于: add >> qbits = (171 << (qbits-9))>>qbits = 171>>9 = 171/512 = 1/3 // 非I_SLICE中add实际相当于: add >> qbits = (85 << (qbits-9))>>qbits = 85>>9 = 85/512 = 1/6 int numCoeff = 1 << (log2TrSize * 2); // 当前变换块中包含得系数个数 uint32_t numSig = primitives.quant(m_resiDctCoeff, quantCoeff, deltaU, coeff, qbits, add, numCoeff); // 进行常规量化,参看C版本函数 quant_c,返回值为量化后非零系数的个数 if (numSig >= 2 && cu.m_slice->m_pps->bSignHideEnabled) // 假如非零系数的个数大于等于2,并且使能符号位隐藏,则进行符号位隐藏的操作 { TUEntropyCodingParameters codeParams; cu.getTUEntropyCodingParameters(codeParams, absPartIdx, log2TrSize, isLuma); return signBitHidingHDQ(coeff, deltaU, numSig, codeParams); } else return numSig; }}void Quant::invtransformNxN(int16_t* residual, uint32_t resiStride, const coeff_t* coeff, uint32_t log2TrSize, TextType ttype, bool bIntra, bool useTransformSkip, uint32_t numSig){ const uint32_t sizeIdx = log2TrSize - 2; if (m_tqBypass) { primitives.cu[sizeIdx].cpy1Dto2D_shl(residual, coeff, resiStride, 0); return; } // Values need to pass as input parameter in dequant int rem = m_qpParam[ttype].rem; int per = m_qpParam[ttype].per; int transformShift = MAX_TR_DYNAMIC_RANGE - X265_DEPTH - log2TrSize; int shift = QUANT_IQUANT_SHIFT - QUANT_SHIFT - transformShift; int numCoeff = 1 << (log2TrSize * 2); if (m_scalingList->m_bEnabled) { int scalingListType = (bIntra ? 0 : 3) + ttype; const int32_t* dequantCoef = m_scalingList->m_dequantCoef[sizeIdx][scalingListType][rem]; primitives.dequant_scaling(coeff, dequantCoef, m_resiDctCoeff, numCoeff, per, shift); } else { int scale = m_scalingList->s_invQuantScales[rem] << per; primitives.dequant_normal(coeff, m_resiDctCoeff, numCoeff, scale, shift); } if (useTransformSkip) {#if X265_DEPTH <= 10 X265_CHECK(transformShift > 0, "invalid transformShift\n"); primitives.cu[sizeIdx].cpy1Dto2D_shr(residual, m_resiDctCoeff, resiStride, transformShift);#else if (transformShift > 0) primitives.cu[sizeIdx].cpy1Dto2D_shr(residual, m_resiDctCoeff, resiStride, transformShift); else primitives.cu[sizeIdx].cpy1Dto2D_shl(residual, m_resiDctCoeff, resiStride, -transformShift);#endif } else { int useDST = !sizeIdx && ttype == TEXT_LUMA && bIntra; X265_CHECK((int)numSig == primitives.cu[log2TrSize - 2].count_nonzero(coeff), "numSig differ\n"); // DC only if (numSig == 1 && coeff[0] != 0 && !useDST) { const int shift_1st = 7 - 6; const int add_1st = 1 << (shift_1st - 1); const int shift_2nd = 12 - (X265_DEPTH - 8) - 3; const int add_2nd = 1 << (shift_2nd - 1); int dc_val = (((m_resiDctCoeff[0] * (64 >> 6) + add_1st) >> shift_1st) * (64 >> 3) + add_2nd) >> shift_2nd; primitives.cu[sizeIdx].blockfill_s(residual, resiStride, (int16_t)dc_val); return; } if (useDST) primitives.idst4x4(m_resiDctCoeff, residual, resiStride); else primitives.cu[sizeIdx].idct(m_resiDctCoeff, residual, resiStride); }}/* Rate distortion optimized quantization for entropy coding engines using * probability models like CABAC *//** 函数功能 : 使用RDO(率失真优化)技术对变换后的系数进行量化 ** RDOQ主要可以分成三步: ** step1. 对每个系数单独做RDO优化,找到率失真意义上的最优量化值 ** step2. 对每一个系数组(Coefficient Group,下面都缩写为CG)进行优化,试图将整个CG都设置为0 ** step3. 找到最优的最后一个非零系数的位置,尝试从最后一个非零位置开始将量化后的系数设置为0 ** 调用范围 :只在Quant::transformNxN函数中被调用* \参数 cu :CUData对象* \参数 dstCoeff :进行RDOQ量化后的系数(变换后的系数存储在m_resiDctCoeff中)* \参数 log2TrSize :TU尺寸* \参数 ttype :数据分量类型(亮度/色度)* \参数 absPartIdx :CU地址* \参数 usePsy :是否使用心理视觉量化* \返回 :非零系数的个数**/uint32_t Quant::rdoQuant(const CUData& cu, int16_t* dstCoeff, uint32_t log2TrSize, TextType ttype, uint32_t absPartIdx, bool usePsy){ int transformShift = MAX_TR_DYNAMIC_RANGE - X265_DEPTH - log2TrSize; // 前变换的需要的右移位数,需要在量化中完成 /* Represents scaling through forward transform */ int scalingListType = (cu.isIntra(absPartIdx) ? 0 : 3) + ttype; // 根据预测类型(Intra/Inter)和当前分量(Y/U/V)判断list类型 const uint32_t usePsyMask = usePsy ? -1 : 0; // 是否使用心理视觉量化 X265_CHECK(scalingListType < 6, "scaling list type out of range\n"); //scalingListType 最大为6 int rem = m_qpParam[ttype].rem; // 得到Qp的余数部分,=Qp%6 int per = m_qpParam[ttype].per; // 得到Qp的整数部分,=Qp/6 int qbits = QUANT_SHIFT + per + transformShift; // 常规量化中需要右移的位数 /* Right shift of non-RDOQ quantizer level = (coeff*Q + offset)>>q_bits */ int add = (1 << (qbits - 1)); // 常规量化中右移前需要补偿的加数 const int32_t* qCoef = m_scalingList->m_quantCoef[log2TrSize - 2][scalingListType][rem]; // 得到常规量化使用的量化乘数 int numCoeff = 1 << (log2TrSize * 2); // 当前TU中系数的个数 uint32_t numSig = primitives.nquant(m_resiDctCoeff, qCoef, dstCoeff, qbits, add, numCoeff); // 对变换系数进行常规量化,参考C语言版本的函数 nquant_c X265_CHECK((int)numSig == primitives.cu[log2TrSize - 2].count_nonzero(dstCoeff), "numSig differ\n"); // 再次统计量化后的非零系数个数,并判断与常规量化的结果是否一致 if (!numSig) // 如果常规量化后系数为全零,则跳过RDOQ过程(这是RDOQ提前终止的快速算法) return 0; uint32_t trSize = 1 << log2TrSize; // 得到TU大小 int64_t lambda2 = m_qpParam[ttype].lambda2; // 得到RDO中的lambda const int64_t psyScale = ((int64_t)m_psyRdoqScale * m_qpParam[ttype].lambda); // 得到心理视觉量化系数 /* unquant constants for measuring distortion. Scaling list quant coefficients have a (1 << 4) * scale applied that must be removed during unquant. Note that in real dequant there is clipping * at several stages. We skip the clipping for simplicity when measuring RD cost */ const int32_t* unquantScale = m_scalingList->m_dequantCoef[log2TrSize - 2][scalingListType][rem]; // 得到反量化乘数 int unquantShift = QUANT_IQUANT_SHIFT - QUANT_SHIFT - transformShift + (m_scalingList->m_bEnabled ? 