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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 <assert.h>
#include <stdlib.h>
#include <string.h>
#include "src/dsp/lossless.h"
#include "src/dsp/lossless_common.h"
#include "src/enc/vp8i_enc.h"
#include "src/enc/vp8li_enc.h"
#include "src/utils/utils.h"
#include "src/webp/encode.h"
#include "src/webp/format_constants.h"
#include "src/webp/types.h"
#define HISTO_SIZE (4 * 256)
static const int64_t kSpatialPredictorBias = 15ll << LOG_2_PRECISION_BITS;
static const int kPredLowEffort = 11;
static const uint32_t kMaskAlpha = 0xff000000;
static const int kNumPredModes = 14;
// 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).
// Compute a bias for prediction entropy using a global heuristic to favor
// values closer to 0. Hence the final negative sign.
// 'exp_val' has a scaling factor of 1/100.
static int64_t PredictionCostBias(const uint32_t counts[256], uint64_t weight_0,
uint64_t exp_val) {
const int significant_symbols = 256 >> 4;
const uint64_t exp_decay_factor = 6; // has a scaling factor of 1/10
uint64_t bits = (weight_0 * counts[0]) << LOG_2_PRECISION_BITS;
int i;
exp_val <<= LOG_2_PRECISION_BITS;
for (i = 1; i < significant_symbols; ++i) {
bits += DivRound(exp_val * (counts[i] + counts[256 - i]), 100);
exp_val = DivRound(exp_decay_factor * exp_val, 10);
}
return -DivRound((int64_t)bits, 10);
}
static int64_t PredictionCostSpatialHistogram(
const uint32_t accumulated[HISTO_SIZE], const uint32_t tile[HISTO_SIZE],
int mode, int left_mode, int above_mode) {
int i;
int64_t retval = 0;
for (i = 0; i < 4; ++i) {
const uint64_t kExpValue = 94;
retval += PredictionCostBias(&tile[i * 256], 1, kExpValue);
// Compute the new cost if 'tile' is added to 'accumulate' but also add the
// cost of the current histogram to guide the spatial predictor selection.
// Basically, favor low entropy, locally and globally.
retval += (int64_t)VP8LCombinedShannonEntropy(&tile[i * 256],
&accumulated[i * 256]);
}
// Favor keeping the areas locally similar.
if (mode == left_mode) retval -= kSpatialPredictorBias;
if (mode == above_mode) retval -= kSpatialPredictorBias;
return retval;
}
static WEBP_INLINE void UpdateHisto(uint32_t histo_argb[HISTO_SIZE],
uint32_t argb) {
++histo_argb[0 * 256 + (argb >> 24)];
++histo_argb[1 * 256 + ((argb >> 16) & 0xff)];
++histo_argb[2 * 256 + ((argb >> 8) & 0xff)];
++histo_argb[3 * 256 + (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 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;
}
}
}
// Accessors to residual histograms.
static WEBP_INLINE uint32_t* GetHistoArgb(uint32_t* const all_histos,
int subsampling_index, int mode) {
return &all_histos[(subsampling_index * kNumPredModes + mode) * HISTO_SIZE];
}
static WEBP_INLINE const uint32_t* GetHistoArgbConst(
const uint32_t* const all_histos, int subsampling_index, int mode) {
return &all_histos[subsampling_index * kNumPredModes * HISTO_SIZE +
mode * HISTO_SIZE];
}
// Accessors to accumulated residual histogram.
static WEBP_INLINE uint32_t* GetAccumulatedHisto(uint32_t* all_accumulated,
int subsampling_index) {
return &all_accumulated[subsampling_index * HISTO_SIZE];
}
// Find and store the best predictor for a tile at subsampling
// 'subsampling_index'.
