// Copyright 2016 Google Inc. All Rights Reserved.

//

// Use of this source code is governed by a BSD-style license

// that can be found in the COPYING file in the root of the source

// tree. An additional intellectual property rights grant can be found

// in the file PATENTS. All contributing project authors may

// be found in the AUTHORS file in the root of the source tree.

// -----------------------------------------------------------------------------

//

// Image transform methods for lossless encoder.

//

// Authors: Vikas Arora (vikaas.arora@gmail.com)

//          Jyrki Alakuijala (jyrki@google.com)

//          Urvang Joshi (urvang@google.com)

//          Vincent Rabaud (vrabaud@google.com)

#include "src/dsp/lossless.h"

#include "src/dsp/lossless_common.h"

#include "src/enc/vp8i_enc.h"

#include "src/enc/vp8li_enc.h"

#define MAX_DIFF_COST (1e30f)

#define HISTO_SIZE (4 * 256)

static const float kSpatialPredictorBias = 15.f;

static const int kPredLowEffort = 11;

static const uint32_t kMaskAlpha = 0xff000000;

// Mostly used to reduce code size + readability

static WEBP_INLINE int GetMin(int a, int b) { return (a > b) ? b : a; }

//------------------------------------------------------------------------------

// Methods to calculate Entropy (Shannon).

// Compute a bias for prediction entropy using a global heuristic to favor

// values closer to 0. Hence the final negative sign.

static float PredictionCostBias(const uint32_t counts[256], int weight_0,

                                float exp_val) {

  const int significant_symbols = 256 >> 4;

  const float exp_decay_factor = 0.6f;

  float bits = (float)weight_0 * counts[0];

  int i;

  for (i = 1; i < significant_symbols; ++i) {

    bits += exp_val * (counts[i] + counts[256 - i]);

    exp_val *= exp_decay_factor;

  }

  return (float)(-0.1 * bits);

}

static float PredictionCostSpatialHistogram(

    const uint32_t accumulated[HISTO_SIZE], const uint32_t tile[HISTO_SIZE],

    int mode, int left_mode, int above_mode) {

  int i;

  float retval = 0.f;

  for (i = 0; i < 4; ++i) {

    const float kExpValue = 0.94f;

    retval += PredictionCostBias(&tile[i * 256], 1, kExpValue);

    // Compute the new cost if 'tile' is added to 'accumulate' but also add the

    // cost of the current histogram to guide the spatial predictor selection.

    // Basically, favor low entropy, locally and globally.

    retval += VP8LCombinedShannonEntropy(&tile[i * 256], &accumulated[i * 256]);

  }

  // Favor keeping the areas locally similar.

  if (mode == left_mode) retval -= kSpatialPredictorBias;

  if (mode == above_mode) retval -= kSpatialPredictorBias;

  return retval;

}

static WEBP_INLINE void UpdateHisto(uint32_t histo_argb[HISTO_SIZE],

                                    uint32_t argb) {

  ++histo_argb[0 * 256 + (argb >> 24)];

  ++histo_argb[1 * 256 + ((argb >> 16) & 0xff)];

  ++histo_argb[2 * 256 + ((argb >> 8) & 0xff)];

  ++histo_argb[3 * 256 + (argb & 0xff)];

}

//------------------------------------------------------------------------------

// Spatial transform functions.

static WEBP_INLINE void PredictBatch(int mode, int x_start, int y,

                                     int num_pixels, const uint32_t* current,

                                     const uint32_t* upper, uint32_t* out) {

  if (x_start == 0) {

    if (y == 0) {

      // ARGB_BLACK.

      VP8LPredictorsSub[0](current, NULL, 1, out);

    } else {

      // Top one.

      VP8LPredictorsSub[2](current, upper, 1, out);

    }

    ++x_start;

    ++out;

    --num_pixels;

  }

  if (y == 0) {

    // Left one.