4 : 0); // 得到反量化右移位数 int unquantRound = (unquantShift > per) ? 1 << (unquantShift - per - 1) : 0; // 反量化右移时补偿加数 int scaleBits = SCALE_BITS - 2 * transformShift; // #define UNQUANT(lvl) (((lvl) * (unquantScale[blkPos] << per) + unquantRound) >> unquantShift)#define SIGCOST(bits) ((lambda2 * (bits)) >> 8)#define RDCOST(d, bits) ((((int64_t)d * d) << scaleBits) + SIGCOST(bits))#define PSYVALUE(rec) ((psyScale * (rec)) >> (2 * transformShift + 1)) // 以下是系数级别的变量,即每个系数都占用一个存储单元 int64_t costCoeff[32 * 32]; // 每一个系数花费 /* d*d + lambda * bits */ int64_t costUncoded[32 * 32]; // 每一个系数被量化为0的花费(Z型顺序存储) /* d*d + lambda * 0 */ int64_t costSig[32 * 32]; // 每一个系数的是否为0标记(sig_coeff_flag)的花费 /* lambda * bits */ int rateIncUp[32 * 32]; /* signal overhead of increasing level */ int rateIncDown[32 * 32]; /* signal overhead of decreasing level */ int sigRateDelta[32 * 32]; // 将系数量化为0和量化为非0,系数标记(sig_coeff_flag)的花费差异 /* signal difference between zero and non-zero */ // 以下是系数组(CG)级别的变量 int64_t costCoeffGroupSig[MLS_GRP_NUM]; // 每一个系数组CG的花费 /* lambda * bits of group coding cost */ uint64_t sigCoeffGroupFlag64 = 0; // CG的非零标记,每一位代表一个CG,某一位为1代表对应的CG不是全0,反之代表对应的CG为全0 const uint32_t cgSize = (1 << MLS_CG_SIZE); // 一个系数组CG中的系数个数 /* 4x4 num coef = 16 */ bool bIsLuma = ttype == TEXT_LUMA; // 当前是否是亮度分量 /* total rate distortion cost of transform block, as CBF=0 */ int64_t totalUncodedCost = 0; // 当前块都被量化为0时的cost /* Total rate distortion cost of this transform block, counting te distortion of uncoded blocks, * the distortion and signal cost of coded blocks, and the coding cost of significant * coefficient and coefficient group bitmaps */ int64_t totalRdCost = 0; TUEntropyCodingParameters codeParams; cu.getTUEntropyCodingParameters(codeParams, absPartIdx, log2TrSize, bIsLuma); // 得到熵编码参数 const uint32_t cgNum = 1 << (codeParams.log2TrSizeCG * 2); // TU中的系数组CG的个数 const uint32_t cgStride = (trSize >> MLS_CG_LOG2_SIZE); // TU中CG的步长 uint8_t coeffNum[MLS_GRP_NUM]; // 每个CG中的非零系数的个数 // value range[0, 16] uint16_t coeffSign[MLS_GRP_NUM]; // 每个CG中的非零系数的符号 // bit mask map for non-zero coeff sign uint16_t coeffFlag[MLS_GRP_NUM]; // 每个CG中的非零系数的标记 // bit mask map for non-zero coeff#if CHECKED_BUILD || _DEBUG // clean output buffer, the asm version of scanPosLast Never output anything after latest non-zero coeff group memset(coeffNum, 0, sizeof(coeffNum)); memset(coeffSign, 0, sizeof(coeffNum)); // 这里size应该是写错了,应该改为 sizeof(coeffSign),下面也是一样 memset(coeffFlag, 0, sizeof(coeffNum));#endif // 统计每个CG中的非零系数的符号、非零系数的标志(是否是非零系数)、非零系数个数,以及最后一个非零系数的扫描位置(lastScanPos) const int lastScanPos = primitives.scanPosLast(codeParams.scan, dstCoeff, coeffSign, coeffFlag, coeffNum, numSig, g_scan4x4[codeParams.scanType], trSize); // 参看 scanPosLast_c const int cgLastScanPos = (lastScanPos >> LOG2_SCAN_SET_SIZE); // 得到最后一个非零系数的扫描位置(lastScanPos)所对应的系数组CG的位置 // 但是如果 lastScanPos%(2^LOG2_SCAN_SET_SIZE) != 0,即如果最后一个非零系数位置并不能被16整除,这里得到的实际上倒数第二个非零CG的位置 /* TODO: update bit estimates if dirty */ EstBitsSbac& estBitsSbac = m_entropyCoder->m_estBitsSbac; uint32_t scanPos = 0; uint32_t c1 = 1; // process trail all zero Coeff Group /* coefficients after lastNZ have no distortion signal cost */ const int zeroCG = cgNum - 1 - cgLastScanPos; // 得到全零CG的个数 memset(&costCoeff[(cgLastScanPos + 1) << MLS_CG_SIZE], 0, zeroCG * MLS_CG_BLK_SIZE * sizeof(int64_t)); // 将全零CG中的每个系数花费都设置为0 memset(&costSig[(cgLastScanPos + 1) << MLS_CG_SIZE], 0, zeroCG * MLS_CG_BLK_SIZE * sizeof(int64_t)); // 将全零CG中的每个系数的标记花费设置为0 /* sum zero coeff (uncodec) cost */ // TODO: does we need these cost? if (usePsyMask) // 如果使用心理视觉量化 { for (int cgScanPos = cgLastScanPos + 1; cgScanPos < (int)cgNum ; cgScanPos++) { X265_CHECK(coeffNum[cgScanPos] == 0, "count of coeff failure\n"); uint32_t scanPosBase = (cgScanPos << MLS_CG_SIZE); uint32_t blkPos = codeParams.scan[scanPosBase]; // TODO: we can't SIMD optimize because PSYVALUE need 64-bits multiplication, convert to Double can work faster by FMA for (int y = 0; y < MLS_CG_SIZE; y++) { for (int x = 