HEVC-帧内预测2： initAdiPattern 函数

最新推荐文章于 2020-12-29 10:07:33 发布

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最新推荐文章于 2020-12-29 10:07:33 发布

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分类专栏： HEVC 文章标签： HEVC

本文链接：https://blog.youkuaiyun.com/u010854339/article/details/9012435

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本文详细解析HEVC编码标准中的initAdiPattern函数，该函数涉及帧内预测的arbitrary direction intra prediction（Adi）概念。内容引用自博主hevc_cjl的博客，并增添了额外注解，帮助理解HEVC帧内预测的框架及其工作原理。

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主要参考作者hevc_cjl的博客： http://blog.youkuaiyun.com/hevc_cjl/article/details/8184276?reload ，自己添加了更为细致的注解。

* 注：带"1晨不变"的，为原作者的注解；本文作者注解标 "wxl_125"

initAdiPattern 函数中，【Adi = arbitrary direction intra prediction 】这个概念提案中提到的。

贴代码之前先说一下整个帧内预测的框架：

/*
initAdiPattern函数【Adi = arbitrary direction intra prediction  】
（1晨不变）
（1）检测当前PU的相邻样点包括左上、上、右上、左、左下邻域样点值的可用性，或者说检查这些点是否存在；
（2）参考样点的替换过程，主要实现的是JCTVC-J1003即draft 8.4.4.2.2的内容，主要由函数fillReferenceSamples来完成，
     这个在之前的文章已经讨论过了；
（3）相邻样点即参考样点的平滑滤波，主要实现draft 8.4.4.2.3的内容。
*/
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;
  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)【wxl_125】

  Int   iUnitSize = 0;   //变量解释见下面参数赋值【wxl_125】
  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   【wxl_125:统计可以参考的领域块的数目】 
  
  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; // iTotalUnits = Top + RightTop + Left + LeftBottom + LeftTop 
  //                                            = iNumUnitsInCu + iNumUnitsInCu + iNumUnitsInCu + iNumUnitsInCu + 1  

  //  wxl_125:统计可以使用的领域参考块的数目  
  // （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;
  bLeft  = true;

  uiWidth=uiCuWidth2+1;
  uiHeight=uiCuHeight2+1;
  
  if (((uiWidth<<2)>iOrgBufStride)||((uiHeight<<2)>iOrgBufHeight))
  {
    return;
  }
   //! piRoiOrigin指向当前PU左上角
  piRoiOrigin = pcCU->getPic()->getPicYuvRec()->getLumaAddr( pcCU->getAddr(), pcCU->getZorderIdxInCU()+uiZorderIdxInPart );
  piAdiTemp   = piAdiBuf;
  
  //wxl_125: 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);
  
  // ****************************************************
  //! (1晨)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;  // left and left above border + above and above right border + top left corner = length of 3. filter buffer

  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，【原因尚不明白】
 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);
    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)
      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]【wxl_125:首尾直接保存，中间样点值进行3抽头[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++];
  }
}