    VP8LPredictorsSub[1](current + x_start, NULL, num_pixels, out);

  } else {

    VP8LPredictorsSub[mode](current + x_start, upper + x_start, num_pixels,

                            out);

  }

}

#if (WEBP_NEAR_LOSSLESS == 1)

static WEBP_INLINE int GetMax(int a, int b) { return (a < b) ? b : a; }

static int MaxDiffBetweenPixels(uint32_t p1, uint32_t p2) {

  const int diff_a = abs((int)(p1 >> 24) - (int)(p2 >> 24));

  const int diff_r = abs((int)((p1 >> 16) & 0xff) - (int)((p2 >> 16) & 0xff));

  const int diff_g = abs((int)((p1 >> 8) & 0xff) - (int)((p2 >> 8) & 0xff));

  const int diff_b = abs((int)(p1 & 0xff) - (int)(p2 & 0xff));

  return GetMax(GetMax(diff_a, diff_r), GetMax(diff_g, diff_b));

}

static int MaxDiffAroundPixel(uint32_t current, uint32_t up, uint32_t down,

                              uint32_t left, uint32_t right) {

  const int diff_up = MaxDiffBetweenPixels(current, up);

  const int diff_down = MaxDiffBetweenPixels(current, down);

  const int diff_left = MaxDiffBetweenPixels(current, left);

  const int diff_right = MaxDiffBetweenPixels(current, right);

  return GetMax(GetMax(diff_up, diff_down), GetMax(diff_left, diff_right));

}

static uint32_t AddGreenToBlueAndRed(uint32_t argb) {

  const uint32_t green = (argb >> 8) & 0xff;

  uint32_t red_blue = argb & 0x00ff00ffu;

  red_blue += (green << 16) | green;

  red_blue &= 0x00ff00ffu;

  return (argb & 0xff00ff00u) | red_blue;

}

static void MaxDiffsForRow(int width, int stride, const uint32_t* const argb,

                           uint8_t* const max_diffs, int used_subtract_green) {

  uint32_t current, up, down, left, right;

  int x;

  if (width <= 2) return;

  current = argb[0];

  right = argb[1];

  if (used_subtract_green) {

    current = AddGreenToBlueAndRed(current);

    right = AddGreenToBlueAndRed(right);

  }

  // max_diffs[0] and max_diffs[width - 1] are never used.

  for (x = 1; x < width - 1; ++x) {

    up = argb[-stride + x];

    down = argb[stride + x];

    left = current;

    current = right;

    right = argb[x + 1];

    if (used_subtract_green) {

      up = AddGreenToBlueAndRed(up);

      down = AddGreenToBlueAndRed(down);

      right = AddGreenToBlueAndRed(right);

    }

    max_diffs[x] = MaxDiffAroundPixel(current, up, down, left, right);

  }

}

// Quantize the difference between the actual component value and its prediction

// to a multiple of quantization, working modulo 256, taking care not to cross

// a boundary (inclusive upper limit).

static uint8_t NearLosslessComponent(uint8_t value, uint8_t predict,

                                     uint8_t boundary, int quantization) {

  const int residual = (value - predict) & 0xff;

  const int boundary_residual = (boundary - predict) & 0xff;

  const int lower = residual & ~(quantization - 1);

  const int upper = lower + quantization;

  // Resolve ties towards a value closer to the prediction (i.e. towards lower

  // if value comes after prediction and towards upper otherwise).

  const int bias = ((boundary - value) & 0xff) < boundary_residual;

  if (residual - lower < upper - residual + bias) {

    // lower is closer to residual than upper.

    if (residual > boundary_residual && lower <= boundary_residual) {

      // Halve quantization step to avoid crossing boundary. This midpoint is

      // on the same side of boundary as residual because midpoint >= residual

      // (since lower is closer than upper) and residual is above the boundary.

      return lower + (quantization >> 1);

    }

    return lower;

  } else {

    // upper is closer to residual than lower.

    if (residual <= boundary_residual && upper > boundary_residual) {

      // Halve quantization step to avoid crossing boundary. This midpoint is

      // on the same side of boundary as residual because midpoint <= residual

      // (since upper is closer than lower) and residual is below the boundary.

      return lower + (quantization >> 1);

    }

    return upper & 0xff;

  }

}

static WEBP_INLINE uint8_t NearLosslessDiff(uint8_t a, uint8_t b) {

  return (uint8_t)((((int)(a) - (int)(b))) & 0xff);

}

// Quantize every component of the difference between the actual pixel value and

// its prediction to a multiple of a quantization (a power of 2, not larger than

// max_quantization which is a power of 2, smaller than max_diff). Take care if

// value and predict have undergone subtract green, which means that red and

// blue are represented as offsets from green.

