// 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 "src/webp/config.h" #endif #include <math.h> #include "src/enc/backward_references_enc.h" #include "src/enc/histogram_enc.h" #include "src/dsp/lossless.h" #include "src/dsp/lossless_common.h" #include "src/utils/utils.h" #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) // Maximum number of histograms allowed in greedy combining algorithm. #define MAX_HISTO_GREEDY 100 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; } // Swap two histogram pointers. static void HistogramSwap(VP8LHistogram** const A, VP8LHistogram** const B) { VP8LHistogram* const tmp = *A; *A = *B; *B = tmp; } 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, NULL, 0); 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 + WEBP_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*)WEBP_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, int (*const distance_modifier)(int, int), int distance_modifier_arg0) { 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]; if (distance_modifier == NULL) { VP8LPrefixEncodeBits(PixOrCopyDistance(v), &code, &extra_bits); } else { VP8LPrefixEncodeBits( distance_modifier(distance_modifier_arg0, PixOrCopyDistance(v)), &code, &extra_bits); } ++histo->distance_[code]; } } // ----------------------------------------------------------------------------- // Entropy-related functions. static WEBP_INLINE double BitsEntropyRefine(const VP8LBitEntropy* entropy) { double mix; if (entropy->nonzeros < 5) { if (entropy->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 (entropy->nonzeros == 2) { return 0.99 * entropy->sum + 0.01 * entropy->entropy; } // 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 (entropy->nonzeros == 3) { mix = 0.95; } else { mix = 0.7; // nonzeros == 4. } } else { mix = 0.627; } { double min_limit = 2 * entropy->sum - entropy->max_val; min_limit = mix * min_limit + (1.0 - mix) * entropy->entropy; return (entropy->entropy < min_limit) ? min_limit : entropy->entropy; } } double VP8LBitsEntropy(const uint32_t* const array, int n, uint32_t* const trivial_symbol) { VP8LBitEntropy entropy; VP8LBitsEntropyUnrefined(array, n, &entropy); if (trivial_symbol != NULL) { *trivial_symbol = (entropy.nonzeros == 1) ? entropy.nonzero_code : VP8L_NON_TRIVIAL_SYM; } return BitsEntropyRefine(&entropy); } 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) { // The constants in this function are experimental and got rounded from // their original values in 1/8 when switched to 1/1024. double retval = InitialHuffmanCost(); // Second coefficient: Many zeros in the histogram are covered efficiently // by a run-length encode. Originally 2/8. retval += stats->counts[0] * 1.5625 + 0.234375 * stats->streaks[0][1]; // Second coefficient: Constant values are encoded less efficiently, but still // RLE'ed. Originally 6/8. retval += stats->counts[1] * 2.578125 + 0.703125 * stats->streaks[1][1]; // 0s are usually encoded more efficiently than non-0s. // Originally 15/8. retval += 1.796875 * stats->streaks[0][0]; // Originally 26/8. retval += 3.28125 * stats->streaks[1][0]; return retval; } // Get the symbol entropy for the distribution 'population'. // Set 'trivial_sym', if there's only one symbol present in the distribution. static double PopulationCost(const uint32_t* const population, int length, uint32_t* const trivial_sym) { VP8LBitEntropy bit_entropy; VP8LStreaks stats; VP8LGetEntropyUnrefined(population, length, &bit_entropy, &stats); if (trivial_sym != NULL) { *trivial_sym = (bit_entropy.nonzeros == 1) ? bit_entropy.nonzero_code : VP8L_NON_TRIVIAL_SYM; } return BitsEntropyRefine(&bit_entropy) + FinalHuffmanCost(&stats); } // trivial_at_end is 1 if the two histograms only have one element that is // non-zero: both the zero-th one, or both the last one. static WEBP_INLINE double GetCombinedEntropy(const uint32_t* const X, const uint32_t* const Y, int length, int trivial_at_end) { VP8LStreaks stats; if (trivial_at_end) { // This configuration is due to palettization that transforms an indexed // pixel into 0xff000000 | (pixel << 8) in VP8LBundleColorMap. // BitsEntropyRefine is 0 for histograms with only one non-zero value. // Only FinalHuffmanCost needs to be evaluated. memset(&stats, 0, sizeof(stats)); // Deal with the non-zero value at index 0 or length-1. stats.streaks[1][0] += 1; // Deal with the following/previous zero streak. stats.counts[0] += 1; stats.streaks[0][1] += length - 1; return FinalHuffmanCost(&stats); } else { VP8LBitEntropy