dany/libwebp
1
0
Fork 0
mirror of https://github.com/webmproject/libwebp.git synced 2024-11-20 04:18:26 +01:00
Code Issues Actions Packages Projects Releases Wiki Activity

Merge "Refactor GetBestPredictorForTile for future tuning."

Browse Source
This commit is contained in:
James Zern 2014-02-28 18:39:27 -08:00 committed by Gerrit Code Review
commit e3dd9243cb
1 changed files with 52 additions and 53 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

105
src/dsp/lossless.c
View File

@ -541,15 +541,14 @@ static const PredictorFunc kPredictors[16] = {
Predictor0, Predictor0 // <- padding security sentinels
};
// TODO(vikasa): Replace 256 etc with defines.
static float PredictionCostSpatial(const int* counts,
int weight_0, double exp_val) {
const int significant_symbols = 16;
static float PredictionCostSpatial(const int* const counts, int weight_0,
double exp_val, int n) {
const int significant_symbols = n >> 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]);
bits += exp_val * (counts[i] + counts[n - i]);
exp_val *= exp_decay_factor;
}
return (float)(-0.1 * bits);
@ -563,12 +562,13 @@ static float CombinedShannonEntropy(const int* const X,
int sumX = 0, sumXY = 0;
for (i = 0; i < n; ++i) {
const int x = X[i];
const int xy = X[i] + Y[i];
const int xy = x + Y[i];
if (x != 0) {
sumX += x;
retval -= VP8LFastSLog2(x);
}
if (xy != 0) {
sumXY += xy;
retval -= VP8LFastSLog2(xy);
} else if (xy != 0) {
sumXY += xy;
retval -= VP8LFastSLog2(xy);
}
@ -577,48 +577,53 @@ static float CombinedShannonEntropy(const int* const X,
return (float)retval;
}
static float PredictionCostSpatialHistogram(int accumulated[4][256],
int tile[4][256]) {
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 += PredictionCostSpatial(tile[i], 1, kExpValue, 256);
retval += CombinedShannonEntropy(tile[i], accumulated[i], 256);
}
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];
}
static int GetBestPredictorForTile(int width, int height,
int tile_x, int tile_y, int bits,
int accumulated[4][256],
const int accumulated[4][256],
const uint32_t* const argb_scratch) {
const int kNumPredModes = 14;
const int col_start = tile_x << bits;
const int row_start = tile_y << bits;
const int tile_size = 1 << bits;
const int ymax = GetMin(tile_size, height - row_start);
const int xmax = GetMin(tile_size, width - col_start);
int histo[4][256];
const int max_y = GetMin(tile_size, height - row_start);
const int max_x = GetMin(tile_size, width - col_start);
float best_diff = MAX_DIFF_COST;
int best_mode = 0;
int mode;
for (mode = 0; mode < kNumPredModes; ++mode) {
const uint32_t* current_row = argb_scratch;
const PredictorFunc pred_func = kPredictors[mode];
float cur_diff;
int y;
memset(&histo[0][0], 0, sizeof(histo));
for (y = 0; y < ymax; ++y) {
int histo_argb[4][256];
memset(histo_argb, 0, sizeof(histo_argb));
for (y = 0; y < max_y; ++y) {
int x;
const int row = row_start + y;
const uint32_t* const upper_row = current_row;
current_row = upper_row + width;
for (x = 0; x < xmax; ++x) {
for (x = 0; x < max_x; ++x) {
const int col = col_start + x;
uint32_t predict;
uint32_t predict_diff;
if (row == 0) {
predict = (col == 0) ? ARGB_BLACK : current_row[col - 1]; // Left.
