// Copyright 2012 Google Inc. All Rights Reserved. // // This code is licensed under the same terms as WebM: // Software License Agreement: http://www.webmproject.org/license/software/ // Additional IP Rights Grant: http://www.webmproject.org/license/additional/ // ----------------------------------------------------------------------------- // // Image transforms and color space conversion methods for lossless decoder. // // Authors: Vikas Arora (vikaas.arora@gmail.com) // Jyrki Alakuijala (jyrki@google.com) // Urvang Joshi (urvang@google.com) #if defined(__cplusplus) || defined(c_plusplus) extern "C" { #endif #include #include #include "./lossless.h" #include "../dec/vp8li.h" #include "../dsp/yuv.h" #include "../dsp/dsp.h" #include "../enc/histogram.h" #define MAX_DIFF_COST (1e30f) // lookup table for small values of log2(int) #define APPROX_LOG_MAX 4096 #define LOG_2_RECIPROCAL 1.44269504088896338700465094007086 #define LOG_LOOKUP_IDX_MAX 256 static const float kLog2Table[LOG_LOOKUP_IDX_MAX] = { 0.0000000000000000f, 0.0000000000000000f, 1.0000000000000000f, 1.5849625007211560f, 2.0000000000000000f, 2.3219280948873621f, 2.5849625007211560f, 2.8073549220576041f, 3.0000000000000000f, 3.1699250014423121f, 3.3219280948873621f, 3.4594316186372973f, 3.5849625007211560f, 3.7004397181410921f, 3.8073549220576041f, 3.9068905956085187f, 4.0000000000000000f, 4.0874628412503390f, 4.1699250014423121f, 4.2479275134435852f, 4.3219280948873626f, 4.3923174227787606f, 4.4594316186372973f, 4.5235619560570130f, 4.5849625007211560f, 4.6438561897747243f, 4.7004397181410917f, 4.7548875021634682f, 4.8073549220576037f, 4.8579809951275718f, 4.9068905956085187f, 4.9541963103868749f, 5.0000000000000000f, 5.0443941193584533f, 5.0874628412503390f, 5.1292830169449663f, 5.1699250014423121f, 5.2094533656289501f, 5.2479275134435852f, 5.2854022188622487f, 5.3219280948873626f, 5.3575520046180837f, 5.3923174227787606f, 5.4262647547020979f, 5.4594316186372973f, 5.4918530963296747f, 5.5235619560570130f, 5.5545888516776376f, 5.5849625007211560f, 5.6147098441152083f, 5.6438561897747243f, 5.6724253419714951f, 5.7004397181410917f, 5.7279204545631987f, 5.7548875021634682f, 5.7813597135246599f, 5.8073549220576037f, 5.8328900141647412f, 5.8579809951275718f, 5.8826430493618415f, 5.9068905956085187f, 5.9307373375628866f, 5.9541963103868749f, 5.9772799234999167f, 6.0000000000000000f, 6.0223678130284543f, 6.0443941193584533f, 6.0660891904577720f, 6.0874628412503390f, 6.1085244567781691f, 6.1292830169449663f, 6.1497471195046822f, 6.1699250014423121f, 6.1898245588800175f, 6.2094533656289501f, 6.2288186904958804f, 6.2479275134435852f, 6.2667865406949010f, 6.2854022188622487f, 6.3037807481771030f, 6.3219280948873626f, 6.3398500028846243f, 6.3575520046180837f, 6.3750394313469245f, 6.3923174227787606f, 6.4093909361377017f, 6.4262647547020979f, 6.4429434958487279f, 6.4594316186372973f, 6.4757334309663976f, 6.4918530963296747f, 6.5077946401986963f, 6.5235619560570130f, 6.5391588111080309f, 6.5545888516776376f, 6.5698556083309478f, 6.5849625007211560f, 6.5999128421871278f, 6.6147098441152083f, 6.6293566200796094f, 6.6438561897747243f, 6.6582114827517946f, 6.6724253419714951f, 6.6865005271832185f, 6.7004397181410917f, 6.7142455176661224f, 6.7279204545631987f, 6.7414669864011464f, 6.7548875021634682f, 6.7681843247769259f, 6.7813597135246599f, 6.7944158663501061f, 6.8073549220576037f, 6.8201789624151878f, 6.8328900141647412f, 6.8454900509443747f, 6.8579809951275718f, 6.8703647195834047f, 6.8826430493618415f, 6.8948177633079437f, 6.9068905956085187f, 6.9188632372745946f, 6.9307373375628866f, 6.9425145053392398f, 6.9541963103868749f, 6.9657842846620869f, 6.9772799234999167f, 6.9886846867721654f, 7.0000000000000000f, 7.0112272554232539f, 7.0223678130284543f, 7.0334230015374501f, 7.0443941193584533f, 7.0552824355011898f, 7.0660891904577720f, 7.0768155970508308f, 7.0874628412503390f, 7.0980320829605263f, 7.1085244567781691f, 7.1189410727235076f, 7.1292830169449663f, 7.1395513523987936f, 7.1497471195046822f, 7.1598713367783890f, 7.1699250014423121f, 7.1799090900149344f, 7.1898245588800175f, 7.1996723448363644f, 7.2094533656289501f, 7.2191685204621611f, 7.2288186904958804f, 7.2384047393250785f, 7.2479275134435852f, 7.2573878426926521f, 7.2667865406949010f, 7.2761244052742375f, 