static void GetBestPredictorForTile(const uint32_t* const all_argb,
int subsampling_index, int tile_x,
int tile_y, int tiles_per_row,
uint32_t* all_accumulated_argb,
uint32_t** const all_modes,
uint32_t* const all_pred_histos) {
uint32_t* const accumulated_argb =
GetAccumulatedHisto(all_accumulated_argb, subsampling_index);
uint32_t* const modes = all_modes[subsampling_index];
uint32_t* const pred_histos =
&all_pred_histos[subsampling_index * kNumPredModes];
// 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;
int mode;
int64_t best_diff = WEBP_INT64_MAX;
uint32_t best_mode = 0;
const uint32_t* best_histo =
GetHistoArgbConst(all_argb, /*subsampling_index=*/0, best_mode);
for (mode = 0; mode < kNumPredModes; ++mode) {
const uint32_t* const histo_argb =
GetHistoArgbConst(all_argb, subsampling_index, mode);
const int64_t cur_diff = PredictionCostSpatialHistogram(
accumulated_argb, histo_argb, mode, left_mode, above_mode);
if (cur_diff < best_diff) {
best_histo = histo_argb;
best_diff = cur_diff;
best_mode = mode;
}
}
// Update the accumulated histogram.
VP8LAddVectorEq(best_histo, accumulated_argb, HISTO_SIZE);
modes[tile_y * tiles_per_row + tile_x] = ARGB_BLACK | (best_mode << 8);
++pred_histos[best_mode];
}
// Computes the residuals for the different predictors.
// 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 void ComputeResidualsForTile(
int width, int height, int tile_x, int tile_y, int min_bits,
uint32_t update_up_to_index, uint32_t* const all_argb,
uint32_t* const argb_scratch, const uint32_t* const argb,
int max_quantization, int exact, int used_subtract_green) {
const int start_x = tile_x << min_bits;
const int start_y = tile_y << min_bits;
const int tile_size = 1 << min_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
// 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);
int mode;
// Need pointers to be able to swap arrays.
uint32_t residuals[1 << MAX_TRANSFORM_BITS];
assert(max_x <= (1 << MAX_TRANSFORM_BITS));
for (mode = 0; mode < kNumPredModes; ++mode) {
int relative_y;
uint32_t* const histo_argb =
GetHistoArgb(all_argb, /*subsampling_index=*/0, mode);
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]);
}
if (update_up_to_index > 0) {
uint32_t subsampling_index;
for (subsampling_index = 1; subsampling_index <= update_up_to_index;
++subsampling_index) {
uint32_t* const super_histo =
GetHistoArgb(all_argb, subsampling_index, mode);
for (relative_x = 0; relative_x < max_x; ++relative_x) {
UpdateHisto(super_histo, residuals[relative_x]);
}
}
}
}
}
}
// 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,
const 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;
}
}
}
}
// Checks whether 'image' can be subsampled by finding the biggest power of 2
// squares (defined by 'best_bits') of uniform value it is made out of.
void VP8LOptimizeSampling(uint32_t* const image, int full_width,
int full_height, int bits, int max_bits,
int* best_bits_out) {
int width = VP8LSubSampleSize(full_width, bits);
int height = VP8LSubSampleSize(full_height, bits);
int old_width, x, y, square_size;
int best_bits = bits;
*best_bits_out = bits;
// Check rows first.
while (best_bits < max_bits) {
const int new_square_size = 1 << (best_bits + 1 - bits);
int is_good = 1;
square_size = 1 << (best_bits - bits);
for (y = 0; y + square_size < height; y += new_square_size) {
// Check the first lines of consecutive line groups.
if (memcmp(&image[y * width], &image[(y + square_size) * width],
width * sizeof(*image)) != 0) {
is_good = 0;
break;
}
}
if (is_good) {
++best_bits;
} else {
break;
}
}
if (best_bits == bits) return;
// Check columns.
while (best_bits > bits) {
int is_good = 1;
square_size = 1 << (best_bits - bits);
for (y = 0; is_good && y < height; ++y) {
for (x = 0; is_good && x < width; x += square_size) {
int i;
for (i = x + 1; i < GetMin(x + square_size, width); ++i) {
if (image[y * width + i] != image[y * width + x]) {
is_good = 0;
break;
}
}
}
}
if (is_good) {
break;
}
--best_bits;
}
if (best_bits == bits) return;
// Subsample the image.
old_width = width;
square_size = 1 << (best_bits - bits);
width = VP8LSubSampleSize(full_width, best_bits);
height = VP8LSubSampleSize(full_height, best_bits);
for (y = 0; y < height; ++y) {
for (x = 0; x < width; ++x) {
image[y * width + x] = image[square_size * (y * old_width + x)];
}
}
*best_bits_out = best_bits;
}
// Computes the best predictor image.
