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Diffstat (limited to 'src/3rdparty/libwebp/src/enc/predictor_enc.c')
| -rw-r--r-- | src/3rdparty/libwebp/src/enc/predictor_enc.c | 750 |
1 files changed, 750 insertions, 0 deletions
diff --git a/src/3rdparty/libwebp/src/enc/predictor_enc.c b/src/3rdparty/libwebp/src/enc/predictor_enc.c new file mode 100644 index 0000000..0639b74 --- /dev/null +++ b/src/3rdparty/libwebp/src/enc/predictor_enc.c @@ -0,0 +1,750 @@ +// 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_enc.h" + +#define MAX_DIFF_COST (1e30f) + +static const float kSpatialPredictorBias = 15.f; +static const int kPredLowEffort = 11; +static const uint32_t kMaskAlpha = 0xff000000; + +// Mostly used to reduce code size + readability +static WEBP_INLINE int GetMin(int a, int b) { return (a > b) ? b : a; } +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 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); + } +} + +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; +} + +// Stores the difference between the pixel and its prediction in "out". +// In case of a lossy encoding, updates the source image to avoid propagating +// the deviation further to pixels which depend on the current pixel for their +// predictions. +static WEBP_INLINE void GetResidual( + int width, int height, uint32_t* const upper_row, + uint32_t* const current_row, const uint8_t* const max_diffs, int mode, + int x_start, int x_end, int y, int max_quantization, int exact, + int used_subtract_green, uint32_t* const out) { + if (exact) { + PredictBatch(mode, x_start, y, x_end - x_start, current_row, upper_row, + out); + } else { + const VP8LPredictorFunc pred_func = VP8LPredictors[mode]; + int x; + for (x = x_start; x < x_end; ++x) { + uint32_t predict; + uint32_t residual; + if (y == 0) { + predict = (x == 0) ? ARGB_BLACK : current_row[x - 1]; // Left. + } else if (x == 0) { + predict = upper_row[x]; // Top. + } else { + predict = pred_func(current_row[x - 1], upper_row + x); + } + if (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 ((current_row[x] & kMaskAlpha) == 0) { + // If alpha is 0, cleanup RGB. We can choose the RGB values of the + // residual for best compression. The prediction of alpha itself can be + // non-zero and must be kept though. We choose RGB of the residual to be + // 0. + residual &= kMaskAlpha; + // Update the source image. + current_row[x] = predict & ~kMaskAlpha; + // The prediction for the rightmost pixel in a row uses the leftmost + // pixel + // in that row as its top-right context pixel. Hence if we change the + // leftmost pixel of current_row, the corresponding change must be + // applied + // to upper_row as well where top-right context is being read from. + if (x == 0 && y != 0) upper_row[width] = current_row[0]; + } + out[x - x_start] = residual; + } + } +} + +// Returns best predictor and updates the accumulated histogram. +// If max_quantization > 1, assumes that near lossless processing will be +// applied, quantizing residuals to multiples of quantization levels up to +// max_quantization (the actual quantization level depends on smoothness near +// the given pixel). +static int GetBestPredictorForTile(int width, int height, + int tile_x, int tile_y, int bits, + int accumulated[4][256], + uint32_t* const argb_scratch, + const uint32_t* const argb, + int max_quantization, + int exact, int used_subtract_green, + const uint32_t* const modes) { + const int kNumPredModes = 14; + const int start_x = tile_x << bits; + const int start_y = tile_y << bits; + const int tile_size = 1 << bits; + const int max_y = GetMin(tile_size, height - start_y); + const int max_x = GetMin(tile_size, width - start_x); + // Whether there exist columns just outside the tile. + const int have_left = (start_x > 0); + 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; + const int tiles_per_row = VP8LSubSampleSize(width, bits); + // Prediction modes of the left and above neighbor tiles. + const int left_mode = (tile_x > 0) ? + (modes[tile_y * tiles_per_row + tile_x - 1] >> 8) & 0xff : 0xff; + const int above_mode = (tile_y > 0) ? + (modes[(tile_y - 1) * tiles_per_row + tile_x] >> 8) & 0xff : 0xff; + // The width of upper_row and current_row is one pixel larger than image width + // to allow the top right pixel to point to the leftmost pixel of the next row + // when at the right edge. + uint32_t* upper_row = argb_scratch; + uint32_t* current_row = upper_row + width + 1; + uint8_t* const max_diffs = (uint8_t*)(current_row + width + 1); + float best_diff = MAX_DIFF_COST; + int best_mode = 0; + int mode; + int histo_stack_1[4][256]; + int histo_stack_2[4][256]; + // Need pointers to be able to swap arrays. + int (*histo_argb)[256] = histo_stack_1; + int (*best_histo)[256] = histo_stack_2; + int i, j; + uint32_t residuals[1 << MAX_TRANSFORM_BITS]; + assert(bits <= MAX_TRANSFORM_BITS); + assert(max_x <= (1 << MAX_TRANSFORM_BITS)); + + for (mode = 0; mode < kNumPredModes; ++mode) { + float cur_diff; + int relative_y; + memset(histo_argb, 0, sizeof(histo_stack_1)); + if (start_y > 0) { + // Read the row above the tile which will become the first upper_row. + // Include a pixel to the left if it exists; include a pixel to the right + // in all cases (wrapping to the leftmost pixel of the next row if it does + // not exist). + memcpy(current_row + context_start_x, + argb + (start_y - 1) * width + context_start_x, + sizeof(*argb) * (max_x + have_left + 1)); + } + for (relative_y = 0; relative_y < max_y; ++relative_y) { + const int y = start_y + relative_y; + int relative_x; + uint32_t* tmp = upper_row; + upper_row = current_row; + current_row = tmp; + // Read current_row. Include a pixel to the left if it exists; include a + // pixel to the right in all cases except at the bottom right corner of + // the image (wrapping to the leftmost pixel of the next row if it does + // not exist in the current row). + memcpy(current_row + context_start_x, + argb + y * width + context_start_x, + sizeof(*argb) * (max_x + have_left + (y + 1 < height))); + if (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); + } + + GetResidual(width, height, upper_row, current_row, max_diffs, mode, + start_x, start_x + max_x, y, max_quantization, exact, + used_subtract_green, residuals); + for (relative_x = 0; relative_x < max_x; ++relative_x) { + UpdateHisto(histo_argb, residuals[relative_x]); + } + } + cur_diff = PredictionCostSpatialHistogram( + (const int (*)[256])accumulated, (const int (*)[256])histo_argb); + // Favor keeping the areas locally similar. + if (mode == left_mode) cur_diff -= kSpatialPredictorBias; + if (mode == above_mode) cur_diff -= kSpatialPredictorBias; + + if (cur_diff < best_diff) { + int (*tmp)[256] = histo_argb; + histo_argb = best_histo; + best_histo = tmp; + best_diff = cur_diff; + best_mode = mode; + } + } + + for (i = 0; i < 4; i++) { + for (j = 0; j < 256; j++) { + accumulated[i][j] += best_histo[i][j]; + } + } + + return best_mode; +} + +// Converts pixels of the image to residuals with respect to predictions. +// If max_quantization > 1, applies near lossless processing, quantizing +// residuals to multiples of quantization levels up to max_quantization +// (the actual quantization level depends on smoothness near the given pixel). +static void CopyImageWithPrediction(int width, int height, + int bits, uint32_t* const modes, + uint32_t* const argb_scratch, + uint32_t* const argb, + int low_effort, int max_quantization, + int exact, int used_subtract_green) { + const int tiles_per_row = VP8LSubSampleSize(width, bits); + // The width of upper_row and current_row is one pixel larger than image width + // to allow the top right pixel to point to the leftmost pixel of the next row + // when at the right edge. + uint32_t* upper_row = argb_scratch; + uint32_t* current_row = upper_row + width + 1; + uint8_t* current_max_diffs = (uint8_t*)(current_row + width + 1); + uint8_t* lower_max_diffs = current_max_diffs + width; + 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 (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;) { + const int mode = + (modes[(y >> bits) * tiles_per_row + (x >> bits)] >> 8) & 0xff; + int x_end = x + (1 << bits); + if (x_end > width) x_end = width; + GetResidual(width, height, upper_row, current_row, current_max_diffs, + mode, x, x_end, y, max_quantization, exact, + used_subtract_green, argb + y * width + x); + x = x_end; + } + } + } +} + +// Finds the best predictor for each tile, and converts the image to residuals +// with respect to predictions. If near_lossless_quality < 100, applies +// near lossless processing, shaving off more bits of residuals for lower +// qualities. +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); + 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]; + } + } + } + } +} |