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Move some lossless logic out of dsp.

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Change-Id: I4cfd60cd5497666a2e1c188ceada2e71b05f1505
This commit is contained in:
Vincent Rabaud 2016-09-13 14:48:50 +02:00
parent 78363e9e51
commit 6cc48b1728
9 changed files with 729 additions and 696 deletions
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1
Android.mk
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@ -116,6 +116,7 @@ enc_srcs := \
src/enc/picture_psnr.c \ src/enc/picture_psnr.c \
src/enc/picture_rescale.c \ src/enc/picture_rescale.c \
src/enc/picture_tools.c \ src/enc/picture_tools.c \
src/enc/predictor.c \
src/enc/quant.c \ src/enc/quant.c \
src/enc/syntax.c \ src/enc/syntax.c \
src/enc/token.c \ src/enc/token.c \

1
Makefile.vc
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@ -286,6 +286,7 @@ ENC_OBJS = \
$(DIROBJ)\enc\picture_psnr.obj \ $(DIROBJ)\enc\picture_psnr.obj \
$(DIROBJ)\enc\picture_rescale.obj \ $(DIROBJ)\enc\picture_rescale.obj \
$(DIROBJ)\enc\picture_tools.obj \ $(DIROBJ)\enc\picture_tools.obj \
$(DIROBJ)\enc\predictor.obj \
$(DIROBJ)\enc\quant.obj \ $(DIROBJ)\enc\quant.obj \
$(DIROBJ)\enc\syntax.obj \ $(DIROBJ)\enc\syntax.obj \
$(DIROBJ)\enc\token.obj \ $(DIROBJ)\enc\token.obj \

1
build.gradle
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@ -195,6 +195,7 @@ model {
include "picture_psnr.c" include "picture_psnr.c"
include "picture_rescale.c" include "picture_rescale.c"
include "picture_tools.c" include "picture_tools.c"
include "predictor.c"
include "quant.c" include "quant.c"
include "syntax.c" include "syntax.c"
include "token.c" include "token.c"

1
makefile.unix
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@ -208,6 +208,7 @@ ENC_OBJS = \
src/enc/picture_psnr.o \ src/enc/picture_psnr.o \
src/enc/picture_rescale.o \ src/enc/picture_rescale.o \
src/enc/picture_tools.o \ src/enc/picture_tools.o \
src/enc/predictor.o \
src/enc/quant.o \ src/enc/quant.o \
src/enc/syntax.o \ src/enc/syntax.o \
src/enc/token.o \ src/enc/token.o \

11
src/dsp/lossless.h
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@ -136,17 +136,6 @@ void VP8LCollectColorBlueTransforms_C(const uint32_t* argb, int stride,
int green_to_blue, int red_to_blue, int green_to_blue, int red_to_blue,
int histo[]); int histo[]);
//------------------------------------------------------------------------------
// Image transforms.
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, int exact,
int used_subtract_green);
void VP8LColorSpaceTransform(int width, int height, int bits, int quality,
uint32_t* const argb, uint32_t* image);
// ----------------------------------------------------------------------------- // -----------------------------------------------------------------------------
// Huffman-cost related functions. // Huffman-cost related functions.