// Finds the best predictors per tile. Once done, finds the best predictor image
// sampling.
// best_bits is set to 0 in case of error.
// The following requires some glossary:
// - a tile is a square of side 2^min_bits pixels.
// - a super-tile of a tile is a square of side 2^bits pixels with bits in
// [min_bits+1, max_bits].
// - the max-tile of a tile is the square of 2^max_bits pixels containing it.
// If this max-tile crosses the border of an image, it is cropped.
// - tile, super-tiles and max_tile are aligned on powers of 2 in the original
// image.
// - coordinates for tile, super-tile, max-tile are respectively named
// tile_x, super_tile_x, max_tile_x at their bit scale.
// - in the max-tile, a tile has local coordinates (local_tile_x, local_tile_y).
// The tiles are processed in the following zigzag order to complete the
// super-tiles as soon as possible:
// 1 2| 5 6
// 3 4| 7 8
// --------------
// 9 10| 13 14
// 11 12| 15 16
// When computing the residuals for a tile, the histogram of the above
// super-tile is updated. If this super-tile is finished, its histogram is used
// to update the histogram of the next super-tile and so on up to the max-tile.
static void GetBestPredictorsAndSubSampling(
int width, int height, const int min_bits, const int max_bits,
uint32_t* const argb_scratch, const uint32_t* const argb,
int max_quantization, int exact, int used_subtract_green,
const WebPPicture* const pic, int percent_range, int* const percent,
uint32_t** const all_modes, int* best_bits, uint32_t** best_mode) {
const uint32_t tiles_per_row = VP8LSubSampleSize(width, min_bits);
const uint32_t tiles_per_col = VP8LSubSampleSize(height, min_bits);
int64_t best_cost;
uint32_t subsampling_index;
const uint32_t max_subsampling_index = max_bits - min_bits;
// Compute the needed memory size for residual histograms, accumulated
// residual histograms and predictor histograms.
const int num_argb = (max_subsampling_index + 1) * kNumPredModes * HISTO_SIZE;
const int num_accumulated_rgb = (max_subsampling_index + 1) * HISTO_SIZE;
const int num_predictors = (max_subsampling_index + 1) * kNumPredModes;
uint32_t* const raw_data = (uint32_t*)WebPSafeCalloc(
num_argb + num_accumulated_rgb + num_predictors, sizeof(uint32_t));
uint32_t* const all_argb = raw_data;
uint32_t* const all_accumulated_argb = all_argb + num_argb;
uint32_t* const all_pred_histos = all_accumulated_argb + num_accumulated_rgb;
const int max_tile_size = 1 << max_subsampling_index; // in tile size
int percent_start = *percent;
// When using the residuals of a tile for its super-tiles, you can either:
// - use each residual to update the histogram of the super-tile, with a cost
// of 4 * (1<<n)^2 increment operations (4 for the number of channels, and
// (1<<n)^2 for the number of pixels in the tile)
// - use the histogram of the tile to update the histogram of the super-tile,
// with a cost of HISTO_SIZE (1024)
// The first method is therefore faster until n==4. 'update_up_to_index'
// defines the maximum subsampling_index for which the residuals should be
// individually added to the super-tile histogram.
const uint32_t update_up_to_index =
GetMax(GetMin(4, max_bits), min_bits) - min_bits;
// Coordinates in the max-tile in tile units.
uint32_t local_tile_x = 0, local_tile_y = 0;
uint32_t max_tile_x = 0, max_tile_y = 0;
uint32_t tile_x = 0, tile_y = 0;
*best_bits = 0;
*best_mode = NULL;
if (raw_data == NULL) return;
while (tile_y < tiles_per_col) {
ComputeResidualsForTile(width, height, tile_x, tile_y, min_bits,
update_up_to_index, all_argb, argb_scratch, argb,
max_quantization, exact, used_subtract_green);
// Update all the super-tiles that are complete.
