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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(¤t_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;
}