帧内预测——initAdiPattern

近期开始接触HEVC，看了一些综述性的文章后开始看平台，可总感觉效果不佳，所以打算把每天看的东西都写一下，巩固巩固。首先是讲一下initAdiPattern这个函数，在看了hevc_cjl的博客：  http://blog.csdn.net/hevc_cjl/article/details/8184276?reload 之后，受益良多。

首先分析下这个函数的用途：这个函数位于帧内预测的亮度预测estIntraPredQT中，主要是用于初始化帧内模式。

细化其功能：（1）检测当前的PU相邻点的可用性（2）参考样点的替换 （3）参考样点的平滑滤波

通过下面这幅图能很好地理解代码的信息。如：

 // ----- 先进行样点值拷贝    Int l = 0;    // left border from bottom to top【左下->左上，滤波前参考样点值拷贝】    for (i = 0; i < uiCuHeight2; i++)    {      piFilterBuf[l++] = piAdiTemp[uiWidth * (uiCuHeight2 - i)];    }    // top left corner    piFilterBuf[l++] = piAdiTemp[0]; //【左上角点， 拷贝】    // above border from left to right    for (i=0; i < uiCuWidth2; i++)  // 【上->右上， 拷贝】    {      piFilterBuf[l++] = piAdiTemp[1 + i];  //l此时初始为uiCuHeight2+1  }  

以上代码为样本点信息拷贝过程，拷贝的过程是自左下而上，过AboveLeft而向右的过程

Void TComPattern::initAdiPattern( TComDataCU* pcCU, UInt uiZorderIdxInPart, UInt uiPartDepth, Int* piAdiBuf, Int iOrgBufStride, Int iOrgBufHeight, Bool& bAbove, Bool& bLeft, Bool bLMmode )  {    Pel*  piRoiOrigin;//piRoiOrgin指向重建Yuv图像对应于当前PU所在位置的首地址    Int*  piAdiTemp;  //    UInt  uiCuWidth   = pcCU->getWidth(0) >> uiPartDepth; //CU宽    UInt  uiCuHeight  = pcCU->getHeight(0)>> uiPartDepth; //CU高    UInt  uiCuWidth2  = uiCuWidth<<1;//  uiCuWidth*2  UInt  uiCuHeight2 = uiCuHeight<<1;    UInt  uiWidth;    UInt  uiHeight;    Int   iPicStride = pcCU->getPic()->getStride(); //图像跨度(原图像宽度 + 两边扩充的参考像素点)     Int   iUnitSize = 0;      Int   iNumUnitsInCu = 0;      Int   iTotalUnits = 0;      Bool  bNeighborFlags[4 * MAX_NUM_SPU_W + 1]; //!< 用于存放4个方向上的相邻样点值的【可用性】，4*32+1    Int   iNumIntraNeighbor = 0;                 ////!< 给可用邻块进行计数          UInt uiPartIdxLT, uiPartIdxRT, uiPartIdxLB;      ////! 获取当前PU左上角LT，右上角RT以及左下角LB 以4x4块为单位的Z-order（Zscan）    pcCU->deriveLeftRightTopIdxAdi( uiPartIdxLT, uiPartIdxRT, uiZorderIdxInPart, uiPartDepth );      pcCU->deriveLeftBottomIdxAdi  ( uiPartIdxLB,              uiZorderIdxInPart, uiPartDepth );        iUnitSize      = g_uiMaxCUWidth >> g_uiMaxCUDepth;  // 4     iNumUnitsInCu  = uiCuWidth / iUnitSize;    // 当前CU的宽/4  = 宽方向上，iUnitSize的个数    iTotalUnits    = (iNumUnitsInCu << 2) + 1; //（iNumUnitsInCu*4） + 1           // （扫描顺序是从左下到左上，再从左上到右上）     bNeighborFlags[iNumUnitsInCu*2] = isAboveLeftAvailable( pcCU, uiPartIdxLT );    iNumIntraNeighbor  += (Int)(bNeighborFlags[iNumUnitsInCu*2]);    iNumIntraNeighbor  += isAboveAvailable     ( pcCU, uiPartIdxLT, uiPartIdxRT, bNeighborFlags+(iNumUnitsInCu*2)+1 );    iNumIntraNeighbor  += isAboveRightAvailable( pcCU, uiPartIdxLT, uiPartIdxRT, bNeighborFlags+(iNumUnitsInCu*3)+1 );    iNumIntraNeighbor  += isLeftAvailable      ( pcCU, uiPartIdxLT, uiPartIdxLB, bNeighborFlags+(iNumUnitsInCu*2)-1 );    iNumIntraNeighbor  += isBelowLeftAvailable ( pcCU, uiPartIdxLT, uiPartIdxLB, bNeighborFlags+ iNumUnitsInCu   -1 );        bAbove = true;  //之前定义为FALSE  bLeft  = true;  //同上    uiWidth=uiCuWidth2+1;    uiHeight=uiCuHeight2+1;        if (((uiWidth<<2)>iOrgBufStride)||((uiHeight<<2)>iOrgBufHeight))  //判定范围  {      return;    }     //! piRoiOrigin指向当前PU左上角    piRoiOrigin = pcCU->getPic()->getPicYuvRec()->getLumaAddr( pcCU->getAddr(), pcCU->getZorderIdxInCU()+uiZorderIdxInPart );    piAdiTemp   = piAdiBuf;        // 参考样点的替换（填补）过程    fillReferenceSamples (g_bitDepthY, piRoiOrigin, piAdiTemp, bNeighborFlags, iNumIntraNeighbor, iUnitSize, iNumUnitsInCu, iTotalUnits, uiCuWidth, uiCuHeight, uiWidth, uiHeight, iPicStride, bLMmode);

