MatrixIntraPrediction.cpp

/* The copyright in this software is being made available under the BSD
* License, included below. This software may be subject to other third party
* and contributor rights, including patent rights, and no such rights are
* granted under this license.
*
* Copyright (c) 2010-2023, ITU/ISO/IEC
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
*  * Redistributions of source code must retain the above copyright notice,
*    this list of conditions and the following disclaimer.
*  * Redistributions in binary form must reproduce the above copyright notice,
*    this list of conditions and the following disclaimer in the documentation
*    and/or other materials provided with the distribution.
*  * Neither the name of the ITU/ISO/IEC nor the names of its contributors may
*    be used to endorse or promote products derived from this software without
*    specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS
* BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
* THE POSSIBILITY OF SUCH DAMAGE.
*/

/** \file     MatrixIntraPrediction.cpp
\brief    matrix-based intra prediction class
*/

#include "MatrixIntraPrediction.h"
#include "dtrace_next.h"

#include "UnitTools.h"
#include "MipData.h"

MatrixIntraPrediction::MatrixIntraPrediction()
  : m_component(MAX_NUM_COMPONENT)
  , m_reducedBoundary(MIP_MAX_INPUT_SIZE)
  , m_reducedBoundaryTransposed(MIP_MAX_INPUT_SIZE)
  , m_inputOffset(0)
  , m_inputOffsetTransp(0)
  , m_refSamplesTop(MIP_MAX_WIDTH)
  , m_refSamplesLeft(MIP_MAX_HEIGHT)
  , m_blockSize(0, 0)
  , m_sizeId(MipSizeId::S0)
  , m_reducedBdrySize(0)
  , m_reducedPredSize(0)
  , m_upsmpFactorHor(0)
  , m_upsmpFactorVer(0)
{
}

void MatrixIntraPrediction::prepareInputForPred(const CPelBuf &pSrc, const Area &block, const int bitDepth,
                                                const ComponentID compId)
{
  m_component = compId;

  // Step 1: Save block size and calculate dependent values
  initPredBlockParams(block);

  // Step 2: Get the input data (left and top reference samples)
  m_refSamplesTop.resize(block.width);
  for (int x = 0; x < block.width; x++)
  {
    m_refSamplesTop[x] = pSrc.at(x + 1, 0);
  }

  m_refSamplesLeft.resize(block.height);
  for (int y = 0; y < block.height; y++)
  {
    m_refSamplesLeft[y] = pSrc.at(y + 1, 1);
  }

  // Step 3: Compute the reduced boundary via Haar-downsampling (input for the prediction)
  const int inputSize = 2 * m_reducedBdrySize;
  m_reducedBoundary          .resize( inputSize );
  m_reducedBoundaryTransposed.resize( inputSize );

  Pel *const topReduced = m_reducedBoundary.data();
  boundaryDownsampling1D( topReduced, m_refSamplesTop.data(), block.width, m_reducedBdrySize );

  Pel *const leftReduced = m_reducedBoundary.data() + m_reducedBdrySize;
  boundaryDownsampling1D( leftReduced, m_refSamplesLeft.data(), block.height, m_reducedBdrySize );

  Pel *const leftReducedTransposed = m_reducedBoundaryTransposed.data();
  Pel *const topReducedTransposed  = m_reducedBoundaryTransposed.data() + m_reducedBdrySize;
  for( int x = 0; x < m_reducedBdrySize; x++ )
  {
    topReducedTransposed[x] = topReduced[x];
  }
  for( int y = 0; y < m_reducedBdrySize; y++ )
  {
    leftReducedTransposed[y] = leftReduced[y];
  }

  // Step 4: Rebase the reduced boundary

  m_inputOffset       = m_reducedBoundary[0];
  m_inputOffsetTransp = m_reducedBoundaryTransposed[0];

  const bool hasFirstCol = (m_sizeId < MipSizeId::S2);
  m_reducedBoundary          [0] = hasFirstCol ? ((1 << (bitDepth - 1)) - m_inputOffset      ) : 0; // first column of matrix not needed for large blocks
  m_reducedBoundaryTransposed[0] = hasFirstCol ? ((1 << (bitDepth - 1)) - m_inputOffsetTransp) : 0;
  for (int i = 1; i < inputSize; i++)
  {
    m_reducedBoundary          [i] -= m_inputOffset;
    m_reducedBoundaryTransposed[i] -= m_inputOffsetTransp;
  }
}

void MatrixIntraPrediction::predBlock(Pel *const result, const int modeIdx, const bool transpose, const int bitDepth,
                                      const ComponentID compId)
{
  CHECK(m_component != compId, "Boundary has not been prepared for this component.");

  const bool needUpsampling = ( m_upsmpFactorHor > 1 ) || ( m_upsmpFactorVer > 1 );

  const uint8_t* matrix = getMatrixData(modeIdx);

  static_vector<Pel, MIP_MAX_REDUCED_OUTPUT_SAMPLES> bufReducedPred(m_reducedPredSize * m_reducedPredSize);

