751 lines
30 KiB
C
751 lines
30 KiB
C
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// Copyright 2016 Google Inc. All Rights Reserved.
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//
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// Use of this source code is governed by a BSD-style license
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// that can be found in the COPYING file in the root of the source
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// tree. An additional intellectual property rights grant can be found
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// in the file PATENTS. All contributing project authors may
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// be found in the AUTHORS file in the root of the source tree.
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// -----------------------------------------------------------------------------
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//
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// Image transform methods for lossless encoder.
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//
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// Authors: Vikas Arora (vikaas.arora@gmail.com)
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// Jyrki Alakuijala (jyrki@google.com)
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// Urvang Joshi (urvang@google.com)
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// Vincent Rabaud (vrabaud@google.com)
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#include "../dsp/lossless.h"
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#include "../dsp/lossless_common.h"
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#include "./vp8li_enc.h"
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#define MAX_DIFF_COST (1e30f)
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static const float kSpatialPredictorBias = 15.f;
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static const int kPredLowEffort = 11;
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static const uint32_t kMaskAlpha = 0xff000000;
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// Mostly used to reduce code size + readability
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static WEBP_INLINE int GetMin(int a, int b) { return (a > b) ? b : a; }
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static WEBP_INLINE int GetMax(int a, int b) { return (a < b) ? b : a; }
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//------------------------------------------------------------------------------
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// Methods to calculate Entropy (Shannon).
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static float PredictionCostSpatial(const int counts[256], int weight_0,
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double exp_val) {
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const int significant_symbols = 256 >> 4;
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const double exp_decay_factor = 0.6;
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double bits = weight_0 * counts[0];
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int i;
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for (i = 1; i < significant_symbols; ++i) {
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bits += exp_val * (counts[i] + counts[256 - i]);
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exp_val *= exp_decay_factor;
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}
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return (float)(-0.1 * bits);
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}
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static float PredictionCostSpatialHistogram(const int accumulated[4][256],
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const int tile[4][256]) {
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int i;
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double retval = 0;
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for (i = 0; i < 4; ++i) {
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const double kExpValue = 0.94;
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retval += PredictionCostSpatial(tile[i], 1, kExpValue);
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retval += VP8LCombinedShannonEntropy(tile[i], accumulated[i]);
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}
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return (float)retval;
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}
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static WEBP_INLINE void UpdateHisto(int histo_argb[4][256], uint32_t argb) {
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++histo_argb[0][argb >> 24];
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++histo_argb[1][(argb >> 16) & 0xff];
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++histo_argb[2][(argb >> 8) & 0xff];
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++histo_argb[3][argb & 0xff];
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}
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//------------------------------------------------------------------------------
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// Spatial transform functions.
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static WEBP_INLINE void PredictBatch(int mode, int x_start, int y,
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int num_pixels, const uint32_t* current,
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const uint32_t* upper, uint32_t* out) {
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if (x_start == 0) {
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if (y == 0) {
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// ARGB_BLACK.
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VP8LPredictorsSub[0](current, NULL, 1, out);
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} else {
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// Top one.
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VP8LPredictorsSub[2](current, upper, 1, out);
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}
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++x_start;
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++out;
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--num_pixels;
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}
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if (y == 0) {
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// Left one.
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VP8LPredictorsSub[1](current + x_start, NULL, num_pixels, out);
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} else {
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VP8LPredictorsSub[mode](current + x_start, upper + x_start, num_pixels,
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out);
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}
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}
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static int MaxDiffBetweenPixels(uint32_t p1, uint32_t p2) {
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const int diff_a = abs((int)(p1 >> 24) - (int)(p2 >> 24));
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const int diff_r = abs((int)((p1 >> 16) & 0xff) - (int)((p2 >> 16) & 0xff));
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const int diff_g = abs((int)((p1 >> 8) & 0xff) - (int)((p2 >> 8) & 0xff));
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const int diff_b = abs((int)(p1 & 0xff) - (int)(p2 & 0xff));
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return GetMax(GetMax(diff_a, diff_r), GetMax(diff_g, diff_b));
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}
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static int MaxDiffAroundPixel(uint32_t current, uint32_t up, uint32_t down,
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uint32_t left, uint32_t right) {
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const int diff_up = MaxDiffBetweenPixels(current, up);
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const int diff_down = MaxDiffBetweenPixels(current, down);
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const int diff_left = MaxDiffBetweenPixels(current, left);
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const int diff_right = MaxDiffBetweenPixels(current, right);
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return GetMax(GetMax(diff_up, diff_down), GetMax(diff_left, diff_right));
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}
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static uint32_t AddGreenToBlueAndRed(uint32_t argb) {
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const uint32_t green = (argb >> 8) & 0xff;
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uint32_t red_blue = argb & 0x00ff00ffu;
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red_blue += (green << 16) | green;
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red_blue &= 0x00ff00ffu;
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return (argb & 0xff00ff00u) | red_blue;
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}
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static void MaxDiffsForRow(int width, int stride, const uint32_t* const argb,
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uint8_t* const max_diffs, int used_subtract_green) {
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uint32_t current, up, down, left, right;
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int x;
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if (width <= 2) return;
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current = argb[0];
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right = argb[1];
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if (used_subtract_green) {
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current = AddGreenToBlueAndRed(current);
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right = AddGreenToBlueAndRed(right);
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}
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// max_diffs[0] and max_diffs[width - 1] are never used.