7.2854022188622487f, 7.2946207488916270f, 7.3037807481771030f, 7.3128829552843557f, 7.3219280948873626f, 7.3309168781146167f, 7.3398500028846243f, 7.3487281542310771f, 7.3575520046180837f, 7.3663222142458160f, 7.3750394313469245f, 7.3837042924740519f, 7.3923174227787606f, 7.4008794362821843f, 7.4093909361377017f, 7.4178525148858982f, 7.4262647547020979f, 7.4346282276367245f, 7.4429434958487279f, 7.4512111118323289f, 7.4594316186372973f, 7.4676055500829976f, 7.4757334309663976f, 7.4838157772642563f, 7.4918530963296747f, 7.4998458870832056f, 7.5077946401986963f, 7.5156998382840427f, 7.5235619560570130f, 7.5313814605163118f, 7.5391588111080309f, 7.5468944598876364f, 7.5545888516776376f, 7.5622424242210728f, 7.5698556083309478f, 7.5774288280357486f, 7.5849625007211560f, 7.5924570372680806f, 7.5999128421871278f, 7.6073303137496104f, 7.6147098441152083f, 7.6220518194563764f, 7.6293566200796094f, 7.6366246205436487f, 7.6438561897747243f, 7.6510516911789281f, 7.6582114827517946f, 7.6653359171851764f, 7.6724253419714951f, 7.6794800995054464f, 7.6865005271832185f, 7.6934869574993252f, 7.7004397181410917f, 7.7073591320808825f, 7.7142455176661224f, 7.7210991887071855f, 7.7279204545631987f, 7.7347096202258383f, 7.7414669864011464f, 7.7481928495894605f, 7.7548875021634682f, 7.7615512324444795f, 7.7681843247769259f, 7.7747870596011736f, 7.7813597135246599f, 7.7879025593914317f, 7.7944158663501061f, 7.8008998999203047f, 7.8073549220576037f, 7.8137811912170374f, 7.8201789624151878f, 7.8265484872909150f, 7.8328900141647412f, 7.8392037880969436f, 7.8454900509443747f, 7.8517490414160571f, 7.8579809951275718f, 7.8641861446542797f, 7.8703647195834047f, 7.8765169465649993f, 7.8826430493618415f, 7.8887432488982591f, 7.8948177633079437f, 7.9008668079807486f, 7.9068905956085187f, 7.9128893362299619f, 7.9188632372745946f, 7.9248125036057812f, 7.9307373375628866f, 7.9366379390025709f, 7.9425145053392398f, 7.9483672315846778f, 7.9541963103868749f, 7.9600019320680805f, 7.9657842846620869f, 7.9715435539507719f, 7.9772799234999167f, 7.9829935746943103f, 7.9886846867721654f, 7.9943534368588577f }; float VP8LFastLog2(int v) { if (v < LOG_LOOKUP_IDX_MAX) { return kLog2Table[v]; } else if (v < APPROX_LOG_MAX) { int log_cnt = 0; while (v >= LOG_LOOKUP_IDX_MAX) { ++log_cnt; v = v >> 1; } return kLog2Table[v] + (float)log_cnt; } else { return (float)(LOG_2_RECIPROCAL * log((double)v)); } } //------------------------------------------------------------------------------ // Image transforms. // In-place sum of each component with mod 256. static WEBP_INLINE void AddPixelsEq(uint32_t* a, uint32_t b) { const uint32_t alpha_and_green = (*a & 0xff00ff00u) + (b & 0xff00ff00u); const uint32_t red_and_blue = (*a & 0x00ff00ffu) + (b & 0x00ff00ffu); *a = (alpha_and_green & 0xff00ff00u) | (red_and_blue & 0x00ff00ffu); } static WEBP_INLINE uint32_t Average2(uint32_t a0, uint32_t a1) { return (((a0 ^ a1) & 0xfefefefeL) >> 1) + (a0 & a1); } static WEBP_INLINE uint32_t Average3(uint32_t a0, uint32_t a1, uint32_t a2) { return Average2(Average2(a0, a2), a1); } static WEBP_INLINE uint32_t Average4(uint32_t a0, uint32_t a1, uint32_t a2, uint32_t a3) { return Average2(Average2(a0, a1), Average2(a2, a3)); } static WEBP_INLINE uint32_t Clip255(uint32_t a) { if (a < 256) { return a; } // return 0, when a is a negative integer. // return 255, when a is positive. return ~a >> 24; } static WEBP_INLINE int AddSubtractComponentFull(int a, int b, int c) { return Clip255(a + b - c); } static WEBP_INLINE uint32_t ClampedAddSubtractFull(uint32_t c0, uint32_t c1, uint32_t c2) { const int a = AddSubtractComponentFull(c0 >> 24, c1 >> 24, c2 >> 24); const int r = AddSubtractComponentFull((c0 >> 16) & 0xff, (c1 >> 16) & 0xff, (c2 >> 16) & 0xff); const int g = AddSubtractComponentFull((c0 >> 8) & 0xff, (c1 >> 8) & 0xff, (c2 >> 8) & 0xff); const int b = AddSubtractComponentFull(c0 & 0xff, c1 & 0xff, c2 & 0xff); return (a << 24) | (r << 16) | (g << 8) | b; } static WEBP_INLINE int AddSubtractComponentHalf(int a, int b) { return Clip255(a + (a - b) / 2); } static WEBP_INLINE uint32_t ClampedAddSubtractHalf(uint32_t c0, uint32_t c1, uint32_t c2) { const uint32_t ave = Average2(c0, c1); const int a = AddSubtractComponentHalf(ave >> 24, c2 >> 24); const int r = AddSubtractComponentHalf((ave >> 16) & 