685
src/dsp/lossless_enc.c
View File

@ -23,11 +23,6 @@
#include "./lossless_common.h" #include "./lossless_common.h"
#include "./yuv.h" #include "./yuv.h"
#define MAX_DIFF_COST (1e30f)
static const int kPredLowEffort = 11;
static const uint32_t kMaskAlpha = 0xff000000;
// lookup table for small values of log2(int) // lookup table for small values of log2(int)
const float kLog2Table[LOG_LOOKUP_IDX_MAX] = { const float kLog2Table[LOG_LOOKUP_IDX_MAX] = {
0.0000000000000000f, 0.0000000000000000f, 0.0000000000000000f, 0.0000000000000000f,
@ -381,26 +376,9 @@ static float FastLog2Slow(uint32_t v) {
} }
} }
// 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). // 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);
}
// Compute the combined Shanon's entropy for distribution {X} and {X+Y} // Compute the combined Shanon's entropy for distribution {X} and {X+Y}
static float CombinedShannonEntropy(const int X[256], const int Y[256]) { static float CombinedShannonEntropy(const int X[256], const int Y[256]) {
int i; int i;
@ -423,18 +401,6 @@ static float CombinedShannonEntropy(const int X[256], const int Y[256]) {
return (float)retval; return (float)retval;
} }
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;
}
void VP8LBitEntropyInit(VP8LBitEntropy* const entropy) { void VP8LBitEntropyInit(VP8LBitEntropy* const entropy) {
entropy->entropy = 0.; entropy->entropy = 0.;
entropy->sum = 0; entropy->sum = 0;
@ -531,395 +497,8 @@ void VP8LGetCombinedEntropyUnrefined(const uint32_t* const X,
bit_entropy->entropy += VP8LFastSLog2(bit_entropy->sum); bit_entropy->entropy += VP8LFastSLog2(bit_entropy->sum);
} }
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 WEBP_INLINE uint32_t Predict(VP8LPredictorFunc pred_func,
int x, int y,
const uint32_t* current_row,
const uint32_t* upper_row) {
if (y == 0) {
return (x == 0) ? ARGB_BLACK : current_row[x - 1]; // Left.
} else if (x == 0) {
return upper_row[x]; // Top.
} else {
return pred_func(current_row[x - 1], upper_row + x);
}
}
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;
}
// Returns the difference between the pixel and its prediction. 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 uint32_t GetResidual(int width, int height,
uint32_t* const upper_row,
uint32_t* const current_row,
const uint8_t* const max_diffs,
int mode, VP8LPredictorFunc pred_func,
int x, int y, int max_quantization,
int exact, int used_subtract_green) {
const uint32_t predict = Predict(pred_func, x, y, current_row, upper_row);
uint32_t residual;
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 (!exact && (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];
}
return 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 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;
// 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;
for (mode = 0; mode < kNumPredModes; ++mode) {
const VP8LPredictorFunc pred_func = VP8LPredictors[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);
}
for (relative_x = 0; relative_x < max_x; ++relative_x) {
const int x = start_x + relative_x;
UpdateHisto(histo_argb,
GetResidual(width, height, upper_row, current_row,
max_diffs, mode, pred_func, x, y,
max_quantization, exact, used_subtract_green));
}
}
cur_diff = PredictionCostSpatialHistogram(
(const int (*)[256])accumulated, (const int (*)[256])histo_argb);
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);
const int mask = (1 << bits) - 1;
// 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;
int mode = 0;
VP8LPredictorFunc pred_func = NULL;
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) {
for (x = 0; x < width; ++x) {
const uint32_t predict = Predict(VP8LPredictors[kPredLowEffort], x, y,
current_row, upper_row);
argb[y * width + x] = VP8LSubPixels(current_row[x], predict);
}
} 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; ++x) {
if ((x & mask) == 0) {
mode = (modes[(y >> bits) * tiles_per_row + (x >> bits)] >> 8) & 0xff;
pred_func = VP8LPredictors[mode];
}
argb[y * width + x] = GetResidual(
width, height, upper_row, current_row, current_max_diffs, mode,
pred_func, x, y, max_quantization, exact, used_subtract_green);
}
}
}
}
// 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[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);
}
void VP8LSubtractGreenFromBlueAndRed_C(uint32_t* argb_data, int num_pixels) { void VP8LSubtractGreenFromBlueAndRed_C(uint32_t* argb_data, int num_pixels) {
int i; int i;
for (i = 0; i < num_pixels; ++i) { for (i = 0; i < num_pixels; ++i) {
@ -931,31 +510,10 @@ void VP8LSubtractGreenFromBlueAndRed_C(uint32_t* argb_data, int num_pixels) {
} }
} }
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 int ColorTransformDelta(int8_t color_pred, int8_t color) { static WEBP_INLINE int ColorTransformDelta(int8_t color_pred, int8_t color) {
return ((int)color_pred * color) >> 5; return ((int)color_pred * color) >> 5;
} }
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_;
}
void VP8LTransformColor_C(const VP8LMultipliers* const m, uint32_t* data, void VP8LTransformColor_C(const VP8LMultipliers* const m, uint32_t* data,
int num_pixels) { int num_pixels) {
int i; int i;
@ -993,15 +551,6 @@ static WEBP_INLINE uint8_t TransformColorBlue(uint8_t green_to_blue,
return (new_blue & 0xff); return (new_blue & 0xff);
} }
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);
}
void VP8LCollectColorRedTransforms_C(const uint32_t* argb, int stride, void VP8LCollectColorRedTransforms_C(const uint32_t* argb, int stride,
int tile_width, int tile_height, int tile_width, int tile_height,
int green_to_red, int histo[]) { int green_to_red, int histo[]) {
@ -1014,59 +563,6 @@ void VP8LCollectColorRedTransforms_C(const uint32_t* argb, int stride,
} }
} }
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;
}
void VP8LCollectColorBlueTransforms_C(const uint32_t* argb, int stride, void VP8LCollectColorBlueTransforms_C(const uint32_t* argb, int stride,
int tile_width, int tile_height, int tile_width, int tile_height,
int green_to_blue, int red_to_blue, int green_to_blue, int red_to_blue,
@ -1080,187 +576,6 @@ void VP8LCollectColorBlueTransforms_C(const uint32_t* argb, int stride,
} }
} }
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];
}
}
}
}
}
//------------------------------------------------------------------------------ //------------------------------------------------------------------------------
static int VectorMismatch(const uint32_t* const array1, static int VectorMismatch(const uint32_t* const array1,

