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/** \file     MatrixIntraPrediction.cpp

\brief    matrix-based intra prediction class

*/

#include "MatrixIntraPrediction.h"

#include "dtrace_next.h"

#include "UnitTools.h"

#include "MipData.h"

namespace vvenc {

static const int MIP_MAX_INPUT_SIZE             =  8;

static const int MIP_MAX_REDUCED_OUTPUT_SAMPLES = 64;

MatrixIntraPrediction::MatrixIntraPrediction()

  : m_reducedBoundary       (nullptr)

  , m_reducedBoundaryTransp (nullptr)

  , m_inputOffset           ( 0 )

  , m_inputOffsetTransp     ( 0 )

  , m_refSamplesTop         (nullptr)

  , m_refSamplesLeft        (nullptr)

  , m_blockSize             ( 0, 0 )

  , m_sizeId                ( 0 )

  , m_reducedBdrySize       ( 0 )

  , m_reducedPredSize       ( 0 )

  , m_upsmpFactorHor        ( 0 )

  , m_upsmpFactorVer        ( 0 )

{

  m_reducedBoundary       = (Pel*)xMalloc( Pel, MIP_MAX_INPUT_SIZE ); 

  m_reducedBoundaryTransp = (Pel*)xMalloc( Pel, MIP_MAX_INPUT_SIZE );

}

MatrixIntraPrediction::~MatrixIntraPrediction()

{

  xFree( m_reducedBoundary );       m_reducedBoundary = nullptr;

  xFree( m_reducedBoundaryTransp ); m_reducedBoundaryTransp = nullptr;

}

void MatrixIntraPrediction::prepareInputForPred(const CPelBuf &pSrc, const Area& block, const int bitDepth)

{

  // Step 1: Save block size and calculate dependent values

  initPredBlockParams(block);

  m_refSamplesTop  = pSrc.bufAt(1, 0);

  m_refSamplesLeft = pSrc.bufAt(1, 1);

  // Step 3: Compute the reduced boundary via Haar-downsampling (input for the prediction)

  const int inputSize = 2 * m_reducedBdrySize;

  Pel* const topReduced = m_reducedBoundary;

  boundaryDownsampling1D( topReduced, m_refSamplesTop, block.width, m_reducedBdrySize );

  Pel* const leftReduced = m_reducedBoundary + m_reducedBdrySize;

  boundaryDownsampling1D( leftReduced, m_refSamplesLeft, block.height, m_reducedBdrySize );

  Pel* const leftReducedTransposed = m_reducedBoundaryTransp;

  Pel* const topReducedTransposed  = m_reducedBoundaryTransp + 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_reducedBoundaryTransp[0];

  const bool hasFirstCol = (m_sizeId < 2);

  m_reducedBoundary      [0] = hasFirstCol ? ((1 << (bitDepth - 1)) - m_inputOffset      ) : 0; // first column of matrix not needed for large blocks

  m_reducedBoundaryTransp[0] = hasFirstCol ? ((1 << (bitDepth - 1)) - m_inputOffsetTransp) : 0;

  for (int i = 1; i < inputSize; i++)

  {

    m_reducedBoundary      [i] -= m_inputOffset;

    m_reducedBoundaryTransp[i] -= m_inputOffsetTransp;

  }

}

void MatrixIntraPrediction::predBlock(Pel* const result, const int modeIdx, const bool transpose, const int bitDepth)

{

  ALIGN_DATA( MEMORY_ALIGN_DEF_SIZE, Pel bufReducedPred[MIP_MAX_REDUCED_OUTPUT_SAMPLES] );

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

  Pel* const       reducedPred     = needUpsampling ? bufReducedPred : result;

  const Pel* const reducedBoundary = transpose ? m_reducedBoundaryTransp : m_reducedBoundary;

  {

    const int outputSize = m_reducedPredSize;

    const int inputSize  = 2 * m_reducedBdrySize;

    const int offset     = transpose ? m_inputOffsetTransp : m_inputOffset;

    const int maxVal     = ( 1 << bitDepth ) - 1;

    if( outputSize == 8)

    {

      g_pelBufOP.mipMatrixMul_8_8( reducedPred, reducedBoundary, &mipMatrix16x16[modeIdx][0][0], maxVal, offset, transpose );

    }

    else

    {

      if( inputSize == 4)

      {

        g_pelBufOP.mipMatrixMul_4_4( reducedPred, reducedBoundary, &mipMatrix4x4[modeIdx][0][0], maxVal, offset, transpose );

      }

      else

      {

        g_pelBufOP.mipMatrixMul_8_4( reducedPred, reducedBoundary, &mipMatrix8x8[modeIdx][0][0], maxVal, offset, transpose );

      }

    }

  }

  // Reduced prediction is transposed if ( transpose && needUpsampling ).

  if( needUpsampling )

  {

    const Pel* verSrc   = reducedPred;

    SizeType verSrcStep = m_blockSize.width;

    // upsampling factors 2, 4 or 8: MIP blocks are at most 64x64 with an aspect ratio of at most 4

    auto log2Factor = []( unsigned factor ) { return factor == 2 ? 1u : factor == 4 ? 2u : 3u; };

    if( m_upsmpFactorHor > 1 )

    {

      Pel* const horDst = result + (m_upsmpFactorVer - 1) * m_blockSize.width;

      verSrc = horDst;

      verSrcStep *= m_upsmpFactorVer;

      g_pelBufOP.mipUpsampleHor( horDst, reducedPred, &m_refSamplesLeft[0], verSrcStep, m_upsmpFactorVer, m_reducedPredSize, log2Factor( m_upsmpFactorHor ) );

    }

    if( m_upsmpFactorVer > 1 )

    {

      g_pelBufOP.mipUpsampleVer( result, verSrc, &m_refSamplesTop[0], m_blockSize.width, verSrcStep, m_reducedPredSize, log2Factor( m_upsmpFactorVer ) );

    }

  }

}

void MatrixIntraPrediction::initPredBlockParams(const Size& block)

{

  m_blockSize = block;

  // init size index

  m_sizeId = getMipSizeId( m_blockSize );

  // init reduced boundary size

  m_reducedBdrySize = (m_sizeId == 0) ? 2 : 4;

  // init reduced prediction size

  m_reducedPredSize = ( m_sizeId < 2 ) ? 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];

    }

  }

}

} // namespace vvenc

//! \}