0xff, (c2 >> 16) & 0xff); const int g = AddSubtractComponentHalf((ave >> 8) & 0xff, (c2 >> 8) & 0xff); const int b = AddSubtractComponentHalf((ave >> 0) & 0xff, (c2 >> 0) & 0xff); return (a << 24) | (r << 16) | (g << 8) | b; } static WEBP_INLINE int Sub3(int a, int b, int c) { const int pa = b - c; const int pb = a - c; return abs(pa) - abs(pb); } static WEBP_INLINE uint32_t Select(uint32_t a, uint32_t b, uint32_t c) { const int pa_minus_pb = Sub3((a >> 24) , (b >> 24) , (c >> 24) ) + Sub3((a >> 16) & 0xff, (b >> 16) & 0xff, (c >> 16) & 0xff) + Sub3((a >> 8) & 0xff, (b >> 8) & 0xff, (c >> 8) & 0xff) + Sub3((a ) & 0xff, (b ) & 0xff, (c ) & 0xff); return (pa_minus_pb <= 0) ? a : b; } //------------------------------------------------------------------------------ // Predictors static uint32_t Predictor0(uint32_t left, const uint32_t* const top) { (void)top; (void)left; return ARGB_BLACK; } static uint32_t Predictor1(uint32_t left, const uint32_t* const top) { (void)top; return left; } static uint32_t Predictor2(uint32_t left, const uint32_t* const top) { (void)left; return top[0]; } static uint32_t Predictor3(uint32_t left, const uint32_t* const top) { (void)left; return top[1]; } static uint32_t Predictor4(uint32_t left, const uint32_t* const top) { (void)left; return top[-1]; } static uint32_t Predictor5(uint32_t left, const uint32_t* const top) { const uint32_t pred = Average3(left, top[0], top[1]); return pred; } static uint32_t Predictor6(uint32_t left, const uint32_t* const top) { const uint32_t pred = Average2(left, top[-1]); return pred; } static uint32_t Predictor7(uint32_t left, const uint32_t* const top) { const uint32_t pred = Average2(left, top[0]); return pred; } static uint32_t Predictor8(uint32_t left, const uint32_t* const top) { const uint32_t pred = Average2(top[-1], top[0]); (void)left; return pred; } static uint32_t Predictor9(uint32_t left, const uint32_t* const top) { const uint32_t pred = Average2(top[0], top[1]); (void)left; return pred; } static uint32_t Predictor10(uint32_t left, const uint32_t* const top) { const uint32_t pred = Average4(left, top[-1], top[0], top[1]); return pred; } static uint32_t Predictor11(uint32_t left, const uint32_t* const top) { const uint32_t pred = Select(top[0], left, top[-1]); return pred; } static uint32_t Predictor12(uint32_t left, const uint32_t* const top) { const uint32_t pred = ClampedAddSubtractFull(left, top[0], top[-1]); return pred; } static uint32_t Predictor13(uint32_t left, const uint32_t* const top) { const uint32_t pred = ClampedAddSubtractHalf(left, top[0], top[-1]); return pred; } typedef uint32_t (*PredictorFunc)(uint32_t left, const uint32_t* const top); static const PredictorFunc kPredictors[16] = { Predictor0, Predictor1, Predictor2, Predictor3, Predictor4, Predictor5, Predictor6, Predictor7, Predictor8, Predictor9, Predictor10, Predictor11, Predictor12, Predictor13, Predictor0, Predictor0 // <- padding security sentinels }; // TODO(vikasa): Replace 256 etc with defines. static float PredictionCostSpatial(const int* counts, int weight_0, double exp_val) { const int significant_symbols = 16; const double exp_decay_factor = 0.6; double bits = weight_0 * counts[0]; int i; for (i = 1; i < significant_symbols; ++i) { bits += exp_val * (counts[i] + counts[256 - i]); exp_val *= exp_decay_factor; } return (float)(-0.1 * bits); } // Compute the Shanon's entropy: Sum(p*log2(p)) static float ShannonEntropy(const int* const array, int n) { int i; float retval = 0.f; int sum = 0; for (i = 0; i < n; ++i) { if (array[i] != 0) { sum += array[i]; retval -= VP8LFastSLog2(array[i]); } } retval += VP8LFastSLog2(sum); return retval; } static float PredictionCostSpatialHistogram(int accumulated[4][256], int tile[4][256]) { int i; int k; int combo[256]; double retval = 0; for (i = 0; i < 4; ++i) { const double exp_val = 0.94; retval += PredictionCostSpatial(&tile[i][0], 1, exp_val); retval += ShannonEntropy(&tile[i][0], 256); for (k = 0; k < 256; ++k) { combo[k] = accumulated[i][k] + tile[i][k]; } retval += ShannonEntropy(&combo[0], 256); } return (float)retval; } static int GetBestPredictorForTile(int width, int height, int tile_x, int tile_y, int bits, int accumulated[4][256], const uint32_t* const argb_scratch) { const int kNumPredModes = 14; const int col_start = tile_x << bits; const int row_start = tile_y << bits; const int tile_size = 1 << bits; const int ymax = (tile_size <= height - row_start) ? tile_size : height - row_start; const int xmax = (tile_size <= width - col_start) ? tile_size : width - col_start; int histo[4][256]; float best_diff = MAX_DIFF_COST; int best_mode = 0; int mode; for (mode = 0; mode < kNumPredModes; ++mode) { const uint32_t* current_row = argb_scratch; const PredictorFunc pred_func = kPredictors[mode]; float cur_diff; int y; memset(&histo[0][0], 0, sizeof(histo)); for (y = 0; y < ymax; ++y) { int x; const int row = row_start + y; const uint32_t* const upper_row = current_row; current_row = upper_row + width; for (x = 0; x < xmax; ++x) { const int col = col_start + x; uint32_t predict; uint32_t predict_diff; if (row == 0) { predict = (col == 0) ? ARGB_BLACK : current_row[col - 1]; // Left. } else if (col == 0) { predict = upper_row[col]; // Top. } else { predict = pred_func(current_row[col - 1], upper_row + col); } predict_diff = VP8LSubPixels(current_row[col], predict); ++histo[0][predict_diff >> 24]; ++histo[1][((predict_diff >> 16) & 0xff)]; ++histo[2][((predict_diff >> 8) & 0xff)]; ++histo[3][(predict_diff & 0xff)]; } } cur_diff = PredictionCostSpatialHistogram(accumulated, histo); if (cur_diff < best_diff) { best_diff = cur_diff; best_mode = mode; } } return best_mode; } static void CopyTileWithPrediction(int width, int height, int tile_x, int tile_y, int bits, int mode, const uint32_t* const argb_scratch, uint32_t* const argb) { const int col_start = tile_x << bits; const int row_start = tile_y << bits; const int tile_size = 1 << bits; const int ymax = (tile_size <= height - row_start) ? tile_size : height - row_start; const int xmax = (tile_size <= width - col_start) ? tile_size : width - col_start; const PredictorFunc pred_func = kPredictors[mode]; const uint32_t* current_row = argb_scratch; int y; for (y = 0; y < ymax; ++y) { int x; const int row = row_start + y; const uint32_t* const upper_row = current_row; current_row = upper_row + width; for (x = 0; x < xmax; ++x) { const int col = col_start + x; const int pix = row * width + col; uint32_t predict; if (row == 0) { predict = (col == 0) ? ARGB_BLACK : current_row[col - 1]; // Left. } else if (col == 0) { predict = upper_row[col]; // Top. } else { predict = pred_func(current_row[col - 1], upper_row + col); } argb[pix] = VP8LSubPixels(current_row[col], predict); } } } void VP8LResidualImage(int width, int height, int bits, uint32_t* const argb, uint32_t* const argb_scratch, uint32_t* const image) { const int max_tile_size = 1 << bits; const int tiles_per_row = VP8LSubSampleSize(width, bits); const int tiles_per_col = VP8LSubSampleSize(height, bits); uint32_t* const upper_row = argb_scratch; uint32_t* const current_tile_rows = argb_scratch + width; int tile_y; int histo[4][256]; memset(histo, 0, sizeof(histo)); for (tile_y = 0; tile_y < tiles_per_col; ++tile_y) { const int tile_y_offset = tile_y * max_tile_size; const int this_tile_height = (tile_y < tiles_per_col - 1) ? max_tile_size : height - tile_y_offset; int tile_x; if (tile_y > 0) { memcpy(upper_row, current_tile_rows + (max_tile_size - 1) * width, width * sizeof(*upper_row)); } memcpy(current_tile_rows, &argb[tile_y_offset * width], this_tile_height * width * sizeof(*current_tile_rows)); for (tile_x = 0; tile_x < tiles_per_row; ++tile_x) { int pred; int y; const int tile_x_offset = tile_x * max_tile_size; int all_x_max = tile_x_offset + max_tile_size; if (all_x_max > width) { all_x_max = width; } pred = GetBestPredictorForTile(width, height, tile_x, tile_y, bits, histo, argb_scratch); image[tile_y * tiles_per_row + tile_x] = 0xff000000u | (pred << 8); CopyTileWithPrediction(width, height, tile_x, tile_y, bits, pred, argb_scratch, argb); for (y = 0; y < max_tile_size; ++y) { int ix; int all_x; int all_y = tile_y_offset + y; if (all_y >= height) { break; } ix = all_y * width + tile_x_offset; for (all_x = tile_x_offset; all_x < all_x_max; ++all_x, ++ix) { const uint32_t a = argb[ix]; ++histo[0][a >> 24]; ++histo[1][((a >> 16) & 0xff)]; ++histo[2][((a >> 8) & 0xff)]; ++histo[3][(a & 0xff)]; } } } } } // Inverse prediction. static void PredictorInverseTransform(const VP8LTransform* const transform, int y_start, int