0; x < MLS_CG_SIZE; x++) { int signCoef = m_resiDctCoeff[blkPos + x]; // 得到变换系数 /* pre-quantization DCT coeff */ int predictedCoef = m_fencDctCoeff[blkPos + x] - signCoef; /* predicted DCT = source DCT - residual DCT*/ costUncoded[blkPos + x] = ((int64_t)signCoef * signCoef) << scaleBits; // 计算将变换系数量化为0的cost(distortion 部分) /* when no residual coefficient is coded, predicted coef == recon coef */ costUncoded[blkPos + x] -= PSYVALUE(predictedCoef); totalUncodedCost += costUncoded[blkPos + x]; // 累加量化为0的cost totalRdCost += costUncoded[blkPos + x]; // 累加量化为0的cost } blkPos += trSize; } } } else // 不使用心理视觉量化,对量化为全零的CG计算每个系数的cost { // non-psy path for (int cgScanPos = cgLastScanPos + 1; cgScanPos < (int)cgNum ; cgScanPos++) // 遍历每一个全零的CG { X265_CHECK(coeffNum[cgScanPos] == 0, "count of coeff failure\n"); // 再次确认是否该CG中非零系数的个数是0 uint32_t scanPosBase = (cgScanPos << MLS_CG_SIZE); // 得到每个CG的首地址(这里的CG顺序是Z型扫描,而不是按照选择的scan模式扫描) uint32_t blkPos = codeParams.scan[scanPosBase]; // 找到一个CG首地址对应的扫描位置 for (int y = 0; y < MLS_CG_SIZE; y++) // 每个CG内部仍然按照Z型扫描存储 costUncoded,但CG位置是通过选择的扫描顺序得到的 { for (int x = 0; x < MLS_CG_SIZE; x++) { int signCoef = m_resiDctCoeff[blkPos + x]; // 得到变换系数 /* pre-quantization DCT coeff */ costUncoded[blkPos + x] = ((int64_t)signCoef * signCoef) << scaleBits; // 计算将变换系数量化为0的cost(distortion 部分) totalUncodedCost += costUncoded[blkPos + x]; // 累加量化为0的cost totalRdCost += costUncoded[blkPos + x]; // 累加量化为0的cost } blkPos += trSize; } } } static const uint8_t table_cnt[5][SCAN_SET_SIZE] = { // patternSigCtx = 0 { 2, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, }, // patternSigCtx = 1 { 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, }, // patternSigCtx = 2 { 2, 1, 0, 0, 2, 1, 0, 0, 2, 1, 0, 0, 2, 1, 0, 0, }, // patternSigCtx = 3 { 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, }, // 4x4 { 0, 1, 4, 5, 2, 3, 4, 5, 6, 6, 8, 8, 7, 7, 8, 8 } }; /* iterate over coding groups in reverse scan order */ // step1. 对每个系数单独做RDO优化,找到率失真意义上的最优量化值 for (int cgScanPos = cgLastScanPos; cgScanPos >= 0; cgScanPos--) // 从最后一非零CG开始遍历每个CG,从最后一个到第一个 { uint32_t ctxSet = (cgScanPos && bIsLuma) ? 2 : 0; const uint32_t cgBlkPos = codeParams.scanCG[cgScanPos]; // 得到CG的扫描位置 const uint32_t cgPosY = cgBlkPos >> codeParams.log2TrSizeCG; // CG扫描位置的Y坐标 const uint32_t cgPosX = cgBlkPos - (cgPosY << codeParams.log2TrSizeCG); // CG扫描位置的X坐标 const uint64_t cgBlkPosMask = ((uint64_t)1 << cgBlkPos); // 当前CG的位置,使用cgBlkPosMask中1的位置来表示 const int patternSigCtx = calcPatternSigCtx(sigCoeffGroupFlag64, cgPosX, cgPosY, cgBlkPos, cgStride); const int ctxSigOffset = codeParams.firstSignificanceMapContext + (cgScanPos && bIsLuma ? 3 : 0); if (c1 == 0) ctxSet++; c1 = 1; if (cgScanPos && (coeffNum[cgScanPos] == 0)) // 如果当前CG不是第一个CG,并且CG系数为全零,即在最后一个非全零CG可能还会存在全零的CG { // 如果发现全零的CG,处理方法与上文中,最后一个非零CG之后的CG的处理方法相同;但是对这些系数又计算了量化为0和非零时,系数标记的花费,这是为了之后进行step2、step3的优化 // TODO: does we need zero-coeff cost? const uint32_t scanPosBase = (cgScanPos << MLS_CG_SIZE); uint32_t blkPos = codeParams.scan[scanPosBase]; if (usePsyMask) // 如果使用心理视觉量化 { // TODO: we can't SIMD optimize because PSYVALUE need 64-bits multiplication, convert to Double can work faster by FMA for (int y = 0; y < MLS_CG_SIZE; y++) { for (int x = 0; x < MLS_CG_SIZE; x++) { int signCoef = m_resiDctCoeff[blkPos + x]; /* pre-quantization DCT coeff */ int predictedCoef = m_fencDctCoeff[blkPos + x] - signCoef; /* predicted DCT = source DCT - residual DCT*/ costUncoded[blkPos + x] = ((int64_t)signCoef * signCoef) << scaleBits; /* when no residual coefficient is coded, predicted coef == recon coef */ costUncoded[blkPos + x] -= PSYVALUE(predictedCoef); totalUncodedCost += costUncoded[blkPos + x]; totalRdCost += costUncoded[blkPos + x]; const uint32_t scanPosOffset = y * MLS_CG_SIZE + x; const uint32_t ctxSig = table_cnt[patternSigCtx][g_scan4x4[codeParams.scanType][scanPosOffset]] + ctxSigOffset; X265_CHECK(trSize > 4, "trSize check failure\n"); X265_CHECK(ctxSig == getSigCtxInc(patternSigCtx, log2TrSize, trSize, codeParams.scan[scanPosBase + scanPosOffset], bIsLuma, codeParams.firstSignificanceMapContext), "sigCtx check failure\n"); costSig[scanPosBase + scanPosOffset] = SIGCOST(estBitsSbac.significantBits[0][ctxSig]); costCoeff[scanPosBase + scanPosOffset] = costUncoded[blkPos + x]; sigRateDelta[blkPos + x] = estBitsSbac.significantBits[1][ctxSig] - estBitsSbac.significantBits[0][ctxSig]; } blkPos += trSize; } } else // 如果不使用心理视觉量化 { // non-psy path for (int y = 0; y < MLS_CG_SIZE; y++) // 对整个CG进行遍历 { for (int x = 0; x < MLS_CG_SIZE; x++) { int signCoef = m_resiDctCoeff[blkPos + x]; // 得到变换系数 /* pre-quantization DCT coeff */ costUncoded[blkPos + x] = ((int64_t)signCoef * signCoef) << scaleBits; // 计算将变换系数量化为0的cost(distortion 部分) totalUncodedCost += costUncoded[blkPos + x]; // 累加量化为0的cost totalRdCost += costUncoded[blkPos + x]; // 累加量化为0的cost const uint32_t scanPosOffset = y * MLS_CG_SIZE + x; const uint32_t ctxSig = table_cnt[patternSigCtx][g_scan4x4[codeParams.scanType][scanPosOffset]] + ctxSigOffset; X265_CHECK(trSize > 4, "trSize check failure\n"); X265_CHECK(ctxSig == getSigCtxInc(patternSigCtx, log2TrSize, trSize, codeParams.scan[scanPosBase + scanPosOffset], bIsLuma, codeParams.firstSignificanceMapContext), "sigCtx check failure\n"); costSig[scanPosBase + scanPosOffset] = SIGCOST(estBitsSbac.significantBits[0][ctxSig]); // 计算系数标记的cost,实际上全零的CG没有这部分的花费 costCoeff[scanPosBase + scanPosOffset] = costUncoded[blkPos + x]; // 对于全零的CG,每个系数的花费就是量化为0的distortion部分 sigRateDelta[blkPos + x] = estBitsSbac.significantBits[1][ctxSig] - estBitsSbac.significantBits[0][ctxSig]; // 计算系数被量化为0和非0,系数标记的bit花费差异 } blkPos += trSize; } } /* there were no coded coefficients in this coefficient group */ { uint32_t ctxSig = getSigCoeffGroupCtxInc(sigCoeffGroupFlag64, cgPosX, cgPosY, cgBlkPos, cgStride); costCoeffGroupSig[cgScanPos] = SIGCOST(estBitsSbac.significantCoeffGroupBits[ctxSig][0]); // 得到CG的非零标记的cost totalRdCost += costCoeffGroupSig[cgScanPos]; /* add cost of 0 bit in significant CG bitmap */ } continue; } coeffGroupRDStats cgRdStats; memset(&cgRdStats, 0, sizeof(coeffGroupRDStats)); uint32_t subFlagMask = coeffFlag[cgScanPos]; // 得到一个CG内系数的标记 int c2 = 0; uint32_t goRiceParam = 0; uint32_t c1Idx = 0; uint32_t c2Idx = 0; /* iterate over coefficients in each group in reverse scan order */ for (int scanPosinCG = cgSize - 1; scanPosinCG >= 0; scanPosinCG--) // 扫描CG内部的每一个系数 { scanPos = (cgScanPos << MLS_CG_SIZE) + scanPosinCG; // 找到每个系数的Z型扫描顺序 uint32_t blkPos = codeParams.scan[scanPos]; // 找到对应的扫描位置 uint32_t maxAbsLevel = abs(dstCoeff[blkPos]); // 得到常规量化后的值 /* abs(quantized coeff) */ int signCoef = m_resiDctCoeff[blkPos]; // 得到DCT系数 /* pre-quantization DCT coeff */ int predictedCoef = m_fencDctCoeff[blkPos] - signCoef; /* predicted DCT = source DCT - residual DCT*/ /* RDOQ measures distortion as the squared difference between the unquantized coded level * and the original DCT coefficient. The result is shifted scaleBits to account for the * FIX15 nature of the CABAC cost tables minus the forward transform scale */ /* cost of not coding this coefficient (all distortion, no signal bits) */ costUncoded[blkPos] = ((int64_t)signCoef * signCoef) << scaleBits; // 得到量化为0的cost X265_CHECK((!!scanPos ^ !!blkPos) == 0, "failed on (blkPos=0 && scanPos!=0)\n"); // ^表示按位异或,若参加运算的两个二进制位值相同则为0,否则为1。这里是保证scanPos和blkPos同时为0或者同时不为0 if (usePsyMask & scanPos) /* when no residual coefficient is coded, predicted coef == recon coef */ costUncoded[blkPos] -= PSYVALUE(predictedCoef); totalUncodedCost += costUncoded[blkPos]; // 累加量化为0的cost // coefficient level estimation const int* greaterOneBits = estBitsSbac.greaterOneBits[4 * ctxSet + c1]; const uint32_t ctxSig = (blkPos == 0) ? 0 : table_cnt[(trSize == 4) ? 4 : patternSigCtx][g_scan4x4[codeParams.scanType][scanPosinCG]] + ctxSigOffset; X265_CHECK(ctxSig == getSigCtxInc(patternSigCtx, log2TrSize, trSize, blkPos, bIsLuma, codeParams.firstSignificanceMapContext), "sigCtx check failure\n"); // before find lastest non-zero coeff if (scanPos > (uint32_t)lastScanPos) // 如果当前扫描位置在最后一个非零系数之后,则将这些系数的cost都设置为0 { // 这种情况出现是由于最后一个非零系数可能出现在最后一个非零CG中的任何位置,所以对最后一个CG扫描就会出现这种情况 /* coefficients after lastNZ have no distortion signal cost */ costCoeff[scanPos] = 0; costSig[scanPos] = 0; /* No non-zero coefficient yet found, but this does not mean * there is no uncoded-cost for this coefficient. Pre- * quantization the coefficient may have been non-zero */ totalRdCost += costUncoded[blkPos]; } else if (!(subFlagMask & 1)) // 如果当前位置位于最后一个非零系数之前,并且该系数为0,计算量化为0的cost { // fast zero coeff path /* set default costs to uncoded costs */ costSig[scanPos] = SIGCOST(estBitsSbac.significantBits[0][ctxSig]); // 计算系数标记sig_coeff_flag为0的cost costCoeff[scanPos] = costUncoded[blkPos] + costSig[scanPos]; // 得到一个系数的总花费, = 量化为0的distortion + 系数标记为0的cost sigRateDelta[blkPos] = estBitsSbac.significantBits[1][ctxSig] - estBitsSbac.significantBits[0][ctxSig]; // 将系数量化为0和量化为非0,系数标记(sig_coeff_flag)的花费差异 totalRdCost += costCoeff[scanPos]; // 累加系数总花费 rateIncUp[blkPos] = greaterOneBits[0]; // 得到比当前量化系数大1所需要花费的bit ??? subFlagMask >>= 1; // 系数非零标记右移移位,下次取到CG中前一个系数的非零标记 } else // 如果当前位置位于最后一个非零系数之前,并且该系数为1,估计每一个系数的最优量化值 { subFlagMask >>= 1; // 系数非零标记右移移位,下次取到CG中前一个系数的非零标记 const uint32_t c1c2Idx = ((c1Idx - 8) >> (sizeof(int) * CHAR_BIT - 1)) + (((-(int)c2Idx) >> (sizeof(int) * CHAR_BIT - 1)) + 1) * 2; const uint32_t baseLevel = ((uint32_t)0xD9 >> (c1c2Idx * 2)) & 3; // {1, 2, 1, 3} X265_CHECK(!!