  Pel *const                                         reducedPred = needUpsampling ? bufReducedPred.data() : result;
  const Pel *const reducedBoundary = transpose ? m_reducedBoundaryTransposed.data() : m_reducedBoundary.data();

  computeReducedPred(reducedPred, reducedBoundary, matrix, transpose, bitDepth);
  if( needUpsampling )
  {
    predictionUpsampling( result, reducedPred );
  }
}

MatrixIntraPrediction::MipSizeId MatrixIntraPrediction::getMipSizeId(const Size &block)
{
  if (block.width == 4 && block.height == 4)
  {
    return MipSizeId::S0;
  }
  else if (block.width == 4 || block.height == 4 || (block.width == 8 && block.height == 8))
  {
    return MipSizeId::S1;
  }
  else
  {
    return MipSizeId::S2;
  }
}

int MatrixIntraPrediction::getNumModesMip(const Size &block)
{
  switch (getMipSizeId(block))
  {
  case MipSizeId::S0:
    return 16;

  case MipSizeId::S1:
    return 8;

  case MipSizeId::S2:
  default:
    return 6;
  }
}

void MatrixIntraPrediction::initPredBlockParams(const Size& block)
{
  m_blockSize = block;
  // init size index
  m_sizeId = getMipSizeId(block);

  // init reduced boundary size
  m_reducedBdrySize = (m_sizeId == MipSizeId::S0) ? 2 : 4;

  // init reduced prediction size
  m_reducedPredSize = (m_sizeId < MipSizeId::S2) ? 4 : 8;

  // init upsampling factors
  m_upsmpFactorHor = m_blockSize.width  / m_reducedPredSize;
  m_upsmpFactorVer = m_blockSize.height / m_reducedPredSize;

  CHECKD( (m_upsmpFactorHor < 1) || ((m_upsmpFactorHor & (m_upsmpFactorHor - 1)) != 0), "Need power of two horizontal upsampling factor." );
  CHECKD( (m_upsmpFactorVer < 1) || ((m_upsmpFactorVer & (m_upsmpFactorVer - 1)) != 0), "Need power of two vertical upsampling factor." );
}

void MatrixIntraPrediction::boundaryDownsampling1D(Pel *reducedDst, const Pel *const fullSrc, const SizeType srcLen,
                                                   const SizeType dstLen)
{
  if (dstLen < srcLen)
  {
    // Create reduced boundary by downsampling
    const SizeType downsmpFactor = srcLen / dstLen;
    const int log2DownsmpFactor = floorLog2(downsmpFactor);
    const int roundingOffset = (1 << (log2DownsmpFactor - 1));

    SizeType srcIdx = 0;
    for( SizeType dstIdx = 0; dstIdx < dstLen; dstIdx++ )
    {
      int sum = 0;
      for( int k = 0; k < downsmpFactor; k++ )
      {
        sum += fullSrc[srcIdx++];
      }
      reducedDst[dstIdx] = (sum + roundingOffset) >> log2DownsmpFactor;
    }
  }
  else
  {
    // Copy boundary if no downsampling is needed
    for (SizeType i = 0; i < dstLen; ++i)
    {
      reducedDst[i] = fullSrc[i];
    }
  }
}

void MatrixIntraPrediction::predictionUpsampling1D(Pel *const dst, const Pel *const src, const Pel *const bndry,
                                                   const SizeType srcSizeUpsmpDim, const SizeType srcSizeOrthDim,
                                                   const SizeType srcStep, const SizeType srcStride,
                                                   const SizeType dstStep, const SizeType dstStride,
                                                   const SizeType bndryStep, const unsigned int upsmpFactor)
{
  const int log2UpsmpFactor = floorLog2( upsmpFactor );
  CHECKD( upsmpFactor <= 1, "Upsampling factor must be at least 2." );
  const int roundingOffset = 1 << (log2UpsmpFactor - 1);