y_end, uint32_t* data) { const int width = transform->xsize_; if (y_start == 0) { // First Row follows the L (mode=1) mode. int x; const uint32_t pred0 = Predictor0(data[-1], NULL); AddPixelsEq(data, pred0); for (x = 1; x < width; ++x) { const uint32_t pred1 = Predictor1(data[x - 1], NULL); AddPixelsEq(data + x, pred1); } data += width; ++y_start; } { int y = y_start; const int mask = (1 << transform->bits_) - 1; const int tiles_per_row = VP8LSubSampleSize(width, transform->bits_); const uint32_t* pred_mode_base = transform->data_ + (y >> transform->bits_) * tiles_per_row; while (y < y_end) { int x; const uint32_t pred2 = Predictor2(data[-1], data - width); const uint32_t* pred_mode_src = pred_mode_base; PredictorFunc pred_func; // First pixel follows the T (mode=2) mode. AddPixelsEq(data, pred2); // .. the rest: pred_func = kPredictors[((*pred_mode_src++) >> 8) & 0xf]; for (x = 1; x < width; ++x) { uint32_t pred; if ((x & mask) == 0) { // start of tile. Read predictor function. pred_func = kPredictors[((*pred_mode_src++) >> 8) & 0xf]; } pred = pred_func(data[x - 1], data + x - width); AddPixelsEq(data + x, pred); } data += width; ++y; if ((y & mask) == 0) { // Use the same mask, since tiles are squares. pred_mode_base += tiles_per_row; } } } } void VP8LSubtractGreenFromBlueAndRed(uint32_t* argb_data, int num_pixs) { int i; for (i = 0; i < num_pixs; ++i) { const uint32_t argb = argb_data[i]; const uint32_t green = (argb >> 8) & 0xff; const uint32_t new_r = (((argb >> 16) & 0xff) - green) & 0xff; const uint32_t new_b = ((argb & 0xff) - green) & 0xff; argb_data[i] = (argb & 0xff00ff00) | (new_r << 16) | new_b; } } // Add green to blue and red channels (i.e. perform the inverse transform of // 'subtract green'). static void AddGreenToBlueAndRed(const VP8LTransform* const transform, int y_start, int y_end, uint32_t* data) { const int width = transform->xsize_; const uint32_t* const data_end = data + (y_end - y_start) * width; while (data < data_end) { const uint32_t argb = *data; // "* 0001001u" is equivalent to "(green << 16) + green)" const uint32_t green = ((argb >> 8) & 0xff); uint32_t red_blue = (argb & 0x00ff00ffu); red_blue += (green << 16) | green; red_blue &= 0x00ff00ffu; *data++ = (argb & 0xff00ff00u) | red_blue; } } typedef struct { // Note: the members are uint8_t, so that any negative values are // automatically converted to "mod 256" values. uint8_t green_to_red_; uint8_t green_to_blue_; uint8_t red_to_blue_; } Multipliers; static WEBP_INLINE void MultipliersClear(Multipliers* m) { m->green_to_red_ = 0; m->green_to_blue_ = 0; m->red_to_blue_ = 0; } static WEBP_INLINE uint32_t ColorTransformDelta(int8_t color_pred, int8_t color) { return (uint32_t)((int)(color_pred) * color) >> 5; } static WEBP_INLINE void ColorCodeToMultipliers(uint32_t color_code, Multipliers* 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(Multipliers* const m) { return 0xff000000u | ((uint32_t)(m->red_to_blue_) << 16) | ((uint32_t)(m->green_to_blue_) << 8) | m->green_to_red_; } static WEBP_INLINE uint32_t TransformColor(const Multipliers* const m, uint32_t argb, int inverse) { const uint32_t green = argb >> 8; const uint32_t red = argb >> 16; uint32_t new_red = red; uint32_t new_blue = argb; if (inverse) { new_red += ColorTransformDelta(m->green_to_red_, green); new_red &= 0xff; new_blue += ColorTransformDelta(m->green_to_blue_, green); new_blue += ColorTransformDelta(m->red_to_blue_, new_red); new_blue &= 0xff; } else { new_red -= ColorTransformDelta(m->green_to_red_, green); new_red &= 0xff; new_blue -= ColorTransformDelta(m->green_to_blue_, green); new_blue -= ColorTransformDelta(m->red_to_blue_, red); new_blue &= 0xff; } return (argb & 0xff00ff00u) | (new_red << 16) | (new_blue); } static WEBP_INLINE int SkipRepeatedPixels(const uint32_t* const argb, int ix, int xsize) { const uint32_t v = argb[ix]; if (ix >= xsize + 3) { if (v == argb[ix - xsize] && argb[ix - 1] == argb[ix - xsize - 1] && argb[ix - 2] == argb[ix - xsize - 2] && argb[ix - 3] == argb[ix - xsize - 3]) { return 1; } return v == argb[ix - 3] && v == argb[ix - 2] && v == argb[ix - 1]; } else if (ix >= 3) { return v == argb[ix - 3] && v == argb[ix - 2] && v == argb[ix - 1]; } return 0; } static float PredictionCostCrossColor(const int accumulated[256], const int counts[256]) { // Favor