((int)c1Idx < C1FLAG_NUMBER) == (int)((c1Idx - 8) >> (sizeof(int) * CHAR_BIT - 1)), "scan validation 1\n"); X265_CHECK(!!(c2Idx == 0) == ((-(int)c2Idx) >> (sizeof(int) * CHAR_BIT - 1)) + 1, "scan validation 2\n"); X265_CHECK((int)baseLevel == ((c1Idx < C1FLAG_NUMBER) ? (2 + (c2Idx == 0)) : 1), "scan validation 3\n"); // coefficient level estimation const int* levelAbsBits = estBitsSbac.levelAbsBits[ctxSet + c2]; uint32_t level = 0; uint32_t sigCoefBits = 0; costCoeff[scanPos] = MAX_INT64; if ((int)scanPos == lastScanPos) // 如果当前位置是最后一个非零系数的位置,则将量化为0和非0的系数标记(sig_coeff_flag)的花费差异设为0 sigRateDelta[blkPos] = 0; else { if (maxAbsLevel < 3) // 如果该系数的常规量化值很小,则尝试将它量化为0 { /* set default costs to uncoded costs */ costSig[scanPos] = SIGCOST(estBitsSbac.significantBits[0][ctxSig]); // 计算系数标记sig_coeff_flag为0的cost costCoeff[scanPos] = costUncoded[blkPos] + costSig[scanPos]; // 得到一个系数的总花费, = 量化为0的distortion + 系数标记为0的cost } sigRateDelta[blkPos] = estBitsSbac.significantBits[1][ctxSig] - estBitsSbac.significantBits[0][ctxSig]; // 将系数量化为0和量化为非0,系数标记(sig_coeff_flag)的花费差异 sigCoefBits = estBitsSbac.significantBits[1][ctxSig]; // 将系数量化为非0,系数标记(sig_coeff_flag)的花费 } // NOTE: X265_MAX(maxAbsLevel - 1, 1) ==> (X>=2 -> X-1), (X<2 -> 1) | (0 < X < 2 ==> X=1) if (maxAbsLevel == 1) // 如果常规量化值为1,则与量化为0相比较 { uint32_t levelBits = (c1c2Idx & 1) ? greaterOneBits[0] + IEP_RATE : ((1 + goRiceParam) << 15) + IEP_RATE; // 计算量化为1时,系数幅值的bit消耗 X265_CHECK(levelBits == getICRateCost(1, 1 - baseLevel, greaterOneBits, levelAbsBits, goRiceParam, c1c2Idx) + IEP_RATE, "levelBits mistake\n"); int unquantAbsLevel = UNQUANT(1); // 得到1的反量化值 int d = abs(signCoef) - unquantAbsLevel; // 得到distortion int64_t curCost = RDCOST(d, sigCoefBits + levelBits); // 计算量化为1的RDcost /* Psy RDOQ: bias in favor of higher AC coefficients in the reconstructed frame */ if (usePsyMask & scanPos) { int reconCoef = abs(unquantAbsLevel + SIGN(predictedCoef, signCoef)); curCost -= PSYVALUE(reconCoef); } if (curCost < costCoeff[scanPos]) // 将量化为1的cost与量化为0相比较,如果量化为1更好,则将其量化为1 { level = 1; // 更新量化值为1 costCoeff[scanPos] = curCost; // 更新量化当前系数的cost costSig[scanPos] = SIGCOST(sigCoefBits); // 更新sig_coeff_flag的cost } } else if (maxAbsLevel) // 如果常规量化值大于1 { // 计算量化为当前常规量化值所花费的比特数,并计算量化为常规量化值减1所花费的比特数 uint32_t levelBits0 = getICRateCost(maxAbsLevel, maxAbsLevel - baseLevel, greaterOneBits, levelAbsBits, goRiceParam, c1c2Idx) + IEP_RATE; uint32_t levelBits1 = getICRateCost(maxAbsLevel - 1, maxAbsLevel - 1 - baseLevel, greaterOneBits, levelAbsBits, goRiceParam, c1c2Idx) + IEP_RATE; // 计算量化为当前常规量化值所产生的cost int unquantAbsLevel0 = UNQUANT(maxAbsLevel); // 得到反量化值 int d0 = abs(signCoef) - unquantAbsLevel0; // 得到distortion int64_t curCost0 = RDCOST(d0, sigCoefBits + levelBits0); // 计算得到RDcost // 计算量化为当前常规量化值减1所产生的cost int unquantAbsLevel1 = UNQUANT(maxAbsLevel - 1); // 得到反量化值 int d1 = abs(signCoef) - unquantAbsLevel1; // 得到distortion int64_t curCost1 = RDCOST(d1, sigCoefBits + levelBits1); // 计算得到RDcost /* Psy RDOQ: bias in favor of higher AC coefficients in the reconstructed frame */ if (usePsyMask & scanPos) { int reconCoef; reconCoef = abs(unquantAbsLevel0 + SIGN(predictedCoef, signCoef)); curCost0 -= PSYVALUE(reconCoef); reconCoef = abs(unquantAbsLevel1 + SIGN(predictedCoef, signCoef)); curCost1 -= PSYVALUE(reconCoef); } if (curCost0 < costCoeff[scanPos]) // 如果量化为当前常规量化值的cost更小,则更新量化值和cost { level = maxAbsLevel; // 更新量化值为1 costCoeff[scanPos] = curCost0; // 更新量化当前系数的cost costSig[scanPos] = SIGCOST(sigCoefBits); // 更新sig_coeff_flag的cost } if (curCost1 < costCoeff[scanPos]) // 如果量化为当前常规量化值减1的cost更小,则更新量化值和cost { level = maxAbsLevel - 1; // 更新量化值为1 costCoeff[scanPos] = curCost1; // 更新量化当前系数的cost costSig[scanPos] = SIGCOST(sigCoefBits); // 更新sig_coeff_flag的cost } } dstCoeff[blkPos] = (int16_t)level; // 在对当前量化系数进行完RDO优化后,更新目标量化值 totalRdCost += costCoeff[scanPos]; // 累加总的RDcost /* record costs for sign-hiding performed at the end */ if ((cu.m_slice->m_pps->bSignHideEnabled ? ~0 : 0) & level) { const int32_t diff0 = level - 1 - baseLevel; const int32_t diff2 = level + 1 - baseLevel; const int32_t maxVlc = g_goRiceRange[goRiceParam]; int rate0, rate1, rate2; if (diff0 < -2) // prob (92.9, 86.5, 74.5)% { // NOTE: Min: L - 1 - {1,2,1,3} < -2 ==> L < {0,1,0,2} // additional L > 0, so I got (L > 0 && L < 2) ==> L = 1 X265_CHECK(level == 1, "absLevel check failure\n"); const int rateEqual2 = greaterOneBits[1] + levelAbsBits[0];; const int rateNotEqual2 = greaterOneBits[0]; rate0 = 0; rate2 = rateEqual2; rate1 = rateNotEqual2; X265_CHECK(rate1 == getICRateNegDiff(level + 0, greaterOneBits, levelAbsBits), "rate1 check failure!