static uint32_t NearLossless(uint32_t value, uint32_t predict,

                             int max_quantization, int max_diff,

                             int used_subtract_green) {

  int quantization;

  uint8_t new_green = 0;

  uint8_t green_diff = 0;

  uint8_t a, r, g, b;

  if (max_diff <= 2) {

    return VP8LSubPixels(value, predict);

  }

  quantization = max_quantization;

  while (quantization >= max_diff) {

    quantization >>= 1;

  }

  if ((value >> 24) == 0 || (value >> 24) == 0xff) {

    // Preserve transparency of fully transparent or fully opaque pixels.

    a = NearLosslessDiff((value >> 24) & 0xff, (predict >> 24) & 0xff);

  } else {

    a = NearLosslessComponent(value >> 24, predict >> 24, 0xff, quantization);

  }

  g = NearLosslessComponent((value >> 8) & 0xff, (predict >> 8) & 0xff, 0xff,

                            quantization);

  if (used_subtract_green) {

    // The green offset will be added to red and blue components during decoding

    // to obtain the actual red and blue values.

    new_green = ((predict >> 8) + g) & 0xff;

    // The amount by which green has been adjusted during quantization. It is

    // subtracted from red and blue for compensation, to avoid accumulating two

    // quantization errors in them.

    green_diff = NearLosslessDiff(new_green, (value >> 8) & 0xff);

  }

  r = NearLosslessComponent(NearLosslessDiff((value >> 16) & 0xff, green_diff),

                            (predict >> 16) & 0xff, 0xff - new_green,

                            quantization);

  b = NearLosslessComponent(NearLosslessDiff(value & 0xff, green_diff),

                            predict & 0xff, 0xff - new_green, quantization);

  return ((uint32_t)a << 24) | ((uint32_t)r << 16) | ((uint32_t)g << 8) | b;

}

#endif  // (WEBP_NEAR_LOSSLESS == 1)

// Stores the difference between the pixel and its prediction in "out".

// In case of a lossy encoding, updates the source image to avoid propagating

// the deviation further to pixels which depend on the current pixel for their

// predictions.

static WEBP_INLINE void GetResidual(

    int width, int height, uint32_t* const upper_row,

    uint32_t* const current_row, const uint8_t* const max_diffs, int mode,

    int x_start, int x_end, int y, int max_quantization, int exact,

    int used_subtract_green, uint32_t* const out) {

  if (exact) {

    PredictBatch(mode, x_start, y, x_end - x_start, current_row, upper_row,

                 out);

  } else {

    const VP8LPredictorFunc pred_func = VP8LPredictors[mode];

    int x;

    for (x = x_start; x < x_end; ++x) {

      uint32_t predict;

      uint32_t residual;

      if (y == 0) {

        predict = (x == 0) ? ARGB_BLACK : current_row[x - 1];  // Left.

      } else if (x == 0) {

        predict = upper_row[x];  // Top.

      } else {

        predict = pred_func(&current_row[x - 1], upper_row + x);

      }

#if (WEBP_NEAR_LOSSLESS == 1)

      if (max_quantization == 1 || mode == 0 || y == 0 || y == height - 1 ||

          x == 0 || x == width - 1) {

        residual = VP8LSubPixels(current_row[x], predict);

      } else {

        residual = NearLossless(current_row[x], predict, max_quantization,

                                max_diffs[x], used_subtract_green);

        // Update the source image.

        current_row[x] = VP8LAddPixels(predict, residual);

        // x is never 0 here so we do not need to update upper_row like below.

      }

#else

      (void)max_diffs;

      (void)height;

      (void)max_quantization;

      (void)used_subtract_green;

      residual = VP8LSubPixels(current_row[x], predict);

#endif

      if ((current_row[x] & kMaskAlpha) == 0) {

        // If alpha is 0, cleanup RGB. We can choose the RGB values of the

        // residual for best compression. The prediction of alpha itself can be

        // non-zero and must be kept though. We choose RGB of the residual to be

        // 0.

        residual &= kMaskAlpha;

        // Update the source image.

        current_row[x] = predict & ~kMaskAlpha;

        // The prediction for the rightmost pixel in a row uses the leftmost

        // pixel

        // in that row as its top-right context pixel. Hence if we change the

        // leftmost pixel of current_row, the corresponding change must be

        // applied

        // to upper_row as well where top-right context is being read from.

        if (x == 0 && y != 0) upper_row[width] = current_row[0];

      }

      out[x - x_start] = residual;

    }

  }

}

// Returns best predictor and updates the accumulated histogram.

