// Copyright 2012 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. // ----------------------------------------------------------------------------- // // Author: Jyrki Alakuijala (jyrki@google.com) // #ifdef HAVE_CONFIG_H #include "../webp/config.h" #endif #include #include "./backward_references.h" #include "./histogram.h" #include "../dsp/lossless.h" #include "../utils/utils.h" #define ALIGN_CST 15 #define DO_ALIGN(PTR) ((uintptr_t)((PTR) + ALIGN_CST) & ~ALIGN_CST) #define MAX_COST 1.e38 // Number of partitions for the three dominant (literal, red and blue) symbol // costs. #define NUM_PARTITIONS 4 // The size of the bin-hash corresponding to the three dominant costs. #define BIN_SIZE (NUM_PARTITIONS * NUM_PARTITIONS * NUM_PARTITIONS) static void HistogramClear(VP8LHistogram* const p) { uint32_t* const literal = p->literal_; const int cache_bits = p->palette_code_bits_; const int histo_size = VP8LGetHistogramSize(cache_bits); memset(p, 0, histo_size); p->palette_code_bits_ = cache_bits; p->literal_ = literal; } static void HistogramCopy(const VP8LHistogram* const src, VP8LHistogram* const dst) { uint32_t* const dst_literal = dst->literal_; const int dst_cache_bits = dst->palette_code_bits_; const int histo_size = VP8LGetHistogramSize(dst_cache_bits); assert(src->palette_code_bits_ == dst_cache_bits); memcpy(dst, src, histo_size); dst->literal_ = dst_literal; } int VP8LGetHistogramSize(int cache_bits) { const int literal_size = VP8LHistogramNumCodes(cache_bits); const size_t total_size = sizeof(VP8LHistogram) + sizeof(int) * literal_size; assert(total_size <= (size_t)0x7fffffff); return (int)total_size; } void VP8LFreeHistogram(VP8LHistogram* const histo) { WebPSafeFree(histo); } void VP8LFreeHistogramSet(VP8LHistogramSet* const histo) { WebPSafeFree(histo); } void VP8LHistogramStoreRefs(const VP8LBackwardRefs* const refs, VP8LHistogram* const histo) { VP8LRefsCursor c = VP8LRefsCursorInit(refs); while (VP8LRefsCursorOk(&c)) { VP8LHistogramAddSinglePixOrCopy(histo, c.cur_pos); VP8LRefsCursorNext(&c); } } void VP8LHistogramCreate(VP8LHistogram* const p, const VP8LBackwardRefs* const refs, int palette_code_bits) { if (palette_code_bits >= 0) { p->palette_code_bits_ = palette_code_bits; } HistogramClear(p); VP8LHistogramStoreRefs(refs, p); } void VP8LHistogramInit(VP8LHistogram* const p, int palette_code_bits) { p->palette_code_bits_ = palette_code_bits; HistogramClear(p); } VP8LHistogram* VP8LAllocateHistogram(int cache_bits) { VP8LHistogram* histo = NULL; const int total_size = VP8LGetHistogramSize(cache_bits); uint8_t* const memory = (uint8_t*)WebPSafeMalloc(total_size, sizeof(*memory)); if (memory == NULL) return NULL; histo = (VP8LHistogram*)memory; // literal_ won't necessary be aligned. histo->literal_ = (uint32_t*)(memory + sizeof(VP8LHistogram)); VP8LHistogramInit(histo, cache_bits); return histo; } VP8LHistogramSet* VP8LAllocateHistogramSet(int size, int cache_bits) { int i; VP8LHistogramSet* set; const int histo_size = VP8LGetHistogramSize(cache_bits); const size_t total_size = sizeof(*set) + size * (sizeof(*set->histograms) + histo_size + ALIGN_CST); uint8_t* memory = (uint8_t*)WebPSafeMalloc(total_size, sizeof(*memory)); if (memory == NULL) return NULL; set = (VP8LHistogramSet*)memory; memory += sizeof(*set); set->histograms = (VP8LHistogram**)memory; memory += size * sizeof(*set->histograms); set->max_size = size; set->size = size; for (i = 0; i < size; ++i) { memory = (uint8_t*)DO_ALIGN(memory); set->histograms[i] = (VP8LHistogram*)memory; // literal_ won't necessary be aligned. set->histograms[i]->literal_ = (uint32_t*)(memory + sizeof(VP8LHistogram)); VP8LHistogramInit(set->histograms[i], cache_bits); memory += histo_size; } return set; } // ----------------------------------------------------------------------------- void VP8LHistogramAddSinglePixOrCopy(VP8LHistogram* const histo, const PixOrCopy* const v) { if (PixOrCopyIsLiteral(v)) { ++histo->alpha_[PixOrCopyLiteral(v, 3)]; ++histo->red_[PixOrCopyLiteral(v, 2)]; ++histo->literal_[PixOrCopyLiteral(v, 1)]; ++histo->blue_[PixOrCopyLiteral(v, 0)]; } else if (PixOrCopyIsCacheIdx(v)) { const int literal_ix = NUM_LITERAL_CODES + NUM_LENGTH_CODES + PixOrCopyCacheIdx(v); ++histo->literal_[literal_ix]; } else { int code, extra_bits; VP8LPrefixEncodeBits(PixOrCopyLength(v), &code, &extra_bits); ++histo->literal_[NUM_LITERAL_CODES + code]; VP8LPrefixEncodeBits(PixOrCopyDistance(v), &code, &extra_bits); ++histo->distance_[code]; } } static WEBP_INLINE double BitsEntropyRefine(int nonzeros, int sum, int max_val, double retval) { double mix; if (nonzeros < 5) { if (nonzeros <= 1) { return 0; } // Two symbols, they will be 0 and 1 in a Huffman code. // Let's mix in a bit of entropy to favor good clustering when // distributions of these are combined. if (nonzeros == 2) { return 0.99 * sum + 0.01 * retval; } // No matter what the entropy says, we cannot be better than min_limit // with Huffman coding. I am mixing a bit of entropy into the // min_limit since it produces much better (~0.5 %) compression results // perhaps because of better entropy clustering. if (nonzeros == 3) { mix = 0.95; } else { mix = 0.7; // nonzeros == 4. } } else { mix = 0.627; } { double min_limit = 2 * sum - max_val; min_limit = mix * min_limit + (1.0 - mix) * retval; return (retval < min_limit) ? min_limit : retval; } } static double BitsEntropy(const uint32_t* const array, int n) { double retval = 0.; uint32_t sum = 0; int nonzeros = 0; uint32_t max_val = 0; int i; for (i = 0; i < n; ++i) { if (array[i] != 0) { sum += array[i]; ++nonzeros; retval -= VP8LFastSLog2(array[i]); if (max_val < array[i]) { max_val = array[i]; } } } retval += VP8LFastSLog2(sum); return BitsEntropyRefine(nonzeros, sum, max_val, retval); } static double BitsEntropyCombined(const uint32_t* const X, const uint32_t* const Y, int n) { double retval = 0.; int sum = 0; int nonzeros = 0; int max_val = 0; int i; for (i = 0; i < n; ++i) { const int xy = X[i] + Y[i]; if (xy != 0) { sum += xy; ++nonzeros; retval -= VP8LFastSLog2(xy); if (max_val < xy) { max_val = xy; } } } retval += VP8LFastSLog2(sum); return BitsEntropyRefine(nonzeros, sum, max_val, retval); } static double InitialHuffmanCost(void) { // Small bias because Huffman code length is typically not stored in // full length. static const int kHuffmanCodeOfHuffmanCodeSize = CODE_LENGTH_CODES * 3; static const double kSmallBias = 9.1; return kHuffmanCodeOfHuffmanCodeSize - kSmallBias; } // Finalize the Huffman cost based on streak numbers and length type (<3 or >=3) static double FinalHuffmanCost(const VP8LStreaks* const stats) { double retval = InitialHuffmanCost(); retval += stats->counts[0] * 1.5625 + 0.234375 * stats->streaks[0][1]; retval += stats->counts[1] * 2.578125 + 0.703125 * stats->streaks[1][1]; retval += 1.796875 * stats->streaks[0][0]; retval += 3.28125 * stats->streaks[1][0]; return