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for (x = 1; x < width - 1; ++x) {
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up = argb[-stride + x];
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down = argb[stride + x];
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left = current;
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current = right;
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right = argb[x + 1];
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if (used_subtract_green) {
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up = AddGreenToBlueAndRed(up);
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down = AddGreenToBlueAndRed(down);
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right = AddGreenToBlueAndRed(right);
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}
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max_diffs[x] = MaxDiffAroundPixel(current, up, down, left, right);
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}
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}
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// Quantize the difference between the actual component value and its prediction
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// to a multiple of quantization, working modulo 256, taking care not to cross
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// a boundary (inclusive upper limit).
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static uint8_t NearLosslessComponent(uint8_t value, uint8_t predict,
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uint8_t boundary, int quantization) {
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const int residual = (value - predict) & 0xff;
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const int boundary_residual = (boundary - predict) & 0xff;
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const int lower = residual & ~(quantization - 1);
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const int upper = lower + quantization;
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// Resolve ties towards a value closer to the prediction (i.e. towards lower
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// if value comes after prediction and towards upper otherwise).
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const int bias = ((boundary - value) & 0xff) < boundary_residual;
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if (residual - lower < upper - residual + bias) {
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// lower is closer to residual than upper.
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if (residual > boundary_residual && lower <= boundary_residual) {
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// Halve quantization step to avoid crossing boundary. This midpoint is
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// on the same side of boundary as residual because midpoint >= residual
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// (since lower is closer than upper) and residual is above the boundary.
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return lower + (quantization >> 1);
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}
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return lower;
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} else {
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// upper is closer to residual than lower.
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if (residual <= boundary_residual && upper > boundary_residual) {
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// Halve quantization step to avoid crossing boundary. This midpoint is
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// on the same side of boundary as residual because midpoint <= residual
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// (since upper is closer than lower) and residual is below the boundary.
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return lower + (quantization >> 1);
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}
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return upper & 0xff;
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}
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}
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// Quantize every component of the difference between the actual pixel value and
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// its prediction to a multiple of a quantization (a power of 2, not larger than
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// max_quantization which is a power of 2, smaller than max_diff). Take care if
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// value and predict have undergone subtract green, which means that red and
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// blue are represented as offsets from green.
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static uint32_t NearLossless(uint32_t value, uint32_t predict,
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int max_quantization, int max_diff,
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int used_subtract_green) {
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int quantization;
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uint8_t new_green = 0;
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uint8_t green_diff = 0;
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uint8_t a, r, g, b;
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if (max_diff <= 2) {
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return VP8LSubPixels(value, predict);
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}
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quantization = max_quantization;
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while (quantization >= max_diff) {
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quantization >>= 1;
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}
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if ((value >> 24) == 0 || (value >> 24) == 0xff) {
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// Preserve transparency of fully transparent or fully opaque pixels.
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a = ((value >> 24) - (predict >> 24)) & 0xff;
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} else {
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a = NearLosslessComponent(value >> 24, predict >> 24, 0xff, quantization);
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}
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g = NearLosslessComponent((value >> 8) & 0xff, (predict >> 8) & 0xff, 0xff,
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quantization);
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if (used_subtract_green) {
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// The green offset will be added to red and blue components during decoding
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// to obtain the actual red and blue values.
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new_green = ((predict >> 8) + g) & 0xff;
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// The amount by which green has been adjusted during quantization. It is
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// subtracted from red and blue for compensation, to avoid accumulating two
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// quantization errors in them.
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green_diff = (new_green - (value >> 8)) & 0xff;
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}
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r = NearLosslessComponent(((value >> 16) - green_diff) & 0xff,
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(predict >> 16) & 0xff, 0xff - new_green,
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quantization);
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b = NearLosslessComponent((value - green_diff) & 0xff, predict & 0xff,
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0xff - new_green, quantization);
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return ((uint32_t)a << 24) | ((uint32_t)r << 16) | ((uint32_t)g << 8) | b;
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}
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// Stores the difference between the pixel and its prediction in "out".