} else if (col == 0) {
@ -626,14 +631,11 @@ static int GetBestPredictorForTile(int width, int height,
} else {
predict = pred_func(current_row[col - 1], upper_row + col);
}
predict_diff = VP8LSubPixels(current_row[col], predict);
++histo[0][predict_diff >> 24];
++histo[1][((predict_diff >> 16) & 0xff)];
++histo[2][((predict_diff >> 8) & 0xff)];
++histo[3][(predict_diff & 0xff)];
UpdateHisto(histo_argb, VP8LSubPixels(current_row[col], predict));
}
}
cur_diff = PredictionCostSpatialHistogram(accumulated, histo);
cur_diff = PredictionCostSpatialHistogram(
accumulated, (const int (*)[256])histo_argb);
if (cur_diff < best_diff) {
best_diff = cur_diff;
best_mode = mode;
@ -650,18 +652,18 @@ static void CopyTileWithPrediction(int width, int height,
const int col_start = tile_x << bits;
const int row_start = tile_y << bits;
const int tile_size = 1 << bits;
const int ymax = GetMin(tile_size, height - row_start);
const int xmax = GetMin(tile_size, width - col_start);
const int max_y = GetMin(tile_size, height - row_start);
const int max_x = GetMin(tile_size, width - col_start);
const PredictorFunc pred_func = kPredictors[mode];
const uint32_t* current_row = argb_scratch;
int y;
for (y = 0; y < ymax; ++y) {
for (y = 0; y < max_y; ++y) {
int x;
const int row = row_start + y;
const uint32_t* const upper_row = current_row;
current_row = upper_row + width;
for (x = 0; x < xmax; ++x) {
for (x = 0; x < max_x; ++x) {
const int col = col_start + x;
const int pix = row * width + col;
uint32_t predict;
@ -707,7 +709,8 @@ void VP8LResidualImage(int width, int height, int bits,
if (all_x_max > width) {
all_x_max = width;
}
pred = GetBestPredictorForTile(width, height, tile_x, tile_y, bits, histo,
pred = GetBestPredictorForTile(width, height, tile_x, tile_y, bits,
(const int (*)[256])histo,
argb_scratch);
image[tile_y * tiles_per_row + tile_x] = 0xff000000u | (pred << 8);
CopyTileWithPrediction(width, height, tile_x, tile_y, bits, pred,
@ -721,11 +724,7 @@ void VP8LResidualImage(int width, int height, int bits,
}
ix = all_y * width + tile_x_offset;
for (all_x = tile_x_offset; all_x < all_x_max; ++all_x, ++ix) {
const uint32_t a = argb[ix];
++histo[0][a >> 24];
++histo[1][((a >> 16) & 0xff)];
++histo[2][((a >> 8) & 0xff)];
++histo[3][(a & 0xff)];
UpdateHisto(histo, argb[ix]);
}
}
}
@ -905,13 +904,13 @@ static float PredictionCostCrossColor(const int accumulated[256],
// Favor small absolute values for PredictionCostSpatial
static const double kExpValue = 2.4;
return CombinedShannonEntropy(counts, accumulated, 256) +
PredictionCostSpatial(counts, 3, kExpValue);
PredictionCostSpatial(counts, 3, kExpValue, 256);
}
static float GetPredictionCostCrossColorRed(
int tile_x_offset, int tile_y_offset, int all_x_max, int all_y_max,
int xsize, Multipliers prev_x, Multipliers prev_y, int green_to_red,
const int* const accumulated_red_histo, const uint32_t* const argb) {
const int accumulated_red_histo[256], const uint32_t* const argb) {
int all_y;
int histo[256] = { 0 };
float cur_diff;
@ -925,7 +924,7 @@ static float GetPredictionCostCrossColorRed(
++histo[TransformColorRed(green_to_red, argb[ix])]; // red.