low entropy, locally and globally. int i; int combo[256]; for (i = 0; i < 256; ++i) { combo[i] = accumulated[i] + counts[i]; } return ShannonEntropy(combo, 256) + ShannonEntropy(counts, 256) + PredictionCostSpatial(counts, 3, 2.4); // Favor small absolute values. } static Multipliers GetBestColorTransformForTile( int tile_x, int tile_y, int bits, Multipliers prevX, Multipliers prevY, int step, int xsize, int ysize, int* accumulated_red_histo, int* accumulated_blue_histo, const uint32_t* const argb) { float best_diff = MAX_DIFF_COST; float cur_diff; const int halfstep = step / 2; 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; int green_to_red; int green_to_blue; int red_to_blue; int all_x_max = tile_x_offset + max_tile_size; int all_y_max = tile_y_offset + max_tile_size; Multipliers best_tx; MultipliersClear(&best_tx); if (all_x_max > xsize) { all_x_max = xsize; } if (all_y_max > ysize) { all_y_max = ysize; } for (green_to_red = -64; green_to_red <= 64; green_to_red += halfstep) { int histo[256] = { 0 }; int all_y; Multipliers tx; MultipliersClear(&tx); tx.green_to_red_ = green_to_red & 0xff; for (all_y = tile_y_offset; all_y < all_y_max; ++all_y) { uint32_t predict; int ix = all_y * xsize + tile_x_offset; int all_x; for (all_x = tile_x_offset; all_x < all_x_max; ++all_x, ++ix) { if (SkipRepeatedPixels(argb, ix, xsize)) { continue; } predict = TransformColor(&tx, argb[ix], 0); ++histo[(predict >> 16) & 0xff]; // red. } } cur_diff = PredictionCostCrossColor(&accumulated_red_histo[0], &histo[0]); if (tx.green_to_red_ == prevX.green_to_red_) { cur_diff -= 3; // favor keeping the areas locally similar } if (tx.green_to_red_ == prevY.green_to_red_) { cur_diff -= 3; // favor keeping the areas locally similar } if (tx.green_to_red_ == 0) { cur_diff -= 3; } if (cur_diff < best_diff) { best_diff = cur_diff; best_tx = tx; } } best_diff = MAX_DIFF_COST; green_to_red = best_tx.green_to_red_; for (green_to_blue = -32; green_to_blue <= 32; green_to_blue += step) { for (red_to_blue = -32; red_to_blue <= 32; red_to_blue += step) { int all_y; int histo[256] = { 0 }; Multipliers tx; tx.green_to_red_ = green_to_red; tx.green_to_blue_ = green_to_blue; tx.red_to_blue_ = red_to_blue; for (all_y = tile_y_offset; all_y < all_y_max; ++all_y) { uint32_t predict; int all_x; int ix = all_y * xsize + tile_x_offset; for (all_x = tile_x_offset; all_x < all_x_max; ++all_x, ++ix) { if (SkipRepeatedPixels(argb, ix, xsize)) { continue; } predict = TransformColor(&tx, argb[ix], 0); ++histo[predict & 0xff]; // blue. } } cur_diff = PredictionCostCrossColor(&accumulated_blue_histo[0], &histo[0]); if (tx.green_to_blue_ == prevX.green_to_blue_) { cur_diff -= 3; // favor keeping the areas locally similar } if (tx.green_to_blue_ == prevY.green_to_blue_) { cur_diff -= 3; // favor keeping the areas locally similar } if (tx.red_to_blue_ == prevX.red_to_blue_) { cur_diff -= 3; // favor keeping the areas locally similar } if (tx.red_to_blue_ == prevY.red_to_blue_) { cur_diff -= 3; // favor keeping the areas locally similar } if (tx.green_to_blue_ == 0) { cur_diff -= 3; } if (tx.red_to_blue_ == 0) { cur_diff -= 3; } if (cur_diff < best_diff) { best_diff = cur_diff; best_tx = tx; } } } return best_tx; } static void CopyTileWithColorTransform(int xsize, int ysize, int tile_x, int tile_y, int bits, Multipliers color_transform, uint32_t* const argb) { int y; int xscan = 1 << bits; int yscan = 1 << bits; tile_x <<= bits; tile_y <<= bits; if (xscan > xsize - tile_x) { xscan = xsize - tile_x; } if (yscan > ysize - tile_y) { yscan = ysize - tile_y; } yscan += tile_y; for (y = tile_y; y < yscan; ++y) { int ix = y * xsize + tile_x; const int end_ix = ix + xscan; for (; ix < end_ix; ++ix) { argb[ix] = TransformColor(&color_transform, argb[ix], 0); } } } void VP8LColorSpaceTransform(int width, int height, int bits, int step, uint32_t* const argb, uint32_t* image) { const int max_tile_size = 1 << bits; int tile_xsize = VP8LSubSampleSize(width, bits); int tile_ysize = VP8LSubSampleSize(height, bits); int accumulated_red_histo[256] = { 0 }; int accumulated_blue_histo[256] = { 0 }; int tile_y; int tile_x; Multipliers prevX; Multipliers prevY; MultipliersClear(&prevY); MultipliersClear(&prevX); for (tile_y = 0; tile_y < tile_ysize; ++tile_y) { for (tile_x = 