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diff --git a/media/libwebp/src/enc/predictor_enc.c b/media/libwebp/src/enc/predictor_enc.c
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+// 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)
+
+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).
+
+static float PredictionCostSpatial(const int 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 int accumulated[4][256],
+ const int tile[4][256]) {
+ int i;
+ float retval = 0.f;
+ for (i = 0; i < 4; ++i) {
+ const float kExpValue = 0.94f;
+ 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);
+ }
+}
+
+#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,
+ 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);
+ // 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;
+ 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 (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(
+ (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);
+#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;
+ 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);
+ }
+
+ 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 int accumulated[256],
+ const int counts[256]) {
+ // Favor low entropy, locally and globally.
+ // Favor small absolute values for PredictionCostSpatial
+ static const float kExpValue = 2.4f;
+ 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 & 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 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 & 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 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;
+ }
+}
+
+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;
+ 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];
+ }
+ }
+ }
+ if (!WebPReportProgress(
+ pic, percent_start + percent_range * tile_y / tile_ysize,
+ percent)) {
+ return 0;
+ }
+ }
+ return 1;
+}