}
}
cur_diff = PredictionCostCrossColor(&accumulated_red_histo[0], &histo[0]);
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
}
@ -941,7 +940,7 @@ static float GetPredictionCostCrossColorRed(
static void GetBestGreenToRed(
int tile_x_offset, int tile_y_offset, int all_x_max, int all_y_max,
int xsize, Multipliers prev_x, Multipliers prev_y,
const int* const accumulated_red_histo, const uint32_t* const argb,
const int accumulated_red_histo[256], const uint32_t* const argb,
Multipliers* best_tx) {
int min_green_to_red = -64;
int max_green_to_red = 64;
@ -955,13 +954,13 @@ static void GetBestGreenToRed(
if (eval_min) {
cur_diff_min = GetPredictionCostCrossColorRed(
tile_x_offset, tile_y_offset, all_x_max, all_y_max, xsize,
prev_x, prev_y, min_green_to_red, &accumulated_red_histo[0], argb);
prev_x, prev_y, min_green_to_red, accumulated_red_histo, argb);
eval_min = 0;
}
if (eval_max) {
cur_diff_max = GetPredictionCostCrossColorRed(
tile_x_offset, tile_y_offset, all_x_max, all_y_max, xsize,
prev_x, prev_y, max_green_to_red, &accumulated_red_histo[0], argb);
prev_x, prev_y, max_green_to_red, accumulated_red_histo, argb);
eval_max = 0;
}
if (cur_diff_min < cur_diff_max) {
@ -980,7 +979,7 @@ static void GetBestGreenToRed(
static float GetPredictionCostCrossColorBlue(
int tile_x_offset, int tile_y_offset, int all_x_max, int all_y_max,
int xsize, Multipliers prev_x, Multipliers prev_y, int green_to_blue,
int red_to_blue, const int* const accumulated_blue_histo,
int red_to_blue, const int accumulated_blue_histo[256],
const uint32_t* const argb) {
int all_y;
int histo[256] = { 0 };
@ -995,7 +994,7 @@ static float GetPredictionCostCrossColorBlue(
++histo[TransformColorBlue(green_to_blue, red_to_blue, argb[ix])];
}
}
cur_diff = PredictionCostCrossColor(&accumulated_blue_histo[0], &histo[0]);
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
}
@ -1020,7 +1019,7 @@ static float GetPredictionCostCrossColorBlue(
static void GetBestGreenRedToBlue(
int tile_x_offset, int tile_y_offset, int all_x_max, int all_y_max,
int xsize, Multipliers prev_x, Multipliers prev_y, int quality,
const int* const accumulated_blue_histo, const uint32_t* const argb,
const int accumulated_blue_histo[256], const uint32_t* const argb,
Multipliers* best_tx) {
float best_diff = MAX_DIFF_COST;
float cur_diff;
@ -1048,7 +1047,7 @@ static void GetBestGreenRedToBlue(
red_to_blue += step) {
cur_diff = GetPredictionCostCrossColorBlue(
tile_x_offset, tile_y_offset, all_x_max, all_y_max, xsize, prev_x,
prev_y, green_to_blue, red_to_blue, &accumulated_blue_histo[0], argb);
prev_y, green_to_blue, red_to_blue, accumulated_blue_histo, argb);
if (cur_diff < best_diff) {
best_diff = cur_diff;
best_tx->green_to_blue_ = green_to_blue;
@ -1066,8 +1065,8 @@ static Multipliers GetBestColorTransformForTile(
Multipliers prev_x,
Multipliers prev_y,
int quality, int xsize, int ysize,
const int* const accumulated_red_histo,
const int* const accumulated_blue_histo,
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;
@ -1078,10 +1077,10 @@ static Multipliers GetBestColorTransformForTile(
MultipliersClear(&best_tx);
GetBestGreenToRed(tile_x_offset, tile_y_offset, all_x_max, all_y_max, xsize,
prev_x, prev_y, &accumulated_red_histo[0], argb, &best_tx);
prev_x, prev_y, accumulated_red_histo, argb, &best_tx);
GetBestGreenRedToBlue(tile_x_offset, tile_y_offset, all_x_max, all_y_max,
xsize, prev_x, prev_y, quality,
&accumulated_blue_histo[0], argb, &best_tx);
xsize, prev_x, prev_y, quality, accumulated_blue_histo,
argb, &best_tx);
return best_tx;
}
@ -1127,8 +1126,8 @@ void VP8LColorSpaceTransform(int width, int height, int bits, int quality,
prev_x = GetBestColorTransformForTile(tile_x, tile_y, bits,
prev_x, prev_y,
quality, width, height,
&accumulated_red_histo[0],
&accumulated_blue_histo[0],
accumulated_red_histo,
accumulated_blue_histo,
argb);
image[offset] = MultipliersToColorCode(&prev_x);
CopyTileWithColorTransform(width, height, tile_x_offset, tile_y_offset,