  SizeType idxOrthDim = 0;
  const Pel *srcLine    = src;
  Pel       *dstLine    = dst;
  const Pel *bndryLine  = bndry + bndryStep - 1;
  while( idxOrthDim < srcSizeOrthDim )
  {
    SizeType idxUpsmpDim = 0;
    const Pel *before      = bndryLine;
    const Pel *behind      = srcLine;
    Pel       *currDst     = dstLine;
    while( idxUpsmpDim < srcSizeUpsmpDim )
    {
      SizeType pos = 1;
      int scaledBefore = ( *before ) << log2UpsmpFactor;
      int scaledBehind = 0;
      while( pos <= upsmpFactor )
      {
        scaledBefore -= *before;
        scaledBehind += *behind;
        *currDst = (scaledBefore + scaledBehind + roundingOffset) >> log2UpsmpFactor;

        pos++;
        currDst += dstStep;
      }

      idxUpsmpDim++;
      before = behind;
      behind += srcStep;
    }

    idxOrthDim++;
    srcLine += srcStride;
    dstLine += dstStride;
    bndryLine += bndryStep;
  }
}

void MatrixIntraPrediction::predictionUpsampling(Pel *const dst, const Pel *const src) const
{
  const Pel *verSrc     = src;
  SizeType   verSrcStep = m_blockSize.width;

  if( m_upsmpFactorHor > 1 )
  {
    Pel *const horDst = dst + (m_upsmpFactorVer - 1) * m_blockSize.width;
    verSrc = horDst;
    verSrcStep *= m_upsmpFactorVer;

    predictionUpsampling1D( horDst, src, m_refSamplesLeft.data(),
                            m_reducedPredSize, m_reducedPredSize,
                            1, m_reducedPredSize, 1, verSrcStep,
                            m_upsmpFactorVer, m_upsmpFactorHor );
  }

  if( m_upsmpFactorVer > 1 )
  {
    predictionUpsampling1D( dst, verSrc, m_refSamplesTop.data(),
                            m_reducedPredSize, m_blockSize.width,
                            verSrcStep, 1, m_blockSize.width, 1,
                            1, m_upsmpFactorVer );
  }
}

const uint8_t* MatrixIntraPrediction::getMatrixData(const int modeIdx) const
{
  switch( m_sizeId )
  {
  case MipSizeId::S0:
    return &mipMatrix4x4[modeIdx][0][0];

  case MipSizeId::S1:
    return &mipMatrix8x8[modeIdx][0][0];

  case MipSizeId::S2:
  default:
    return &mipMatrix16x16[modeIdx][0][0];
  }
}

void MatrixIntraPrediction::computeReducedPred(Pel *const result, const Pel *const input, const uint8_t *matrix,
                                               const bool transpose, const int bitDepth)
{
  const int inputSize = 2 * m_reducedBdrySize;

  // use local buffer for transposed result
  static_vector<Pel, MIP_MAX_REDUCED_OUTPUT_SAMPLES> resBufTransposed(m_reducedPredSize * m_reducedPredSize);

  Pel *const resPtr = (transpose) ? resBufTransposed.data() : result;

  int sum = 0;
  for (int i = 0; i < inputSize; i++)
  {
    sum += input[i];
  }
  const int offset = (1 << (MIP_SHIFT_MATRIX - 1)) - MIP_OFFSET_MATRIX * sum;
  CHECK( inputSize != 4 * (inputSize >> 2), "Error, input size not divisible by four" );

  const uint8_t *weight = matrix;
  const int   inputOffset = transpose ? m_inputOffsetTransp : m_inputOffset;

  const bool redSize = (m_sizeId == MipSizeId::S2);
  int posRes = 0;
  for( int y = 0; y < m_reducedPredSize; y++ )
  {
    for( int x = 0; x < m_reducedPredSize; x++ )
    {
      if (redSize)
      {
        weight -= 1;
      }
      int tmp0 = redSize ? 0 : (input[0] * weight[0]);
      int tmp1 = input[1] * weight[1];
      int tmp2 = input[2] * weight[2];
      int tmp3 = input[3] * weight[3];
      for (int i = 4; i < inputSize; i += 4)
      {
        tmp0 += input[i]     * weight[i];
        tmp1 += input[i + 1] * weight[i + 1];
        tmp2 += input[i + 2] * weight[i + 2];
        tmp3 += input[i + 3] * weight[i + 3];
      }
      resPtr[posRes++] = ClipBD<int>(((tmp0 + tmp1 + tmp2 + tmp3 + offset) >> MIP_SHIFT_MATRIX) + inputOffset, bitDepth);

      weight += inputSize;
    }
  }

  if( transpose )
  {
    for( int y = 0; y < m_reducedPredSize; y++ )
    {
      for( int x = 0; x < m_reducedPredSize; x++ )
      {
        result[ y * m_reducedPredSize + x ] = resPtr[ x * m_reducedPredSize + y ];
      }
    }
  }
}