0; tile_x < tile_xsize; ++tile_x) { Multipliers color_transform; int all_x_max; int y; const int tile_y_offset = tile_y * max_tile_size; const int tile_x_offset = tile_x * max_tile_size; if (tile_y != 0) { ColorCodeToMultipliers(image[tile_y * tile_xsize + tile_x - 1], &prevX); ColorCodeToMultipliers(image[(tile_y - 1) * tile_xsize + tile_x], &prevY); } else if (tile_x != 0) { ColorCodeToMultipliers(image[tile_y * tile_xsize + tile_x - 1], &prevX); } color_transform = GetBestColorTransformForTile(tile_x, tile_y, bits, prevX, prevY, step, width, height, &accumulated_red_histo[0], &accumulated_blue_histo[0], argb); image[tile_y * tile_xsize + tile_x] = MultipliersToColorCode(&color_transform); CopyTileWithColorTransform(width, height, tile_x, tile_y, bits, color_transform, argb); // Gather accumulated histogram data. all_x_max = tile_x_offset + max_tile_size; if (all_x_max > width) { all_x_max = width; } for (y = 0; y < max_tile_size; ++y) { int ix; int all_x; int all_y = tile_y_offset + y; if (all_y >= height) { break; } ix = all_y * width + tile_x_offset; for (all_x = tile_x_offset; all_x < all_x_max; ++all_x, ++ix) { if (ix >= 2 && argb[ix] == argb[ix - 2] && argb[ix] == 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] && argb[ix] == argb[ix - width]) { continue; // repeated pixels are handled by backward references } ++accumulated_red_histo[(argb[ix] >> 16) & 0xff]; ++accumulated_blue_histo[argb[ix] & 0xff]; } } } } } // Color space inverse transform. static void ColorSpaceInverseTransform(const VP8LTransform* const transform, int y_start, int y_end, uint32_t* data) { const int width = transform->xsize_; const int mask = (1 << transform->bits_) - 1; const int tiles_per_row = VP8LSubSampleSize(width, transform->bits_); int y = y_start; const uint32_t* pred_row = transform->data_ + (y >> transform->bits_) * tiles_per_row; while (y < y_end) { const uint32_t* pred = pred_row; Multipliers m = { 0, 0, 0 }; int x; for (x = 0; x < width; ++x) { if ((x & mask) == 0) ColorCodeToMultipliers(*pred++, &m); data[x] = TransformColor(&m, data[x], 1); } data += width; ++y; if ((y & mask) == 0) pred_row += tiles_per_row;; } } // Separate out pixels packed together using pixel-bundling. static void ColorIndexInverseTransform( const VP8LTransform* const transform, int y_start, int y_end, const uint32_t* src, uint32_t* dst) { int y; const int bits_per_pixel = 8 >> transform->bits_; const int width = transform->xsize_; const uint32_t* const color_map = transform->data_; if (bits_per_pixel < 8) { const int pixels_per_byte = 1 << transform->bits_; const int count_mask = pixels_per_byte - 1; const uint32_t bit_mask = (1 << bits_per_pixel) - 1; for (y = y_start; y < y_end; ++y) { uint32_t packed_pixels = 0; int x; for (x = 0; x < width; ++x) { // We need to load fresh 'packed_pixels' once every 'pixels_per_byte' // increments of x. Fortunately, pixels_per_byte is a power of 2, so // can just use a mask for that, instead of decrementing a counter. if ((x & count_mask) == 0) packed_pixels = ((*src++) >> 8) & 0xff; *dst++ = color_map[packed_pixels & bit_mask]; packed_pixels >>= bits_per_pixel; } } } else { for (y = y_start; y < y_end; ++y) { int x; for (x = 0; x < width; ++x) { *dst++ = color_map[((*src++) >> 8) & 0xff]; } } } } void VP8LInverseTransform(const VP8LTransform* const transform, int row_start, int row_end, const uint32_t* const in, uint32_t* const out) { assert(row_start < row_end); assert(row_end <= transform->ysize_); switch (transform->type_) { case SUBTRACT_GREEN: AddGreenToBlueAndRed(transform, row_start, row_end, out); break; case PREDICTOR_TRANSFORM: PredictorInverseTransform(transform, row_start, row_end, out); if (row_end != transform->ysize_) { // The last predicted row in this iteration will be the top-pred row // for the first row in next iteration. const int width = transform->xsize_; memcpy(out - width, out + (row_end - row_start - 1) * width, width * sizeof(*out)); } break; case CROSS_COLOR_TRANSFORM: ColorSpaceInverseTransform(transform, row_start, row_end, out); break; case COLOR_INDEXING_TRANSFORM: if (in == out && transform->bits_ > 0) { // Move packed pixels to the end of unpacked region, so that unpacking // can occur seamlessly. // Also, note that this is the only transform that applies on // the effective