// If max_quantization > 1, assumes that near lossless processing will be

// applied, quantizing residuals to multiples of quantization levels up to

// max_quantization (the actual quantization level depends on smoothness near

// the given pixel).

static int GetBestPredictorForTile(

    int width, int height, int tile_x, int tile_y, int bits,

    uint32_t accumulated[HISTO_SIZE], uint32_t* const argb_scratch,

    const uint32_t* const argb, int max_quantization, int exact,

    int used_subtract_green, const uint32_t* const modes) {

  const int kNumPredModes = 14;

  const int start_x = tile_x << bits;

  const int start_y = tile_y << bits;

  const int tile_size = 1 << bits;

  const int max_y = GetMin(tile_size, height - start_y);

  const int max_x = GetMin(tile_size, width - start_x);

  // Whether there exist columns just outside the tile.

  const int have_left = (start_x > 0);

  // Position and size of the strip covering the tile and adjacent columns if

  // they exist.

  const int context_start_x = start_x - have_left;

#if (WEBP_NEAR_LOSSLESS == 1)

  const int context_width = max_x + have_left + (max_x < width - start_x);

#endif

  const int tiles_per_row = VP8LSubSampleSize(width, bits);

  // Prediction modes of the left and above neighbor tiles.

  const int left_mode = (tile_x > 0) ?

      (modes[tile_y * tiles_per_row + tile_x - 1] >> 8) & 0xff : 0xff;

  const int above_mode = (tile_y > 0) ?

      (modes[(tile_y - 1) * tiles_per_row + tile_x] >> 8) & 0xff : 0xff;

  // The width of upper_row and current_row is one pixel larger than image width

  // to allow the top right pixel to point to the leftmost pixel of the next row

  // when at the right edge.

  uint32_t* upper_row = argb_scratch;

  uint32_t* current_row = upper_row + width + 1;

  uint8_t* const max_diffs = (uint8_t*)(current_row + width + 1);

  float best_diff = MAX_DIFF_COST;

  int best_mode = 0;

  int mode;

  uint32_t histo_stack_1[HISTO_SIZE];

  uint32_t histo_stack_2[HISTO_SIZE];

  // Need pointers to be able to swap arrays.

  uint32_t* histo_argb = histo_stack_1;

  uint32_t* best_histo = histo_stack_2;

  uint32_t residuals[1 << MAX_TRANSFORM_BITS];

  assert(bits <= MAX_TRANSFORM_BITS);

  assert(max_x <= (1 << MAX_TRANSFORM_BITS));

  for (mode = 0; mode < kNumPredModes; ++mode) {

    float cur_diff;

    int relative_y;

    memset(histo_argb, 0, sizeof(histo_stack_1));

    if (start_y > 0) {

      // Read the row above the tile which will become the first upper_row.

      // Include a pixel to the left if it exists; include a pixel to the right

      // in all cases (wrapping to the leftmost pixel of the next row if it does

      // not exist).

      memcpy(current_row + context_start_x,

             argb + (start_y - 1) * width + context_start_x,

             sizeof(*argb) * (max_x + have_left + 1));

    }

    for (relative_y = 0; relative_y < max_y; ++relative_y) {

      const int y = start_y + relative_y;

      int relative_x;

      uint32_t* tmp = upper_row;

      upper_row = current_row;

      current_row = tmp;

      // Read current_row. Include a pixel to the left if it exists; include a

      // pixel to the right in all cases except at the bottom right corner of

      // the image (wrapping to the leftmost pixel of the next row if it does

      // not exist in the current row).

      memcpy(current_row + context_start_x,

             argb + y * width + context_start_x,

             sizeof(*argb) * (max_x + have_left + (y + 1 < height)));

#if (WEBP_NEAR_LOSSLESS == 1)

      if (max_quantization > 1 && y >= 1 && y + 1 < height) {

        MaxDiffsForRow(context_width, width, argb + y * width + context_start_x,

                       max_diffs + context_start_x, used_subtract_green);

      }

#endif

      GetResidual(width, height, upper_row, current_row, max_diffs, mode,

                  start_x, start_x + max_x, y, max_quantization, exact,

                  used_subtract_green, residuals);

      for (relative_x = 0; relative_x < max_x; ++relative_x) {

        UpdateHisto(histo_argb, residuals[relative_x]);

      }

    }

    cur_diff = PredictionCostSpatialHistogram(accumulated, histo_argb, mode,

                                              left_mode, above_mode);

    if (cur_diff < best_diff) {

      uint32_t* tmp = histo_argb;

      histo_argb = best_histo;

      best_histo = tmp;

      best_diff = cur_diff;

      best_mode = mode;

    }

  }

  VP8LAddVectorEq(best_histo, accumulated, HISTO_SIZE);

  return best_mode;

}

// Converts pixels of the image to residuals with respect to predictions.