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// In case of a lossy encoding, updates the source image to avoid propagating
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// the deviation further to pixels which depend on the current pixel for their
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// predictions.
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static WEBP_INLINE void GetResidual(
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int width, int height, uint32_t* const upper_row,
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uint32_t* const current_row, const uint8_t* const max_diffs, int mode,
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int x_start, int x_end, int y, int max_quantization, int exact,
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int used_subtract_green, uint32_t* const out) {
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if (exact) {
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PredictBatch(mode, x_start, y, x_end - x_start, current_row, upper_row,
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out);
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} else {
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const VP8LPredictorFunc pred_func = VP8LPredictors[mode];
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int x;
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for (x = x_start; x < x_end; ++x) {
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uint32_t predict;
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uint32_t residual;
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if (y == 0) {
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predict = (x == 0) ? ARGB_BLACK : current_row[x - 1]; // Left.
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} else if (x == 0) {
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predict = upper_row[x]; // Top.
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} else {
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predict = pred_func(current_row[x - 1], upper_row + x);
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}
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if (max_quantization == 1 || mode == 0 || y == 0 || y == height - 1 ||
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x == 0 || x == width - 1) {
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residual = VP8LSubPixels(current_row[x], predict);
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} else {
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residual = NearLossless(current_row[x], predict, max_quantization,
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max_diffs[x], used_subtract_green);
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// Update the source image.
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current_row[x] = VP8LAddPixels(predict, residual);
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// x is never 0 here so we do not need to update upper_row like below.
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}
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if ((current_row[x] & kMaskAlpha) == 0) {
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// If alpha is 0, cleanup RGB. We can choose the RGB values of the
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// residual for best compression. The prediction of alpha itself can be
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// non-zero and must be kept though. We choose RGB of the residual to be
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// 0.
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residual &= kMaskAlpha;
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// Update the source image.
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current_row[x] = predict & ~kMaskAlpha;
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// The prediction for the rightmost pixel in a row uses the leftmost
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// pixel
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// in that row as its top-right context pixel. Hence if we change the
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// leftmost pixel of current_row, the corresponding change must be
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// applied
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// to upper_row as well where top-right context is being read from.
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if (x == 0 && y != 0) upper_row[width] = current_row[0];
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}
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out[x - x_start] = residual;
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}
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}
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}
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// Returns best predictor and updates the accumulated histogram.
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// If max_quantization > 1, assumes that near lossless processing will be
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// applied, quantizing residuals to multiples of quantization levels up to
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// max_quantization (the actual quantization level depends on smoothness near
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// the given pixel).
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static int GetBestPredictorForTile(int width, int height,
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int tile_x, int tile_y, int bits,
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int accumulated[4][256],
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uint32_t* const argb_scratch,
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const uint32_t* const argb,
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int max_quantization,
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int exact, int used_subtract_green,
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const uint32_t* const modes) {
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const int kNumPredModes = 14;
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const int start_x = tile_x << bits;
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const int start_y = tile_y << bits;
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const int tile_size = 1 << bits;
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const int max_y = GetMin(tile_size, height - start_y);
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const int max_x = GetMin(tile_size, width - start_x);
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// Whether there exist columns just outside the tile.
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const int have_left = (start_x > 0);
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const int have_right = (max_x < width - start_x);
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// Position and size of the strip covering the tile and adjacent columns if
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// they exist.
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const int context_start_x = start_x - have_left;
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const int context_width = max_x + have_left + have_right;
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const int tiles_per_row = VP8LSubSampleSize(width, bits);
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// Prediction modes of the left and above neighbor tiles.
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const int left_mode = (tile_x > 0) ?
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(modes[tile_y * tiles_per_row + tile_x - 1] >> 8) & 0xff : 0xff;
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const int above_mode = (tile_y > 0) ?
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(modes[(tile_y - 1) * tiles_per_row + tile_x] >> 8) & 0xff : 0xff;
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// The width of upper_row and current_row is one pixel larger than image width
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// to allow the top right pixel to point to the leftmost pixel of the next row
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// when at the right edge.
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uint32_t* upper_row = argb_scratch;
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uint32_t* current_row = upper_row + width + 1;
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uint8_t* const max_diffs = (uint8_t*)(current_row + width + 1);
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float best_diff = MAX_DIFF_COST;
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int best_mode = 0;
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int mode;
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int histo_stack_1[4][256];
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int histo_stack_2[4][256];
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// Need pointers to be able to swap arrays.
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int (*histo_argb)[256] = histo_stack_1;
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int (*best_histo)[256] = histo_stack_2;
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int i, j;
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|
|
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];
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|