\n"); X265_CHECK(rate2 == getICRateNegDiff(level + 1, greaterOneBits, levelAbsBits), "rate1 check failure!\n"); X265_CHECK(rate0 == getICRateNegDiff(level - 1, greaterOneBits, levelAbsBits), "rate1 check failure!\n"); } else if (diff0 >= 0 && diff2 <= maxVlc) // prob except from above path (98.6, 97.9, 96.9)% { // NOTE: no c1c2 correct rate since all of rate include this factor rate1 = getICRateLessVlc(level + 0, diff0 + 1, goRiceParam); rate2 = getICRateLessVlc(level + 1, diff0 + 2, goRiceParam); rate0 = getICRateLessVlc(level - 1, diff0 + 0, goRiceParam); } else { rate1 = getICRate(level + 0, diff0 + 1, greaterOneBits, levelAbsBits, goRiceParam, maxVlc, c1c2Idx); rate2 = getICRate(level + 1, diff0 + 2, greaterOneBits, levelAbsBits, goRiceParam, maxVlc, c1c2Idx); rate0 = getICRate(level - 1, diff0 + 0, greaterOneBits, levelAbsBits, goRiceParam, maxVlc, c1c2Idx); } rateIncUp[blkPos] = rate2 - rate1; rateIncDown[blkPos] = rate0 - rate1; } else { rateIncUp[blkPos] = greaterOneBits[0]; rateIncDown[blkPos] = 0; } /* Update CABAC estimation state */ if (level >= baseLevel && goRiceParam < 4 && level > (3U << goRiceParam)) goRiceParam++; c1Idx -= (-(int32_t)level) >> 31; /* update bin model */ if (level > 1) { c1 = 0; c2 += (uint32_t)(c2 - 2) >> 31; c2Idx++; } else if ((c1 < 3) && (c1 > 0) && level) c1++; if (dstCoeff[blkPos]) // 如果当前量化系数为非零 { sigCoeffGroupFlag64 |= cgBlkPosMask; // 与当前CG位置的Mask相或,标志该CG不是全0 cgRdStats.codedLevelAndDist += costCoeff[scanPos] - costSig[scanPos]; // cgRdStats.uncodedDist += costUncoded[blkPos]; cgRdStats.nnzBeforePos0 += scanPosinCG; } } cgRdStats.sigCost += costSig[scanPos]; } /* end for (scanPosinCG) */ X265_CHECK((cgScanPos << MLS_CG_SIZE) == (int)scanPos, "scanPos mistake\n"); cgRdStats.sigCost0 = costSig[scanPos]; costCoeffGroupSig[cgScanPos] = 0; /* nothing to do at this case */ X265_CHECK(cgLastScanPos >= 0, "cgLastScanPos check failure\n"); if (!cgScanPos || cgScanPos == cgLastScanPos) { /* coeff group 0 is implied to be present, no signal cost */ /* coeff group with last NZ is implied to be present, handled below */ } else if (sigCoeffGroupFlag64 & cgBlkPosMask) { if (!cgRdStats.nnzBeforePos0) { /* if only coeff 0 in this CG is coded, its significant coeff bit is implied */ totalRdCost -= cgRdStats.sigCost0; cgRdStats.sigCost -= cgRdStats.sigCost0; } /* there are coded coefficients in this group, but now we include the signaling cost * of the significant coefficient group flag and evaluate whether the RD cost of the * coded group is more than the RD cost of the uncoded group */ uint32_t sigCtx = getSigCoeffGroupCtxInc(sigCoeffGroupFlag64, cgPosX, cgPosY, cgBlkPos, cgStride); int64_t costZeroCG = totalRdCost + SIGCOST(estBitsSbac.significantCoeffGroupBits[sigCtx][0]); costZeroCG += cgRdStats.uncodedDist; /* add distortion for resetting non-zero levels to zero levels */ costZeroCG -= cgRdStats.codedLevelAndDist; /* remove distortion and level cost of coded coefficients */ costZeroCG -= cgRdStats.sigCost; /* remove signaling cost of significant coeff bitmap */ costCoeffGroupSig[cgScanPos] = SIGCOST(estBitsSbac.significantCoeffGroupBits[sigCtx][1]); totalRdCost += costCoeffGroupSig[cgScanPos]; /* add the cost of 1 bit in significant CG bitmap */ if (costZeroCG < totalRdCost && m_rdoqLevel > 1) { sigCoeffGroupFlag64 &= ~cgBlkPosMask; totalRdCost = costZeroCG; costCoeffGroupSig[cgScanPos] = SIGCOST(estBitsSbac.significantCoeffGroupBits[sigCtx][0]); /* reset all coeffs to 0. UNCODE THIS COEFF GROUP! */ const uint32_t blkPos = codeParams.scan[cgScanPos * cgSize]; memset(&dstCoeff[blkPos + 0 * trSize], 0, 4 * sizeof(*dstCoeff)); memset(&dstCoeff[blkPos + 1 * trSize], 0, 4 * sizeof(*dstCoeff)); memset(&dstCoeff[blkPos + 2 * trSize], 0, 4 * sizeof(*dstCoeff)); memset(&dstCoeff[blkPos + 3 * trSize], 0, 4 * sizeof(*dstCoeff)); } } else { /* there were no coded coefficients in this coefficient group */ uint32_t ctxSig = getSigCoeffGroupCtxInc(sigCoeffGroupFlag64, cgPosX, cgPosY, cgBlkPos, cgStride); costCoeffGroupSig[cgScanPos] = SIGCOST(estBitsSbac.significantCoeffGroupBits[ctxSig][0]); totalRdCost += costCoeffGroupSig[cgScanPos]; /* add cost of 0 bit in significant CG bitmap */ totalRdCost -= cgRdStats.sigCost; /* remove cost of significant coefficient bitmap */ } } /* end for (cgScanPos) */ X265_CHECK(lastScanPos >= 0, "numSig non zero, but no coded CG\n"); /* calculate RD cost of uncoded block CBF=0, and add cost of CBF=1 to total */ int64_t bestCost; if (!cu.isIntra(absPartIdx) && bIsLuma && !cu.m_tuDepth[absPartIdx]) { bestCost = totalUncodedCost + SIGCOST(estBitsSbac.blockRootCbpBits[0]); totalRdCost += SIGCOST(estBitsSbac.blockRootCbpBits[1]); } else { int ctx = ctxCbf[ttype][cu.m_tuDepth[absPartIdx]]; bestCost = totalUncodedCost + SIGCOST(estBitsSbac.blockCbpBits[ctx][0]); totalRdCost += SIGCOST(estBitsSbac.blockCbpBits[ctx][1]); } /* This loop starts with the last non-zero found in the first loop and then refines this last * non-zero by measuring the true RD cost of the last NZ at this position, and then the RD costs * at all previous coefficients until a coefficient greater than 1 is encountered or we run out * of coefficients to evaluate. This will factor in the cost of coding empty groups and empty * coeff prior to the last NZ. The base best cost is the RD cost of CBF=0 */ int bestLastIdx = 0; bool foundLast = false; for (int cgScanPos = cgLastScanPos; cgScanPos >= 0 && !foundLast; cgScanPos--) { if (!cgScanPos || cgScanPos == cgLastScanPos) { /* the presence of these coefficient groups are inferred, they have no bit in * sigCoeffGroupFlag64 and no saved costCoeffGroupSig[] cost */ } else if (sigCoeffGroupFlag64 & (1ULL << codeParams.scanCG[cgScanPos])) { /* remove cost of significant coeff group flag, the group's presence would be inferred * from lastNZ if it were present in this group */ totalRdCost -= costCoeffGroupSig[cgScanPos]; } else { /* remove cost of signaling this empty group as not present */ totalRdCost -= costCoeffGroupSig[cgScanPos]; continue; } for (int scanPosinCG = cgSize - 1; scanPosinCG >= 0; scanPosinCG--) { scanPos = cgScanPos * cgSize + scanPosinCG; if ((int)scanPos > lastScanPos) continue; /* if the coefficient was coded, measure the RD cost of it as the last non-zero and then * continue as if it were uncoded. If the coefficient was already uncoded, remove the * cost of signaling it as not-significant */ uint32_t blkPos = codeParams.scan[scanPos]; if (dstCoeff[blkPos]) // 如果目标量化系数不是0,则试图将该系数设置为0 { // Calculates the cost of signaling the last significant coefficient in the block uint32_t pos[2] = { (blkPos & (trSize - 1)), (blkPos >> log2TrSize) }; // 得到该系数所在位置的X/Y坐标,X = blkPos&(trSize-1); Y = blkPos>>log2TrSize if (codeParams.scanType == SCAN_VER) // 如果当前的扫描类型是竖直扫描,则调换X和Y坐标 std::swap(pos[0], pos[1]); uint32_t bitsLastNZ = 0; for (int i = 0; i < 2; i++) // 估计该位置的X/Y坐标所需要的bit花费,X=pos[0],Y=pos[1] { int temp = g_lastCoeffTable[pos[i]]; // 得到该系数位置的前缀和后缀 int prefixOnes = temp & 15; int suffixLen = temp >> 4; bitsLastNZ += m_entropyCoder->m_estBitsSbac.lastBits[i][prefixOnes]; // 估计对前缀熵编码所消耗的bits bitsLastNZ += IEP_RATE * suffixLen; // 加上后缀所消耗的bits } int64_t costAsLast = totalRdCost - costSig[scanPos] + SIGCOST(bitsLastNZ); if (costAsLast < bestCost) { bestLastIdx = scanPos + 1; bestCost = costAsLast; } if (dstCoeff[blkPos] > 1 || m_rdoqLevel == 1) { foundLast = true; break; } totalRdCost -= costCoeff[scanPos]; totalRdCost += costUncoded[blkPos]; } else totalRdCost -= costSig[scanPos]; } } /* recount non-zero coefficients and re-apply sign of DCT coef */ numSig = 0; for (int pos = 0; pos < bestLastIdx; pos++) { int blkPos = codeParams.scan[pos]; int level = dstCoeff[blkPos]; numSig += (level != 0); uint32_t mask = (int32_t)m_resiDctCoeff[blkPos] >> 31; dstCoeff[blkPos] = (int16_t)((level ^ mask) - mask); } // Average 49.62 pixels /* clean uncoded coefficients */ for (int pos = bestLastIdx; pos <= fastMin(lastScanPos, (bestLastIdx | (SCAN_SET_SIZE - 1))); pos++) { dstCoeff[codeParams.scan[pos]] = 0; } for (int pos = (bestLastIdx & ~(SCAN_SET_SIZE - 1)) + SCAN_SET_SIZE; pos <= lastScanPos; pos += SCAN_SET_SIZE) { const uint32_t blkPos = codeParams.scan[pos]; memset(&dstCoeff[blkPos + 0 * trSize], 0, 4 * sizeof(*dstCoeff)); memset(&dstCoeff[blkPos + 1 * trSize], 0, 4 * sizeof(*dstCoeff)); memset(&dstCoeff[blkPos + 2 * trSize], 0, 4 * sizeof(*dstCoeff)); memset(&dstCoeff[blkPos + 3 * trSize], 0, 4 * sizeof(*dstCoeff)); } /* rate-distortion based sign-hiding */ if (cu.m_slice->m_pps->bSignHideEnabled && numSig >= 2) { const int realLastScanPos = (bestLastIdx - 1) >> LOG2_SCAN_SET_SIZE; int lastCG = true; for (int subSet = realLastScanPos; subSet >= 0; subSet--) { int subPos = subSet << LOG2_SCAN_SET_SIZE; int n; if (!(sigCoeffGroupFlag64 & (1ULL << codeParams.scanCG[subSet]))) continue; /* measure distance between first and last non-zero coef in this * coding group */ const uint32_t posFirstLast = primitives.findPosFirstLast(&dstCoeff[codeParams.scan[subPos]], trSize, g_scan4x4[codeParams.scanType]); int firstNZPosInCG = (uint16_t)posFirstLast; int lastNZPosInCG = posFirstLast >> 16; if (lastNZPosInCG - firstNZPosInCG >= SBH_THRESHOLD) { uint32_t signbit = (dstCoeff[codeParams.scan[subPos + firstNZPosInCG]] > 0 ? 