// If max_quantization > 1, applies near lossless processing, quantizing

// residuals to multiples of quantization levels up to max_quantization

// (the actual quantization level depends on smoothness near the given pixel).

static void CopyImageWithPrediction(int width, int height, int bits,

                                    const uint32_t* const modes,

                                    uint32_t* const argb_scratch,

                                    uint32_t* const argb, int low_effort,

                                    int max_quantization, int exact,

                                    int used_subtract_green) {

  const int tiles_per_row = VP8LSubSampleSize(width, bits);

  // The width of upper_row and current_row is one pixel larger than image width

  // to allow the top right pixel to point to the leftmost pixel of the next row

  // when at the right edge.

  uint32_t* upper_row = argb_scratch;

  uint32_t* current_row = upper_row + width + 1;

  uint8_t* current_max_diffs = (uint8_t*)(current_row + width + 1);

#if (WEBP_NEAR_LOSSLESS == 1)

  uint8_t* lower_max_diffs = current_max_diffs + width;

#endif

  int y;

  for (y = 0; y < height; ++y) {

    int x;

    uint32_t* const tmp32 = upper_row;

    upper_row = current_row;

    current_row = tmp32;

    memcpy(current_row, argb + y * width,

           sizeof(*argb) * (width + (y + 1 < height)));

    if (low_effort) {

      PredictBatch(kPredLowEffort, 0, y, width, current_row, upper_row,

                   argb + y * width);

    } else {

#if (WEBP_NEAR_LOSSLESS == 1)

      if (max_quantization > 1) {

        // Compute max_diffs for the lower row now, because that needs the

        // contents of argb for the current row, which we will overwrite with

        // residuals before proceeding with the next row.

        uint8_t* const tmp8 = current_max_diffs;

        current_max_diffs = lower_max_diffs;

        lower_max_diffs = tmp8;

        if (y + 2 < height) {

          MaxDiffsForRow(width, width, argb + (y + 1) * width, lower_max_diffs,

                         used_subtract_green);

        }

      }

#endif

      for (x = 0; x < width;) {

        const int mode =

            (modes[(y >> bits) * tiles_per_row + (x >> bits)] >> 8) & 0xff;

        int x_end = x + (1 << bits);

        if (x_end > width) x_end = width;

        GetResidual(width, height, upper_row, current_row, current_max_diffs,

                    mode, x, x_end, y, max_quantization, exact,

                    used_subtract_green, argb + y * width + x);

        x = x_end;

      }

    }

  }

}

// Finds the best predictor for each tile, and converts the image to residuals

// with respect to predictions. If near_lossless_quality < 100, applies

// near lossless processing, shaving off more bits of residuals for lower

// qualities.

int VP8LResidualImage(int width, int height, int bits, int low_effort,

                      uint32_t* const argb, uint32_t* const argb_scratch,

                      uint32_t* const image, int near_lossless_quality,

                      int exact, int used_subtract_green,

                      const WebPPicture* const pic, int percent_range,

                      int* const percent) {

  const int tiles_per_row = VP8LSubSampleSize(width, bits);

  const int tiles_per_col = VP8LSubSampleSize(height, bits);

  int percent_start = *percent;

  const int max_quantization = 1 << VP8LNearLosslessBits(near_lossless_quality);

  if (low_effort) {

    int i;

    for (i = 0; i < tiles_per_row * tiles_per_col; ++i) {

      image[i] = ARGB_BLACK | (kPredLowEffort << 8);

    }

  } else {

    int tile_y;

    uint32_t histo[HISTO_SIZE] = { 0 };

    for (tile_y = 0; tile_y < tiles_per_col; ++tile_y) {

      int tile_x;

      for (tile_x = 0; tile_x < tiles_per_row; ++tile_x) {

        const int pred = GetBestPredictorForTile(

            width, height, tile_x, tile_y, bits, histo, argb_scratch, argb,

            max_quantization, exact, used_subtract_green, image);

        image[tile_y * tiles_per_row + tile_x] = ARGB_BLACK | (pred << 8);