retval; } // Trampolines static double HuffmanCost(const uint32_t* const population, int length) { const VP8LStreaks stats = VP8LHuffmanCostCount(population, length); return FinalHuffmanCost(&stats); } static double HuffmanCostCombined(const uint32_t* const X, const uint32_t* const Y, int length) { const VP8LStreaks stats = VP8LHuffmanCostCombinedCount(X, Y, length); return FinalHuffmanCost(&stats); } // Aggregated costs static double PopulationCost(const uint32_t* const population, int length) { return BitsEntropy(population, length) + HuffmanCost(population, length); } static double GetCombinedEntropy(const uint32_t* const X, const uint32_t* const Y, int length) { return BitsEntropyCombined(X, Y, length) + HuffmanCostCombined(X, Y, length); } // Estimates the Entropy + Huffman + other block overhead size cost. double VP8LHistogramEstimateBits(const VP8LHistogram* const p) { return PopulationCost(p->literal_, VP8LHistogramNumCodes(p->palette_code_bits_)) + PopulationCost(p->red_, NUM_LITERAL_CODES) + PopulationCost(p->blue_, NUM_LITERAL_CODES) + PopulationCost(p->alpha_, NUM_LITERAL_CODES) + PopulationCost(p->distance_, NUM_DISTANCE_CODES) + VP8LExtraCost(p->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES) + VP8LExtraCost(p->distance_, NUM_DISTANCE_CODES); } double VP8LHistogramEstimateBitsBulk(const VP8LHistogram* const p) { return BitsEntropy(p->literal_, VP8LHistogramNumCodes(p->palette_code_bits_)) + BitsEntropy(p->red_, NUM_LITERAL_CODES) + BitsEntropy(p->blue_, NUM_LITERAL_CODES) + BitsEntropy(p->alpha_, NUM_LITERAL_CODES) + BitsEntropy(p->distance_, NUM_DISTANCE_CODES) + VP8LExtraCost(p->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES) + VP8LExtraCost(p->distance_, NUM_DISTANCE_CODES); } // ----------------------------------------------------------------------------- // Various histogram combine/cost-eval functions static int GetCombinedHistogramEntropy(const VP8LHistogram* const a, const VP8LHistogram* const b, double cost_threshold, double* cost) { const int palette_code_bits = a->palette_code_bits_; assert(a->palette_code_bits_ == b->palette_code_bits_); *cost += GetCombinedEntropy(a->literal_, b->literal_, VP8LHistogramNumCodes(palette_code_bits)); *cost += VP8LExtraCostCombined(a->literal_ + NUM_LITERAL_CODES, b->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES); if (*cost > cost_threshold) return 0; *cost += GetCombinedEntropy(a->red_, b->red_, NUM_LITERAL_CODES); if (*cost > cost_threshold) return 0; *cost += GetCombinedEntropy(a->blue_, b->blue_, NUM_LITERAL_CODES); if (*cost > cost_threshold) return 0; *cost += GetCombinedEntropy(a->alpha_, b->alpha_, NUM_LITERAL_CODES); if (*cost > cost_threshold) return 0; *cost += GetCombinedEntropy(a->distance_, b->distance_, NUM_DISTANCE_CODES); *cost += VP8LExtraCostCombined(a->distance_, b->distance_, NUM_DISTANCE_CODES); if (*cost > cost_threshold) return 0; return 1; } // Performs out = a + b, computing the cost C(a+b) - C(a) - C(b) while comparing // to the threshold value 'cost_threshold'. The score returned is // Score = C(a+b) - C(a) - C(b), where C(a) + C(b) is known and fixed. // Since the previous score passed is 'cost_threshold', we only need to compare // the partial cost against 'cost_threshold + C(a) + C(b)' to possibly bail-out // early. static double HistogramAddEval(const VP8LHistogram* const a, const VP8LHistogram* const b, VP8LHistogram* const out, double cost_threshold) { double