width of VP8LSubSampleSize(xsize_, bits_). All other // transforms work on effective width of xsize_. const int out_stride = (row_end - row_start) * transform->xsize_; const int in_stride = (row_end - row_start) * VP8LSubSampleSize(transform->xsize_, transform->bits_); uint32_t* const src = out + out_stride - in_stride; memmove(src, out, in_stride * sizeof(*src)); ColorIndexInverseTransform(transform, row_start, row_end, src, out); } else { ColorIndexInverseTransform(transform, row_start, row_end, in, out); } break; } } //------------------------------------------------------------------------------ // Color space conversion. static int is_big_endian(void) { static const union { uint16_t w; uint8_t b[2]; } tmp = { 1 }; return (tmp.b[0] != 1); } static void ConvertBGRAToRGB(const uint32_t* src, int num_pixels, uint8_t* dst) { const uint32_t* const src_end = src + num_pixels; while (src < src_end) { const uint32_t argb = *src++; *dst++ = (argb >> 16) & 0xff; *dst++ = (argb >> 8) & 0xff; *dst++ = (argb >> 0) & 0xff; } } static void ConvertBGRAToRGBA(const uint32_t* src, int num_pixels, uint8_t* dst) { const uint32_t* const src_end = src + num_pixels; while (src < src_end) { const uint32_t argb = *src++; *dst++ = (argb >> 16) & 0xff; *dst++ = (argb >> 8) & 0xff; *dst++ = (argb >> 0) & 0xff; *dst++ = (argb >> 24) & 0xff; } } static void ConvertBGRAToRGBA4444(const uint32_t* src, int num_pixels, uint8_t* dst) { const uint32_t* const src_end = src + num_pixels; while (src < src_end) { const uint32_t argb = *src++; *dst++ = ((argb >> 16) & 0xf0) | ((argb >> 12) & 0xf); *dst++ = ((argb >> 0) & 0xf0) | ((argb >> 28) & 0xf); } } static void ConvertBGRAToRGB565(const uint32_t* src, int num_pixels, uint8_t* dst) { const uint32_t* const src_end = src + num_pixels; while (src < src_end) { const uint32_t argb = *src++; *dst++ = ((argb >> 16) & 0xf8) | ((argb >> 13) & 0x7); *dst++ = ((argb >> 5) & 0xe0) | ((argb >> 3) & 0x1f); } } static void ConvertBGRAToBGR(const uint32_t* src, int num_pixels, uint8_t* dst) { const uint32_t* const src_end = src + num_pixels; while (src < src_end) { const uint32_t argb = *src++; *dst++ = (argb >> 0) & 0xff; *dst++ = (argb >> 8) & 0xff; *dst++ = (argb >> 16) & 0xff; } } static void CopyOrSwap(const uint32_t* src, int num_pixels, uint8_t* dst, int swap_on_big_endian) { if (is_big_endian() == swap_on_big_endian) { const uint32_t* const src_end = src + num_pixels; while (src < src_end) { uint32_t argb = *src++; #if !defined(__BIG_ENDIAN__) && (defined(__i386__) || defined(__x86_64__)) __asm__ volatile("bswap %0" : "=r"(argb) : "0"(argb)); *(uint32_t*)dst = argb; dst += sizeof(argb); #elif !defined(__BIG_ENDIAN__) && defined(_MSC_VER) argb = _byteswap_ulong(argb); *(uint32_t*)dst = argb; dst += sizeof(argb); #else *dst++ = (argb >> 24) & 0xff; *dst++ = (argb >> 16) & 0xff; *dst++ = (argb >> 8) & 0xff; *dst++ = (argb >> 0) & 0xff; #endif } } else { memcpy(dst, src, num_pixels * sizeof(*src)); } } void VP8LConvertFromBGRA(const uint32_t* const in_data, int num_pixels, WEBP_CSP_MODE out_colorspace, uint8_t* const rgba) { switch (out_colorspace) { case MODE_RGB: ConvertBGRAToRGB(in_data, num_pixels, rgba); break; case MODE_RGBA: ConvertBGRAToRGBA(in_data, num_pixels, rgba); break; case MODE_rgbA: ConvertBGRAToRGBA(in_data, num_pixels, rgba); WebPApplyAlphaMultiply(rgba, 0, num_pixels, 1, 0); break; case MODE_BGR: ConvertBGRAToBGR(in_data, num_pixels, rgba); break; case MODE_BGRA: CopyOrSwap(in_data, num_pixels, rgba, 1); break; case MODE_bgrA: CopyOrSwap(in_data, num_pixels, rgba, 1); WebPApplyAlphaMultiply(rgba, 0, num_pixels, 1, 0); break; case MODE_ARGB: CopyOrSwap(in_data, num_pixels, rgba, 0); break; case MODE_Argb: CopyOrSwap(in_data, num_pixels, rgba, 0); WebPApplyAlphaMultiply(rgba, 1, num_pixels, 1, 0); break; case MODE_RGBA_4444: ConvertBGRAToRGBA4444(in_data, num_pixels, rgba); break; case MODE_rgbA_4444: ConvertBGRAToRGBA4444(in_data, num_pixels, rgba); WebPApplyAlphaMultiply4444(rgba, num_pixels, 1, 0); break; case MODE_RGB_565: ConvertBGRAToRGB565(in_data, num_pixels, rgba); break; default: assert(0); // Code flow should not reach here. } } //------------------------------------------------------------------------------ #if defined(__cplusplus) || defined(c_plusplus) } // extern "C" #endif