      }

      if (!WebPReportProgress(

              pic, percent_start + percent_range * tile_y / tiles_per_col,

              percent)) {

        return 0;

      }

    }

  }

  CopyImageWithPrediction(width, height, bits, image, argb_scratch, argb,

                          low_effort, max_quantization, exact,

                          used_subtract_green);

  return WebPReportProgress(pic, percent_start + percent_range, percent);

}

//------------------------------------------------------------------------------

// Color transform functions.

static WEBP_INLINE void MultipliersClear(VP8LMultipliers* const m) {

  m->green_to_red_ = 0;

  m->green_to_blue_ = 0;

  m->red_to_blue_ = 0;

}

static WEBP_INLINE void ColorCodeToMultipliers(uint32_t color_code,

                                               VP8LMultipliers* const m) {

  m->green_to_red_  = (color_code >>  0) & 0xff;

  m->green_to_blue_ = (color_code >>  8) & 0xff;

  m->red_to_blue_   = (color_code >> 16) & 0xff;

}

static WEBP_INLINE uint32_t MultipliersToColorCode(

    const VP8LMultipliers* const m) {

  return 0xff000000u |

         ((uint32_t)(m->red_to_blue_) << 16) |

         ((uint32_t)(m->green_to_blue_) << 8) |

         m->green_to_red_;

}

static float PredictionCostCrossColor(const uint32_t accumulated[256],

                                      const uint32_t counts[256]) {

  // Favor low entropy, locally and globally.

  // Favor small absolute values for PredictionCostSpatial

  static const float kExpValue = 2.4f;

  return VP8LCombinedShannonEntropy(counts, accumulated) +

         PredictionCostBias(counts, 3, kExpValue);

}

static float GetPredictionCostCrossColorRed(

    const uint32_t* argb, int stride, int tile_width, int tile_height,

    VP8LMultipliers prev_x, VP8LMultipliers prev_y, int green_to_red,

    const uint32_t accumulated_red_histo[256]) {

  uint32_t histo[256] = { 0 };

  float cur_diff;

  VP8LCollectColorRedTransforms(argb, stride, tile_width, tile_height,

                                green_to_red, histo);

  cur_diff = PredictionCostCrossColor(accumulated_red_histo, histo);

  if ((uint8_t)green_to_red == prev_x.green_to_red_) {

    cur_diff -= 3;  // favor keeping the areas locally similar

  }

  if ((uint8_t)green_to_red == prev_y.green_to_red_) {

    cur_diff -= 3;  // favor keeping the areas locally similar

  }

  if (green_to_red == 0) {

    cur_diff -= 3;

  }

  return cur_diff;

}

static void GetBestGreenToRed(const uint32_t* argb, int stride, int tile_width,

                              int tile_height, VP8LMultipliers prev_x,

                              VP8LMultipliers prev_y, int quality,

                              const uint32_t accumulated_red_histo[256],

                              VP8LMultipliers* const best_tx) {

  const int kMaxIters = 4 + ((7 * quality) >> 8);  // in range [4..6]

  int green_to_red_best = 0;

  int iter, offset;

  float best_diff = GetPredictionCostCrossColorRed(

      argb, stride, tile_width, tile_height, prev_x, prev_y,

      green_to_red_best, accumulated_red_histo);

  for (iter = 0; iter < kMaxIters; ++iter) {

    // ColorTransformDelta is a 3.5 bit fixed point, so 32 is equal to

    // one in color computation. Having initial delta here as 1 is sufficient

    // to explore the range of (-2, 2).

    const int delta = 32 >> iter;

    // Try a negative and a positive delta from the best known value.

    for (offset = -delta; offset <= delta; offset += 2 * delta) {

      const int green_to_red_cur = offset + green_to_red_best;

      const float cur_diff = GetPredictionCostCrossColorRed(

          argb, stride, tile_width, tile_height, prev_x, prev_y,

          green_to_red_cur, accumulated_red_histo);

      if (cur_diff < best_diff) {

        best_diff = cur_diff;

        green_to_red_best = green_to_red_cur;

      }

    }

  }

  best_tx->green_to_red_ = (green_to_red_best & 0xff);