1
src/enc/Makefile.am
View File

@ -21,6 +21,7 @@ libwebpencode_la_SOURCES += picture_csp.c
libwebpencode_la_SOURCES += picture_psnr.c libwebpencode_la_SOURCES += picture_psnr.c
libwebpencode_la_SOURCES += picture_rescale.c libwebpencode_la_SOURCES += picture_rescale.c
libwebpencode_la_SOURCES += picture_tools.c libwebpencode_la_SOURCES += picture_tools.c
libwebpencode_la_SOURCES += predictor.c
libwebpencode_la_SOURCES += quant.c libwebpencode_la_SOURCES += quant.c
libwebpencode_la_SOURCES += syntax.c libwebpencode_la_SOURCES += syntax.c
libwebpencode_la_SOURCES += token.c libwebpencode_la_SOURCES += token.c

713
src/enc/predictor.c Normal file
View File

@ -0,0 +1,713 @@
// 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.h"
#define MAX_DIFF_COST (1e30f)
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 uint32_t Predict(VP8LPredictorFunc pred_func,
int x, int y,
const uint32_t* current_row,
const uint32_t* upper_row) {
if (y == 0) {
return (x == 0) ? ARGB_BLACK : current_row[x - 1]; // Left.
} else if (x == 0) {
return upper_row[x]; // Top.
} else {
return pred_func(current_row[x - 1], upper_row + x);
}
}
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;
}
// Returns the difference between the pixel and its prediction. 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 uint32_t GetResidual(int width, int height,
uint32_t* const upper_row,
uint32_t* const current_row,
const uint8_t* const max_diffs,
int mode, VP8LPredictorFunc pred_func,
int x, int y, int max_quantization,
int exact, int used_subtract_green) {
const uint32_t predict = Predict(pred_func, x, y, current_row, upper_row);
uint32_t residual;
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 (!exact && (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];
}
return 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 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;
// 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;
for (mode = 0; mode < kNumPredModes; ++mode) {
const VP8LPredictorFunc pred_func = VP8LPredictors[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);
}
for (relative_x = 0; relative_x < max_x; ++relative_x) {
const int x = start_x + relative_x;
UpdateHisto(histo_argb,
GetResidual(width, height, upper_row, current_row,
max_diffs, mode, pred_func, x, y,
max_quantization, exact, used_subtract_green));
}
}
cur_diff = PredictionCostSpatialHistogram(
(const int (*)[256])accumulated, (const int (*)[256])histo_argb);
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);
const int mask = (1 << bits) - 1;
// 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;
int mode = 0;
VP8LPredictorFunc pred_func = NULL;
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) {
for (x = 0; x < width; ++x) {
const uint32_t predict = Predict(VP8LPredictors[kPredLowEffort], x, y,
current_row, upper_row);
argb[y * width + x] = VP8LSubPixels(current_row[x], predict);
}
} 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; ++x) {
if ((x & mask) == 0) {
mode = (modes[(y >> bits) * tiles_per_row + (x >> bits)] >> 8) & 0xff;
pred_func = VP8LPredictors[mode];
}
argb[y * width + x] = GetResidual(
width, height, upper_row, current_row, current_max_diffs, mode,
pred_func, x, y, max_quantization, exact, used_subtract_green);
}
}
}
}
// 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[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];
}
}
}
}
}

11
src/enc/vp8li.h
View File

@ -72,6 +72,17 @@ WebPEncodingError VP8LEncodeStream(const WebPConfig* const config,
const WebPPicture* const picture, const WebPPicture* const picture,
VP8LBitWriter* const bw, int use_cache); VP8LBitWriter* const bw, int use_cache);
//------------------------------------------------------------------------------
// Image transforms in predictor.c.
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, int exact,
int used_subtract_green);
void VP8LColorSpaceTransform(int width, int height, int bits, int quality,
uint32_t* const argb, uint32_t* image);
//------------------------------------------------------------------------------ //------------------------------------------------------------------------------
#ifdef __cplusplus #ifdef __cplusplus