subsampling_index = 0;
while (1) {
const uint32_t super_tile_x = tile_x >> subsampling_index;
const uint32_t super_tile_y = tile_y >> subsampling_index;
const uint32_t super_tiles_per_row =
VP8LSubSampleSize(width, min_bits + subsampling_index);
GetBestPredictorForTile(all_argb, subsampling_index, super_tile_x,
super_tile_y, super_tiles_per_row,
all_accumulated_argb, all_modes, all_pred_histos);
if (subsampling_index == max_subsampling_index) break;
// Update the following super-tile histogram if it has not been updated
// yet.
++subsampling_index;
if (subsampling_index > update_up_to_index &&
subsampling_index <= max_subsampling_index) {
VP8LAddVectorEq(
GetHistoArgbConst(all_argb, subsampling_index - 1, /*mode=*/0),
GetHistoArgb(all_argb, subsampling_index, /*mode=*/0),
HISTO_SIZE * kNumPredModes);
}
// Check whether the super-tile is not complete (if the smallest tile
// is not at the end of a line/column or at the beginning of a super-tile
// of size (1 << subsampling_index)).
if (!((tile_x == (tiles_per_row - 1) ||
(local_tile_x + 1) % (1 << subsampling_index) == 0) &&
(tile_y == (tiles_per_col - 1) ||
(local_tile_y + 1) % (1 << subsampling_index) == 0))) {
--subsampling_index;
// subsampling_index now is the index of the last finished super-tile.
break;
}
}
// Reset all the histograms belonging to finished tiles.
memset(all_argb, 0,
HISTO_SIZE * kNumPredModes * (subsampling_index + 1) *
sizeof(*all_argb));
if (subsampling_index == max_subsampling_index) {
// If a new max-tile is started.
if (tile_x == (tiles_per_row - 1)) {
max_tile_x = 0;
++max_tile_y;
} else {
++max_tile_x;
}
local_tile_x = 0;
local_tile_y = 0;
} else {
// Proceed with the Z traversal.
uint32_t coord_x = local_tile_x >> subsampling_index;
uint32_t coord_y = local_tile_y >> subsampling_index;
if (tile_x == (tiles_per_row - 1) && coord_x % 2 == 0) {
++coord_y;
} else {
if (coord_x % 2 == 0) {
++coord_x;
} else {
// Z traversal.
++coord_y;
--coord_x;
}
}
local_tile_x = coord_x << subsampling_index;
local_tile_y = coord_y << subsampling_index;
}
tile_x = max_tile_x * max_tile_size + local_tile_x;
tile_y = max_tile_y * max_tile_size + local_tile_y;
if (tile_x == 0 &&
!WebPReportProgress(
pic, percent_start + percent_range * tile_y / tiles_per_col,
percent)) {
WebPSafeFree(raw_data);
return;
}
}
// Figure out the best sampling.
best_cost = WEBP_INT64_MAX;
for (subsampling_index = 0; subsampling_index <= max_subsampling_index;
++subsampling_index) {
int plane;
const uint32_t* const accumulated =
GetAccumulatedHisto(all_accumulated_argb, subsampling_index);
int64_t cost = VP8LShannonEntropy(
&all_pred_histos[subsampling_index * kNumPredModes], kNumPredModes);
for (plane = 0; plane < 4; ++plane) {
cost += VP8LShannonEntropy(&accumulated[plane * 256], 256);
}
if (cost < best_cost) {
best_cost = cost;
*best_bits = min_bits + subsampling_index;
*best_mode = all_modes[subsampling_index];
}
}
WebPSafeFree(raw_data);
VP8LOptimizeSampling(*best_mode, width, height, *best_bits,
MAX_TRANSFORM_BITS, best_bits);
}
// 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 min_bits, int max_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,
int* const best_bits) {
int percent_start = *percent;
const int max_quantization = 1 << VP8LNearLosslessBits(near_lossless_quality);
if (low_effort) {
const int tiles_per_row = VP8LSubSampleSize(width, max_bits);
const int tiles_per_col = VP8LSubSampleSize(height, max_bits);
int i;
for (i = 0; i < tiles_per_row * tiles_per_col; ++i) {
image[i] = ARGB_BLACK | (kPredLowEffort << 8);
}
*best_bits = max_bits;
} else {
// Allocate data to try all samplings from min_bits to max_bits.