}

static float GetPredictionCostCrossColorBlue(

    const uint32_t* argb, int stride, int tile_width, int tile_height,

    VP8LMultipliers prev_x, VP8LMultipliers prev_y, int green_to_blue,

    int red_to_blue, const uint32_t accumulated_blue_histo[256]) {

  uint32_t histo[256] = { 0 };

  float cur_diff;

  VP8LCollectColorBlueTransforms(argb, stride, tile_width, tile_height,

                                 green_to_blue, red_to_blue, histo);

  cur_diff = PredictionCostCrossColor(accumulated_blue_histo, histo);

  if ((uint8_t)green_to_blue == prev_x.green_to_blue_) {

    cur_diff -= 3;  // favor keeping the areas locally similar

  }

  if ((uint8_t)green_to_blue == prev_y.green_to_blue_) {

    cur_diff -= 3;  // favor keeping the areas locally similar

  }

  if ((uint8_t)red_to_blue == prev_x.red_to_blue_) {

    cur_diff -= 3;  // favor keeping the areas locally similar

  }

  if ((uint8_t)red_to_blue == prev_y.red_to_blue_) {

    cur_diff -= 3;  // favor keeping the areas locally similar

  }

  if (green_to_blue == 0) {

    cur_diff -= 3;

  }

  if (red_to_blue == 0) {

    cur_diff -= 3;

  }

  return cur_diff;

}

#define kGreenRedToBlueNumAxis 8

#define kGreenRedToBlueMaxIters 7

static void GetBestGreenRedToBlue(const uint32_t* argb, int stride,

                                  int tile_width, int tile_height,

                                  VP8LMultipliers prev_x,

                                  VP8LMultipliers prev_y, int quality,

                                  const uint32_t accumulated_blue_histo[256],

                                  VP8LMultipliers* const best_tx) {

  const int8_t offset[kGreenRedToBlueNumAxis][2] =

      {{0, -1}, {0, 1}, {-1, 0}, {1, 0}, {-1, -1}, {-1, 1}, {1, -1}, {1, 1}};

  const int8_t delta_lut[kGreenRedToBlueMaxIters] = { 16, 16, 8, 4, 2, 2, 2 };

  const int iters =

      (quality < 25) ? 1 : (quality > 50) ? kGreenRedToBlueMaxIters : 4;

  int green_to_blue_best = 0;

  int red_to_blue_best = 0;

  int iter;

  // Initial value at origin:

  float best_diff = GetPredictionCostCrossColorBlue(

      argb, stride, tile_width, tile_height, prev_x, prev_y,

      green_to_blue_best, red_to_blue_best, accumulated_blue_histo);

  for (iter = 0; iter < iters; ++iter) {

    const int delta = delta_lut[iter];

    int axis;

    for (axis = 0; axis < kGreenRedToBlueNumAxis; ++axis) {

      const int green_to_blue_cur =

          offset[axis][0] * delta + green_to_blue_best;

      const int red_to_blue_cur = offset[axis][1] * delta + red_to_blue_best;

      const float cur_diff = GetPredictionCostCrossColorBlue(

          argb, stride, tile_width, tile_height, prev_x, prev_y,

          green_to_blue_cur, red_to_blue_cur, accumulated_blue_histo);

      if (cur_diff < best_diff) {

        best_diff = cur_diff;

        green_to_blue_best = green_to_blue_cur;

        red_to_blue_best = red_to_blue_cur;

      }

      if (quality < 25 && iter == 4) {

        // Only axis aligned diffs for lower quality.

        break;  // next iter.

      }

    }

    if (delta == 2 && green_to_blue_best == 0 && red_to_blue_best == 0) {

      // Further iterations would not help.

      break;  // out of iter-loop.

    }

  }

  best_tx->green_to_blue_ = green_to_blue_best & 0xff;

  best_tx->red_to_blue_ = red_to_blue_best & 0xff;

}

#undef kGreenRedToBlueMaxIters

#undef kGreenRedToBlueNumAxis

static VP8LMultipliers GetBestColorTransformForTile(

    int tile_x, int tile_y, int bits, VP8LMultipliers prev_x,

    VP8LMultipliers prev_y, int quality, int xsize, int ysize,

    const uint32_t accumulated_red_histo[256],

    const uint32_t accumulated_blue_histo[256], const uint32_t* const argb) {

  const int max_tile_size = 1 << bits;

  const int tile_y_offset = tile_y * max_tile_size;

  const int tile_x_offset = tile_x * max_tile_size;

  const int all_x_max = GetMin(tile_x_offset + max_tile_size, xsize);

  const int all_y_max = GetMin(tile_y_offset + max_tile_size, ysize);

  const int tile_width = all_x_max - tile_x_offset;

  const int tile_height = all_y_max - tile_y_offset;

  const uint32_t* const tile_argb = argb + tile_y_offset * xsize

                                  + tile_x_offset;