0 : 1); int absSum = 0; for (n = firstNZPosInCG; n <= lastNZPosInCG; n++) absSum += dstCoeff[codeParams.scan[n + subPos]]; if (signbit != (absSum & 1U)) { /* We must find a coeff to toggle up or down so the sign bit of the first non-zero coeff * is properly implied. Note dstCoeff[] are signed by this point but curChange and * finalChange imply absolute levels (+1 is away from zero, -1 is towards zero) */ int64_t minCostInc = MAX_INT64, curCost = MAX_INT64; int minPos = -1; int16_t finalChange = 0, curChange = 0; for (n = (lastCG ? lastNZPosInCG : SCAN_SET_SIZE - 1); n >= 0; --n) { uint32_t blkPos = codeParams.scan[n + subPos]; int signCoef = m_resiDctCoeff[blkPos]; /* pre-quantization DCT coeff */ int absLevel = abs(dstCoeff[blkPos]); int d = abs(signCoef) - UNQUANT(absLevel); int64_t origDist = (((int64_t)d * d)) << scaleBits;#define DELTARDCOST(d, deltabits) ((((int64_t)d * d) << scaleBits) - origDist + ((lambda2 * (int64_t)(deltabits)) >> 8)) if (dstCoeff[blkPos]) { d = abs(signCoef) - UNQUANT(absLevel + 1); int64_t costUp = DELTARDCOST(d, rateIncUp[blkPos]); /* if decrementing would make the coeff 0, we can include the * significant coeff flag cost savings */ d = abs(signCoef) - UNQUANT(absLevel - 1); bool isOne = abs(dstCoeff[blkPos]) == 1; int downBits = rateIncDown[blkPos] - (isOne ? (IEP_RATE + sigRateDelta[blkPos]) : 0); int64_t costDown = DELTARDCOST(d, downBits); if (lastCG && lastNZPosInCG == n && isOne) costDown -= 4 * IEP_RATE; if (costUp < costDown) { curCost = costUp; curChange = 1; } else { curChange = -1; if (n == firstNZPosInCG && isOne) curCost = MAX_INT64; else curCost = costDown; } } else if (n < firstNZPosInCG && signbit != (signCoef >= 0 ? 0 : 1U)) { /* don't try to make a new coded coeff before the first coeff if its * sign would be different than the first coeff, the inferred sign would * still be wrong and we'd have to do this again. */ curCost = MAX_INT64; } else { /* evaluate changing an uncoded coeff 0 to a coded coeff +/-1 */ d = abs(signCoef) - UNQUANT(1); curCost = DELTARDCOST(d, rateIncUp[blkPos] + IEP_RATE + sigRateDelta[blkPos]); curChange = 1; } if (curCost < minCostInc) { minCostInc = curCost; finalChange = curChange; minPos = blkPos; } } if (dstCoeff[minPos] == 32767 || dstCoeff[minPos] == -32768) /* don't allow sign hiding to violate the SPEC range */ finalChange = -1; if (dstCoeff[minPos] == 0) numSig++; else if (finalChange == -1 && abs(dstCoeff[minPos]) == 1) numSig--; if (m_resiDctCoeff[minPos] >= 0) dstCoeff[minPos] += finalChange; else dstCoeff[minPos] -= finalChange; } } lastCG = false; } } return numSig;}/* Context derivation process of coeff_abs_significant_flag */uint32_t Quant::getSigCtxInc(uint32_t patternSigCtx, uint32_t log2TrSize, uint32_t trSize, uint32_t blkPos, bool bIsLuma, uint32_t firstSignificanceMapContext){ static const uint8_t ctxIndMap[16] = { 0, 1, 4, 5, 2, 3, 4, 5, 6, 6, 8, 8, 7, 7, 8, 8 }; if (!blkPos) // special case for the DC context variable return 0; if (log2TrSize == 2) // 4x4 return ctxIndMap[blkPos]; const uint32_t posY = blkPos >> log2TrSize; const uint32_t posX = blkPos & (trSize - 1); X265_CHECK((blkPos - (posY << log2TrSize)) == posX, "block pos check failed\n"); int posXinSubset = blkPos & 3; X265_CHECK((posX & 3) == (blkPos & 3), "pos alignment fail\n"); int posYinSubset = posY & 3; // NOTE: [patternSigCtx][posXinSubset][posYinSubset] static const uint8_t table_cnt[4][4][4] = { // patternSigCtx = 0 { { 2, 1, 1, 0 }, { 1, 1, 0, 0 }, { 1, 0, 0, 0 }, { 0, 0, 0, 0 }, }, // patternSigCtx = 1 { { 2, 1, 0, 0 }, { 2, 1, 0, 0 }, { 2, 1, 0, 0 }, { 2, 1, 0, 0 }, }, // patternSigCtx = 2 { { 2, 2, 2, 2 }, { 1, 1, 1, 1 }, { 0, 0, 0, 0 }, { 0, 0, 0, 0 }, }, // patternSigCtx = 3 { { 2, 2, 2, 2 }, { 2, 2, 2, 2 }, { 2, 2, 2, 2 }, { 2, 2, 2, 2 }, } }; int cnt = table_cnt[patternSigCtx][posXinSubset][posYinSubset]; int offset = firstSignificanceMapContext; offset += cnt; return (bIsLuma && (posX | posY) >= 4) ? 3 + offset : offset;}

转载地址:http://kouub.baihongyu.com/

你可能感兴趣的文章
attention 机制入门
查看>>
手把手用 IntelliJ IDEA 和 SBT 创建 scala 项目
查看>>
GAN 的 keras 实现
查看>>
AI 在 marketing 上的应用
查看>>
Logistic regression 为什么用 sigmoid ?
查看>>
Logistic Regression 为什么用极大似然函数
查看>>
SVM 的核函数选择和调参
查看>>
LightGBM 如何调参
查看>>
用 TensorFlow.js 在浏览器中训练神经网络
查看>>
cs230 深度学习 Lecture 2 编程作业: Logistic Regression with a Neural Network mindset
查看>>
梯度消失问题与如何选择激活函数
查看>>
为什么需要 Mini-batch 梯度下降,及 TensorFlow 应用举例
查看>>
为什么在优化算法中使用指数加权平均
查看>>
什么是 Q-learning
查看>>
用一个小游戏入门深度强化学习
查看>>
如何应用 BERT :Bidirectional Encoder Representations from Transformers
查看>>
5 分钟入门 Google 最强NLP模型:BERT
查看>>
强化学习第1课:像学自行车一样的强化学习
查看>>
强化学习第2课:强化学习,监督式学习,非监督式学习的区别
查看>>
强化学习第3课:有些问题就像个赌局
查看>>