cost = 0; const double sum_cost = a->bit_cost_ + b->bit_cost_; cost_threshold += sum_cost; if (GetCombinedHistogramEntropy(a, b, cost_threshold, &cost)) { VP8LHistogramAdd(a, b, out); out->bit_cost_ = cost; out->palette_code_bits_ = a->palette_code_bits_; } return cost - sum_cost; } // Same as HistogramAddEval(), except that the resulting histogram // is not stored. Only the cost C(a+b) - C(a) is evaluated. We omit // the term C(b) which is constant over all the evaluations. static double HistogramAddThresh(const VP8LHistogram* const a, const VP8LHistogram* const b, double cost_threshold) { double cost = -a->bit_cost_; GetCombinedHistogramEntropy(a, b, cost_threshold, &cost); return cost; } // ----------------------------------------------------------------------------- // The structure to keep track of cost range for the three dominant entropy // symbols. // TODO(skal): Evaluate if float can be used here instead of double for // representing the entropy costs. typedef struct { double literal_max_; double literal_min_; double red_max_; double red_min_; double blue_max_; double blue_min_; } DominantCostRange; static void DominantCostRangeInit(DominantCostRange* const c) { c->literal_max_ = 0.; c->literal_min_ = MAX_COST; c->red_max_ = 0.; c->red_min_ = MAX_COST; c->blue_max_ = 0.; c->blue_min_ = MAX_COST; } static void UpdateDominantCostRange( const VP8LHistogram* const h, DominantCostRange* const c) { if (c->literal_max_ < h->literal_cost_) c->literal_max_ = h->literal_cost_; if (c->literal_min_ > h->literal_cost_) c->literal_min_ = h->literal_cost_; if (c->red_max_ < h->red_cost_) c->red_max_ = h->red_cost_; if (c->red_min_ > h->red_cost_) c->red_min_ = h->red_cost_; if (c->blue_max_ < h->blue_cost_) c->blue_max_ = h->blue_cost_; if (c->blue_min_ > h->blue_cost_) c->blue_min_ = h->blue_cost_; } static void UpdateHistogramCost(VP8LHistogram* const h) { const double alpha_cost = PopulationCost(h->alpha_, NUM_LITERAL_CODES); const double distance_cost = PopulationCost(h->distance_, NUM_DISTANCE_CODES) + VP8LExtraCost(h->distance_, NUM_DISTANCE_CODES); const int num_codes = VP8LHistogramNumCodes(h->palette_code_bits_); h->literal_cost_ = PopulationCost(h->literal_, num_codes) + VP8LExtraCost(h->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES); h->red_cost_ = PopulationCost(h->red_, NUM_LITERAL_CODES); h->blue_cost_ = PopulationCost(h->blue_, NUM_LITERAL_CODES); h->bit_cost_ = h->literal_cost_ + h->red_cost_ + h->blue_cost_ + alpha_cost + distance_cost; } static int GetBinIdForEntropy(double min, double max, double val) { const double range = max - min + 1e-6; const double delta = val - min; return (int)(NUM_PARTITIONS * delta / range); } // TODO(vikasa): Evaluate, if there's any correlation between red & blue. static int GetHistoBinIndex( const VP8LHistogram* const h, const DominantCostRange* const c) { const int bin_id = GetBinIdForEntropy(c->blue_min_, c->blue_max_, h->blue_cost_) + NUM_PARTITIONS * GetBinIdForEntropy(c->red_min_, c->red_max_, h->red_cost_) + NUM_PARTITIONS * NUM_PARTITIONS * GetBinIdForEntropy(c->literal_min_, c->literal_max_, h->literal_cost_); assert(bin_id < BIN_SIZE); return bin_id; } // Construct the histograms from backward references. static void HistogramBuild( int xsize, int histo_bits, const VP8LBackwardRefs* const backward_refs, VP8LHistogramSet* const image_histo) { int x = 0, y = 0; const int histo_xsize = VP8LSubSampleSize(xsize, histo_bits); VP8LHistogram** const