  VP8LMultipliers best_tx;

  MultipliersClear(&best_tx);

  GetBestGreenToRed(tile_argb, xsize, tile_width, tile_height,

                    prev_x, prev_y, quality, accumulated_red_histo, &best_tx);

  GetBestGreenRedToBlue(tile_argb, xsize, tile_width, tile_height,

                        prev_x, prev_y, quality, accumulated_blue_histo,

                        &best_tx);

  return best_tx;

}

static void CopyTileWithColorTransform(int xsize, int ysize,

                                       int tile_x, int tile_y,

                                       int max_tile_size,

                                       VP8LMultipliers color_transform,

                                       uint32_t* argb) {

  const int xscan = GetMin(max_tile_size, xsize - tile_x);

  int yscan = GetMin(max_tile_size, ysize - tile_y);

  argb += tile_y * xsize + tile_x;

  while (yscan-- > 0) {

    VP8LTransformColor(&color_transform, argb, xscan);

    argb += xsize;

  }

}

int VP8LColorSpaceTransform(int width, int height, int bits, int quality,

                            uint32_t* const argb, uint32_t* image,

                            const WebPPicture* const pic, int percent_range,

                            int* const percent) {

  const int max_tile_size = 1 << bits;

  const int tile_xsize = VP8LSubSampleSize(width, bits);

  const int tile_ysize = VP8LSubSampleSize(height, bits);

  int percent_start = *percent;

  uint32_t accumulated_red_histo[256] = { 0 };

  uint32_t accumulated_blue_histo[256] = { 0 };

  int tile_x, tile_y;

  VP8LMultipliers prev_x, prev_y;

  MultipliersClear(&prev_y);

  MultipliersClear(&prev_x);

  for (tile_y = 0; tile_y < tile_ysize; ++tile_y) {

    for (tile_x = 0; tile_x < tile_xsize; ++tile_x) {

      int y;

      const int tile_x_offset = tile_x * max_tile_size;

      const int tile_y_offset = tile_y * max_tile_size;

      const int all_x_max = GetMin(tile_x_offset + max_tile_size, width);

      const int all_y_max = GetMin(tile_y_offset + max_tile_size, height);

      const int offset = tile_y * tile_xsize + tile_x;

      if (tile_y != 0) {

        ColorCodeToMultipliers(image[offset - tile_xsize], &prev_y);

      }

      prev_x = GetBestColorTransformForTile(tile_x, tile_y, bits,

                                            prev_x, prev_y,

                                            quality, width, height,

                                            accumulated_red_histo,

                                            accumulated_blue_histo,

                                            argb);

      image[offset] = MultipliersToColorCode(&prev_x);

      CopyTileWithColorTransform(width, height, tile_x_offset, tile_y_offset,

                                 max_tile_size, prev_x, argb);

      // Gather accumulated histogram data.

      for (y = tile_y_offset; y < all_y_max; ++y) {

        int ix = y * width + tile_x_offset;

        const int ix_end = ix + all_x_max - tile_x_offset;

        for (; ix < ix_end; ++ix) {

          const uint32_t pix = argb[ix];

          if (ix >= 2 &&

              pix == argb[ix - 2] &&

              pix == argb[ix - 1]) {

            continue;  // repeated pixels are handled by backward references

          }

          if (ix >= width + 2 &&

              argb[ix - 2] == argb[ix - width - 2] &&

              argb[ix - 1] == argb[ix - width - 1] &&

              pix == argb[ix - width]) {

            continue;  // repeated pixels are handled by backward references

          }

          ++accumulated_red_histo[(pix >> 16) & 0xff];

          ++accumulated_blue_histo[(pix >> 0) & 0xff];

        }

      }

    }

    if (!WebPReportProgress(

            pic, percent_start + percent_range * tile_y / tile_ysize,

            percent)) {

      return 0;

    }

  }

  return 1;

}