// ****************************************************    //! 下面所进行的工作主要是对参考样点进行3抽头的滤波。    //    piAdiBuf指向滤波前的参考样点的首地址，在滤波前，先将所有参考样点拷贝到piFilterBuf指向的区域，    //! 经滤波后的样点值保存在piFilterBufN指向的区域，最终将滤波后的样点值拷贝到piFilterBuf1。      //  值得一提的是，最终的结果是，piAdiBuf指向的区域是未经滤波的样点值，而piFilterBuf1指向的区域是经过    //! 滤波的样点值，两者的地址相差uiWH = uiWidth * uiHeight = (uiCuWidth2 + 1) * (uiCuHeight2 + 1)，这就解释了在进行     //! 真正的帧内预测时，在需要滤波时，指向piAdiBuf的指针需要加上uiWH的偏移量。    Int   i;    // generate filtered intra prediction samples    Int iBufSize = uiCuHeight2 + uiCuWidth2 + 1;  //结合图中信息比较    UInt uiWH = uiWidth * uiHeight;               // number of elements in one buffer      Int* piFilteredBuf1 = piAdiBuf + uiWH;        // 1. filter buffer    Int* piFilteredBuf2 = piFilteredBuf1 + uiWH;  // 2. filter buffer    Int* piFilterBuf = piFilteredBuf2 + uiWH;     // buffer for 2. filtering (sequential)      //piFilterBufN 存放的是参考样点经3抽头滤波后的值    Int* piFilterBufN = piFilterBuf + iBufSize;   // buffer for 1. filtering (sequential)          // ----- 先进行样点值拷贝    Int l = 0;    // left border from bottom to top【左下->左上，滤波前参考样点值拷贝】    for (i = 0; i < uiCuHeight2; i++)    {      piFilterBuf[l++] = piAdiTemp[uiWidth * (uiCuHeight2 - i)];    }    // top left corner    piFilterBuf[l++] = piAdiTemp[0]; //【左上角点， 拷贝】    // above border from left to right    for (i=0; i < uiCuWidth2; i++)  // 【上->右上， 拷贝】    {      piFilterBuf[l++] = piAdiTemp[1 + i];    }       //对32*32的块进行 StrongIntraSmoothing if (pcCU->getSlice()->getSPS()->getUseStrongIntraSmoothing()) //cfg里面设置    {      Int blkSize = 32;      Int bottomLeft = piFilterBuf[0];      Int topLeft = piFilterBuf[uiCuHeight2];      Int topRight = piFilterBuf[iBufSize-1];      Int threshold = 1 << (g_bitDepthY - 5);//8      Bool bilinearLeft = abs(bottomLeft+topLeft-2*piFilterBuf[uiCuHeight]) < threshold;      Bool bilinearAbove  = abs(topLeft+topRight-2*piFilterBuf[uiCuHeight2+uiCuHeight]) < threshold;          if (uiCuWidth>=blkSize && (bilinearLeft && bilinearAbove))      {        Int shift = g_aucConvertToBit[uiCuWidth] + 3;  // log2(uiCuHeight2) =7       piFilterBufN[0] = piFilterBuf[0];        piFilterBufN[uiCuHeight2] = piFilterBuf[uiCuHeight2];        piFilterBufN[iBufSize - 1] = piFilterBuf[iBufSize - 1];        for (i = 1; i < uiCuHeight2; i++)        {          piFilterBufN[i] = ((uiCuHeight2-i)*bottomLeft + i*topLeft + uiCuHeight) >> shift;        }            for (i = 1; i < uiCuWidth2; i++)        {          piFilterBufN[uiCuHeight2 + i] = ((uiCuWidth2-i)*topLeft + i*topRight + uiCuWidth) >> shift;        }      }      else       {        // 1. filtering with [1 2 1]        piFilterBufN[0] = piFilterBuf[0];        piFilterBufN[iBufSize - 1] = piFilterBuf[iBufSize - 1];        for (i = 1; i < iBufSize - 1; i++)        {          piFilterBufN[i] = (piFilterBuf[i - 1] + 2 * piFilterBuf[i]+piFilterBuf[i + 1] + 2) >> 2; // 最后 +2是为了四舍五入取整        }      }    }    else     {      // 1. filtering with [1 2 1]       piFilterBufN[0] = piFilterBuf[0];      piFilterBufN[iBufSize - 1] = piFilterBuf[iBufSize - 1];        for (i = 1; i < iBufSize - 1; i++)      {        piFilterBufN[i] = (piFilterBuf[i - 1] + 2 * piFilterBuf[i]+piFilterBuf[i + 1] + 2) >> 2;      }    }      // fill 1. filter buffer with filtered values    l=0;    for (i = 0; i < uiCuHeight2; i++)    {      piFilteredBuf1[uiWidth * (uiCuHeight2 - i)] = piFilterBufN[l++];    }    piFilteredBuf1[0] = piFilterBufN[l++];    for (i = 0; i < uiCuWidth2; i++)    {      piFilteredBuf1[1 + i] = piFilterBufN[l++];    }  }