histograms = image_histo->histograms; VP8LRefsCursor c = VP8LRefsCursorInit(backward_refs); assert(histo_bits > 0); // Construct the Histo from a given backward references. while (VP8LRefsCursorOk(&c)) { const PixOrCopy* const v = c.cur_pos; const int ix = (y >> histo_bits) * histo_xsize + (x >> histo_bits); VP8LHistogramAddSinglePixOrCopy(histograms[ix], v); x += PixOrCopyLength(v); while (x >= xsize) { x -= xsize; ++y; } VP8LRefsCursorNext(&c); } } // Copies the histograms and computes its bit_cost. static void HistogramCopyAndAnalyze( VP8LHistogramSet* const orig_histo, VP8LHistogramSet* const image_histo) { int i; const int histo_size = orig_histo->size; VP8LHistogram** const orig_histograms = orig_histo->histograms; VP8LHistogram** const histograms = image_histo->histograms; for (i = 0; i < histo_size; ++i) { VP8LHistogram* const histo = orig_histograms[i]; UpdateHistogramCost(histo); // Copy histograms from orig_histo[] to image_histo[]. HistogramCopy(histo, histograms[i]); } } // Partition histograms to different entropy bins for three dominant (literal, // red and blue) symbol costs and compute the histogram aggregate bit_cost. static void HistogramAnalyzeEntropyBin( VP8LHistogramSet* const image_histo, int16_t* const bin_map) { int i; VP8LHistogram** const histograms = image_histo->histograms; const int histo_size = image_histo->size; const int bin_depth = histo_size + 1; DominantCostRange cost_range; DominantCostRangeInit(&cost_range); // Analyze the dominant (literal, red and blue) entropy costs. for (i = 0; i < histo_size; ++i) { VP8LHistogram* const histo = histograms[i]; UpdateDominantCostRange(histo, &cost_range); } // bin-hash histograms on three of the dominant (literal, red and blue) // symbol costs. for (i = 0; i < histo_size; ++i) { int num_histos; VP8LHistogram* const histo = histograms[i]; const int16_t bin_id = (int16_t)GetHistoBinIndex(histo, &cost_range); const int bin_offset = bin_id * bin_depth; // bin_map[n][0] for every bin 'n' maintains the counter for the number of // histograms in that bin. // Get and increment the num_histos in that bin. num_histos = ++bin_map[bin_offset]; assert(bin_offset + num_histos < bin_depth * BIN_SIZE); // Add histogram i'th index at num_histos (last) position in the bin_map. bin_map[bin_offset + num_histos] = i; } } // Compact the histogram set by moving the valid one left in the set to the // head and moving the ones that have been merged to other histograms towards // the end. // TODO(vikasa): Evaluate if this method can be avoided by altering the code // logic of HistogramCombineEntropyBin main loop. static void HistogramCompactBins(VP8LHistogramSet* const image_histo) { int start = 0; int end = image_histo->size - 1; VP8LHistogram** const histograms = image_histo->histograms; while (start < end) { while (start <= end && histograms[start] != NULL && histograms[start]->bit_cost_ != 0.) { ++start; } while (start <= end && histograms[end]->bit_cost_ == 0.) { histograms[end] = NULL; --end; } if (start < end) { assert(histograms[start] != NULL); assert(histograms[end] != NULL); HistogramCopy(histograms[end], histograms[start]); histograms[end] = NULL; --end; } } image_histo->size = end + 1; } static void HistogramCombineEntropyBin(VP8LHistogramSet* const image_histo, VP8LHistogram* const histos, int16_t* const bin_map, int bin_depth, double combine_cost_factor) { int bin_id; VP8LHistogram* cur_combo = histos; VP8LHistogram** const histograms = image_histo->histograms; for (bin_id = 0; bin_id < BIN_SIZE; ++bin_id) { const int bin_offset = bin_id * bin_depth; const int num_histos = bin_map[bin_offset]; const int idx1 = bin_map[bin_offset + 1]; int n; for (n = 2; n <= num_histos; ++n) { const int idx2 = bin_map[bin_offset + n]; const double bit_cost_idx2 = histograms[idx2]->bit_cost_; if (bit_cost_idx2 > 0.) { const double bit_cost_thresh = -bit_cost_idx2 * combine_cost_factor; const double curr_cost_diff = HistogramAddEval(histograms[idx1], histograms[idx2], cur_combo, bit_cost_thresh); if (curr_cost_diff < bit_cost_thresh) { HistogramCopy(cur_combo, histograms[idx1]); histograms[idx2]->bit_cost_ = 0.; } } } } HistogramCompactBins(image_histo); } static uint32_t MyRand(uint32_t *seed) { *seed *= 16807U; if (*seed == 0) { *seed = 1; } return *seed; } static void HistogramCombine(VP8LHistogramSet* const image_histo, VP8LHistogramSet* const histos, int quality) { int iter; uint32_t seed = 0; int tries_with_no_success = 0; int image_histo_size = image_histo->size; const int iter_mult = (quality < 25) ? 2 : 2 + (quality - 25) / 8; const int outer_iters = image_histo_size * iter_mult; const int num_pairs = image_histo_size / 2; const int num_tries_no_success = outer_iters / 2; const int min_cluster_size = 2; VP8LHistogram** const histograms = image_histo->histograms; VP8LHistogram* cur_combo = histos->histograms[0]; // trial histogram VP8LHistogram* best_combo = histos->histograms[1]; // best histogram so far // Collapse similar histograms in 'image_histo'. for (iter = 0; iter < outer_iters && image_histo_size >= min_cluster_size; ++iter) { double best_cost_diff = 0.; int best_idx1 = -1, best_idx2 = 1; int j; const int num_tries = (num_pairs < image_histo_size) ? num_pairs : image_histo_size; seed += iter; for (j = 0; j < num_tries; ++j) { double curr_cost_diff; // Choose two histograms at random and try to combine them. const uint32_t idx1 = MyRand(&seed) % image_histo_size; const uint32_t tmp = (j & 7) + 1; const uint32_t diff = (tmp < 3) ? tmp : MyRand(&seed) % (image_histo_size - 1); const uint32_t idx2 = (idx1 + diff + 1) % image_histo_size; if (idx1 == idx2) { continue; } // Calculate cost reduction on combining. curr_cost_diff = HistogramAddEval(histograms[idx1], histograms[idx2], cur_combo, best_cost_diff); if (curr_cost_diff < best_cost_diff) { // found a better pair? { // swap cur/best combo histograms VP8LHistogram* const tmp_histo = cur_combo; cur_combo = best_combo; best_combo = tmp_histo; } best_cost_diff = curr_cost_diff; best_idx1 = idx1; best_idx2 = idx2; } } if (best_idx1 >= 0) { HistogramCopy(best_combo, histograms[best_idx1]); // swap best_idx2 slot with last one (which is now unused) --image_histo_size; if (best_idx2 != image_histo_size) { HistogramCopy(histograms[image_histo_size], histograms[best_idx2]); histograms[image_histo_size] = NULL; } tries_with_no_success = 0; } if (++tries_with_no_success >= num_tries_no_success) { break; } } image_histo->size = image_histo_size; } // ----------------------------------------------------------------------------- // Histogram refinement // Find the best 'out' histogram for each of the 'in' histograms. // Note: we assume that out[]->bit_cost_ is already up-to-date. static void HistogramRemap(const VP8LHistogramSet* const orig_histo, const VP8LHistogramSet* const image_histo, uint16_t* const symbols) { int i; VP8LHistogram** const