int bits;
uint32_t sum_num_pixels = 0u;
uint32_t *modes_raw, *best_mode;
uint32_t* modes[MAX_TRANSFORM_BITS + 1];
uint32_t num_pixels[MAX_TRANSFORM_BITS + 1];
for (bits = min_bits; bits <= max_bits; ++bits) {
const int tiles_per_row = VP8LSubSampleSize(width, bits);
const int tiles_per_col = VP8LSubSampleSize(height, bits);
num_pixels[bits] = tiles_per_row * tiles_per_col;
sum_num_pixels += num_pixels[bits];
}
modes_raw = (uint32_t*)WebPSafeMalloc(sum_num_pixels, sizeof(*modes_raw));
if (modes_raw == NULL) return 0;
// Have modes point to the right global memory modes_raw.
modes[min_bits] = modes_raw;
for (bits = min_bits + 1; bits <= max_bits; ++bits) {
modes[bits] = modes[bits - 1] + num_pixels[bits - 1];
}
// Find the best sampling.
GetBestPredictorsAndSubSampling(
width, height, min_bits, max_bits, argb_scratch, argb, max_quantization,
exact, used_subtract_green, pic, percent_range, percent,
&modes[min_bits], best_bits, &best_mode);
if (*best_bits == 0) {
WebPSafeFree(modes_raw);
return 0;
}
// Keep the best predictor image.
memcpy(image, best_mode,
VP8LSubSampleSize(width, *best_bits) *
VP8LSubSampleSize(height, *best_bits) * sizeof(*image));
WebPSafeFree(modes_raw);
}
CopyImageWithPrediction(width, height, *best_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 int64_t PredictionCostCrossColor(const uint32_t accumulated[256],
const uint32_t counts[256]) {
// Favor low entropy, locally and globally.
// Favor small absolute values for PredictionCostSpatial
static const uint64_t kExpValue = 240;
return (int64_t)VP8LCombinedShannonEntropy(counts, accumulated) +
PredictionCostBias(counts, 3, kExpValue);
}
static int64_t GetPredictionCostCrossColorRed(
const uint32_t* argb, int stride, int tile_width, int tile_height,
VP8LMultipliers prev_x, VP8LMultipliers prev_y, int green_to_red,
const uint32_t accumulated_red_histo[256]) {
uint32_t histo[256] = { 0 };
int64_t 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) {
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if ((uint8_t)green_to_red == prev_y.green_to_red) {
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if (green_to_red == 0) {
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
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 uint32_t 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;
int64_t 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 int64_t 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 int64_t 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 uint32_t accumulated_blue_histo[256]) {
uint32_t histo[256] = { 0 };
int64_t 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) {
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if ((uint8_t)green_to_blue == prev_y.green_to_blue) {
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if ((uint8_t)red_to_blue == prev_x.red_to_blue) {
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if ((uint8_t)red_to_blue == prev_y.red_to_blue) {
// favor keeping the areas locally similar
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if (green_to_blue == 0) {
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
if (red_to_blue == 0) {
cur_diff -= 3ll << LOG_2_PRECISION_BITS;
}
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 uint32_t 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:
int64_t 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 int64_t 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 uint32_t accumulated_red_histo[256],
const uint32_t 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, int* const best_bits) {
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;
uint32_t accumulated_red_histo[256] = { 0 };
uint32_t 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;
}
}
VP8LOptimizeSampling(image, width, height, bits, MAX_TRANSFORM_BITS,
best_bits);
return 1;
}