	
				
	
					   
帧内预测——initAdiPattern
	  	   
帧内预测-函数initAdiPattern
	  	   
HEVC-帧内预测2：  initAdiPattern 函数
	  	   
帧内预测——fillReferenceSamples
	  	   
HM编码器代码阅读(38)——帧内预测（五）帧内预测之正式的预测操作
	  	   
帧内预测模式预测
	  	   
H.264笔记之三——帧内预测
	  	   
homerHEVC代码阅读（41）——帧内预测
	  	   
详细解析H.264——帧内预测
	  	   
HM编码器代码阅读(35)——帧内预测（二）帧内预测总流程
	  	   
HM编码器代码阅读(39)——帧内预测（六）帧内预测之色度色度的操作
	  	   
帧内预测编码
	  	   
帧内预测
	  	   
帧内预测
	  	   
HEVC帧内预测
	  	   
hevc帧内预测
	  	   
x264帧内预测
	  	   
帧内预测与帧间预测
	     		  
	  	   
C++白盒测试最佳实践课程,3个免费名额火热申请中,31号前截止申请...
	  	   
Android实战技巧：深入解析AsyncTask与thread的区别 THREAD_POOL_EXECUTOR
	  	   
CentOS 6.3 发布 附ISO下载地址
	  	   
问题定位：内存泄漏，踩内存。
	  	   
CSDN的博客积分计算规则及博客排名规则
	  	   
帧内预测——initAdiPattern
	  	   
SQL查询操作
	  	   
Citrix 服务器虚拟化之八 Xenserver虚拟机模版
	  	   
感悟--黄小琥的二十年的坚持
	  	   
hdu 1241 Oil Deposits (dfs)
	  	   
单例模式
	  	   
maven-war-plugin
	  	   
SOJ 4299: component
	  	   
hdu 1856