orig_histograms = orig_histo->histograms; VP8LHistogram** const histograms = image_histo->histograms; for (i = 0; i < orig_histo->size; ++i) { int best_out = 0; double best_bits = HistogramAddThresh(histograms[0], orig_histograms[i], MAX_COST); int k; for (k = 1; k < image_histo->size; ++k) { const double cur_bits = HistogramAddThresh(histograms[k], orig_histograms[i], best_bits); if (cur_bits < best_bits) { best_bits = cur_bits; best_out = k; } } symbols[i] = best_out; } // Recompute each out based on raw and symbols. for (i = 0; i < image_histo->size; ++i) { HistogramClear(histograms[i]); } for (i = 0; i < orig_histo->size; ++i) { const int idx = symbols[i]; VP8LHistogramAdd(orig_histograms[i], histograms[idx], histograms[idx]); } } static double GetCombineCostFactor(int histo_size, int quality) { double combine_cost_factor = 0.16; if (histo_size > 256) combine_cost_factor /= 2.; if (histo_size > 512) combine_cost_factor /= 2.; if (histo_size > 1024) combine_cost_factor /= 2.; if (quality <= 50) combine_cost_factor /= 2.; return combine_cost_factor; } int VP8LGetHistoImageSymbols(int xsize, int ysize, const VP8LBackwardRefs* const refs, int quality, int histo_bits, int cache_bits, VP8LHistogramSet* const image_histo, uint16_t* const histogram_symbols) { int ok = 0; const int histo_xsize = histo_bits ? VP8LSubSampleSize(xsize, histo_bits) : 1; const int histo_ysize = histo_bits ? VP8LSubSampleSize(ysize, histo_bits) : 1; const int image_histo_raw_size = histo_xsize * histo_ysize; // The bin_map for every bin follows following semantics: // bin_map[n][0] = num_histo; // The number of histograms in that bin. // bin_map[n][1] = index of first histogram in that bin; // bin_map[n][num_histo] = index of last histogram in that bin; // bin_map[n][num_histo + 1] ... bin_map[n][bin_depth - 1] = un-used indices. const int bin_depth = image_histo_raw_size + 1; int16_t* bin_map = NULL; VP8LHistogramSet* const histos = VP8LAllocateHistogramSet(2, cache_bits); VP8LHistogramSet* const orig_histo = VP8LAllocateHistogramSet(image_histo_raw_size, cache_bits); if (orig_histo == NULL || histos == NULL) { goto Error; } // Don't attempt linear bin-partition heuristic for: // histograms of small sizes, as bin_map will be very sparse and; // Higher qualities (> 90), to preserve the compression gains at those // quality settings. if (orig_histo->size > 2 * BIN_SIZE && quality < 90) { const int bin_map_size = bin_depth * BIN_SIZE; bin_map = (int16_t*)WebPSafeCalloc(bin_map_size, sizeof(*bin_map)); if (bin_map == NULL) goto Error; } // Construct the histograms from backward references. HistogramBuild(xsize, histo_bits, refs, orig_histo); // Copies the histograms and computes its bit_cost. HistogramCopyAndAnalyze(orig_histo, image_histo); if (bin_map != NULL) { const double combine_cost_factor = GetCombineCostFactor(image_histo_raw_size, quality); HistogramAnalyzeEntropyBin(orig_histo, bin_map); // Collapse histograms with similar entropy. HistogramCombineEntropyBin(image_histo, histos->histograms[0], bin_map, bin_depth, combine_cost_factor); } // Collapse similar histograms by random histogram-pair compares. HistogramCombine(image_histo, histos, quality); // Find the optimal map from original histograms to the final ones. HistogramRemap(orig_histo, image_histo, histogram_symbols); ok = 1; Error: WebPSafeFree(bin_map); VP8LFreeHistogramSet(orig_histo); VP8LFreeHistogramSet(histos); return ok; }