Update libwebp to 0.6.0

1 files changed, 750 insertions, 0 deletions

diff --git a/thirdparty/libwebp/enc/predictor_enc.c b/thirdparty/libwebp/enc/predictor_enc.c
new file mode 100644
index 0000000000..0639b74f1c
--- /dev/null
+++ b/thirdparty/libwebp/enc/predictor_enc.c
@@ -0,0 +1,750 @@
+// 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 "../dsp/lossless.h"
+#include "../dsp/lossless_common.h"
+#include "./vp8li_enc.h"
+
+#define MAX_DIFF_COST (1e30f)
+
+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; }
+static WEBP_INLINE int GetMax(int a, int b) { return (a < b) ? b : a; }
+
+//------------------------------------------------------------------------------
+// Methods to calculate Entropy (Shannon).
+
+static float PredictionCostSpatial(const int counts[256], int weight_0,
+                                   double exp_val) {
+  const int significant_symbols = 256 >> 4;
+  const double exp_decay_factor = 0.6;
+  double bits = 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 int accumulated[4][256],
+                                            const int tile[4][256]) {
+  int i;
+  double retval = 0;
+  for (i = 0; i < 4; ++i) {
+    const double kExpValue = 0.94;
+    retval += PredictionCostSpatial(tile[i], 1, kExpValue);
+    retval += VP8LCombinedShannonEntropy(tile[i], accumulated[i]);
+  }
+  return (float)retval;
+}
+
+static WEBP_INLINE void UpdateHisto(int histo_argb[4][256], uint32_t argb) {
+  ++histo_argb[0][argb >> 24];
+  ++histo_argb[1][(argb >> 16) & 0xff];
+  ++histo_argb[2][(argb >> 8) & 0xff];
+  ++histo_argb[3][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);
+  }
+}
+
+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;
+  }
+}
+
+// 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 = ((value >> 24) - (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 = (new_green - (value >> 8)) & 0xff;
+  }
+  r = NearLosslessComponent(((value >> 16) - green_diff) & 0xff,
+                            (predict >> 16) & 0xff, 0xff - new_green,
+                            quantization);
+  b = NearLosslessComponent((value - green_diff) & 0xff, predict & 0xff,
+                            0xff - new_green, quantization);
+  return ((uint32_t)a << 24) | ((uint32_t)r << 16) | ((uint32_t)g << 8) | b;
+}
+
+// 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 (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.
+      }
+      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,
+                                   int accumulated[4][256],
+                                   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);
+  const int have_right = (max_x < width - start_x);
+  // Position and size of the strip covering the tile and adjacent columns if
+  // they exist.
+  const int context_start_x = start_x - have_left;
+  const int context_width = max_x + have_left + have_right;
+  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;
+  int histo_stack_1[4][256];
+  int histo_stack_2[4][256];
+  // Need pointers to be able to swap arrays.
+  int (*histo_argb)[256] = histo_stack_1;
+  int (*best_histo)[256] = histo_stack_2;
+  int i, j;
+  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 (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);
+      }
+
+      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(
+        (const int (*)[256])accumulated, (const int (*)[256])histo_argb);
+    // Favor keeping the areas locally similar.
+    if (mode == left_mode) cur_diff -= kSpatialPredictorBias;
+    if (mode == above_mode) cur_diff -= kSpatialPredictorBias;
+
+    if (cur_diff < best_diff) {
+      int (*tmp)[256] = histo_argb;
+      histo_argb = best_histo;
+      best_histo = tmp;
+      best_diff = cur_diff;
+      best_mode = mode;
+    }
+  }
+
+  for (i = 0; i < 4; i++) {
+    for (j = 0; j < 256; j++) {
+      accumulated[i][j] += best_histo[i][j];
+    }
+  }
+
+  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, 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);
+  uint8_t* lower_max_diffs = current_max_diffs + width;
+  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 (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);
+        }
+      }
+      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.
+void 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 int tiles_per_row = VP8LSubSampleSize(width, bits);
+  const int tiles_per_col = VP8LSubSampleSize(height, bits);
+  int tile_y;
+  int histo[4][256];
+  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 {
+    memset(histo, 0, sizeof(histo));
+    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);
+      }
+    }
+  }
+
+  CopyImageWithPrediction(width, height, bits, image, argb_scratch, argb,
+                          low_effort, max_quantization, exact,
+                          used_subtract_green);
+}
+
+//------------------------------------------------------------------------------
+// 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 int accumulated[256],
+                                      const int counts[256]) {
+  // Favor low entropy, locally and globally.
+  // Favor small absolute values for PredictionCostSpatial
+  static const double kExpValue = 2.4;
+  return VP8LCombinedShannonEntropy(counts, accumulated) +
+         PredictionCostSpatial(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 int accumulated_red_histo[256]) {
+  int 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 int 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;
+}
+
+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 int accumulated_blue_histo[256]) {
+  int 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 int 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;
+  best_tx->red_to_blue_ = red_to_blue_best;
+}
+#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 int accumulated_red_histo[256],
+    const int 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;
+  }
+}
+
+void VP8LColorSpaceTransform(int width, int height, int bits, int quality,
+                             uint32_t* const argb, uint32_t* image) {
+  const int max_tile_size = 1 << bits;
+  const int tile_xsize = VP8LSubSampleSize(width, bits);
+  const int tile_ysize = VP8LSubSampleSize(height, bits);
+  int accumulated_red_histo[256] = { 0 };
+  int 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];
+        }
+      }
+    }
+  }
+}