bit_entropy; VP8LGetCombinedEntropyUnrefined(X, Y, length, &bit_entropy, &stats); return BitsEntropyRefine(&bit_entropy) + FinalHuffmanCost(&stats); } } // Estimates the Entropy + Huffman + other block overhead size cost. double VP8LHistogramEstimateBits(const VP8LHistogram* const p) { return PopulationCost( p->literal_, VP8LHistogramNumCodes(p->palette_code_bits_), NULL) + PopulationCost(p->red_, NUM_LITERAL_CODES, NULL) + PopulationCost(p->blue_, NUM_LITERAL_CODES, NULL) + PopulationCost(p->alpha_, NUM_LITERAL_CODES, NULL) + PopulationCost(p->distance_, NUM_DISTANCE_CODES, NULL) + 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_; int trivial_at_end = 0; assert(a->palette_code_bits_ == b->palette_code_bits_); *cost += GetCombinedEntropy(a->literal_, b->literal_, VP8LHistogramNumCodes(palette_code_bits), 0); *cost += VP8LExtraCostCombined(a->literal_ + NUM_LITERAL_CODES, b->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES); if (*cost > cost_threshold) return 0; if (a->trivial_symbol_ != VP8L_NON_TRIVIAL_SYM && a->trivial_symbol_ == b->trivial_symbol_) { // A, R and B are all 0 or 0xff. const uint32_t color_a = (a->trivial_symbol_ >> 24) & 0xff; const uint32_t color_r = (a->trivial_symbol_ >> 16) & 0xff; const uint32_t color_b = (a->trivial_symbol_ >> 0) & 0xff; if ((color_a == 0 || color_a == 0xff) && (color_r == 0 || color_r == 0xff) && (color_b == 0 || color_b == 0xff)) { trivial_at_end = 1; } } *cost += GetCombinedEntropy(a->red_, b->red_, NUM_LITERAL_CODES, trivial_at_end); if (*cost > cost_threshold) return 0; *cost += GetCombinedEntropy(a->blue_, b->blue_, NUM_LITERAL_CODES, trivial_at_end); if (*cost > cost_threshold) return 0; *cost += GetCombinedEntropy(a->alpha_, b->alpha_, NUM_LITERAL_CODES, trivial_at_end); if (*cost > cost_threshold) return 0; *cost += GetCombinedEntropy(a->distance_, b->distance_, NUM_DISTANCE_CODES, 0); *cost += VP8LExtraCostCombined(a->distance_, b->distance_, NUM_DISTANCE_CODES); if (*cost > cost_threshold) return 0; return 1; } static WEBP_INLINE void HistogramAdd(const VP8LHistogram* const a, const VP8LHistogram* const b, VP8LHistogram* const out) { VP8LHistogramAdd(a, b, out); out->trivial_symbol_ = (a->trivial_symbol_ == b->trivial_symbol_) ? a->trivial_symbol_ : VP8L_NON_TRIVIAL_SYM; } // 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)) { HistogramAdd(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) { uint32_t alpha_sym, red_sym, blue_sym; const double alpha_cost = PopulationCost(h->alpha_, NUM_LITERAL_CODES, &alpha_sym); const double distance_cost = PopulationCost(h->distance_, NUM_DISTANCE_CODES, NULL) + VP8LExtraCost(h->distance_, NUM_DISTANCE_CODES); const int num_codes = VP8LHistogramNumCodes(h->palette_code_bits_); h->literal_cost_ = PopulationCost(h->literal_, num_codes, NULL) + VP8LExtraCost(h->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES); h->red_cost_ = PopulationCost(h->red_, NUM_LITERAL_CODES, &red_sym); h->blue_cost_ = PopulationCost(h->blue_, NUM_LITERAL_CODES, &blue_sym); h->bit_cost_ = h->literal_cost_ + h->red_cost_ + h->blue_cost_ + alpha_cost + distance_cost; if ((alpha_sym | red_sym | blue_sym) == VP8L_NON_TRIVIAL_SYM) { h->trivial_symbol_ = VP8L_NON_TRIVIAL_SYM; } else { h->trivial_symbol_ = ((uint32_t)alpha_sym << 24) | (red_sym << 16) | (blue_sym << 0); } } static int GetBinIdForEntropy(double min, double max, double val) { const double range = max - min; if (range > 0.) { const double delta = val - min; return (int)((NUM_PARTITIONS - 1e-6) * delta / range); } else { return 0; } } static int GetHistoBinIndex(const VP8LHistogram* const h, const DominantCostRange* const c, int low_effort) { int bin_id = GetBinIdForEntropy(c->literal_min_, c->literal_max_, h->literal_cost_); assert(bin_id < NUM_PARTITIONS); if (!low_effort) { bin_id = bin_id * NUM_PARTITIONS + GetBinIdForEntropy(c->red_min_, c->red_max_, h->red_cost_); bin_id = bin_id * NUM_PARTITIONS + GetBinIdForEntropy(c->blue_min_, c->blue_max_, h->blue_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); 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, NULL, 0); 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, uint16_t* const bin_map, int low_effort) { int i; VP8LHistogram** const histograms = image_histo->histograms; const int histo_size = image_histo->size; DominantCostRange cost_range; DominantCostRangeInit(&cost_range); // Analyze the dominant (literal, red and blue) entropy costs. for (i = 0; i < histo_size; ++i) { UpdateDominantCostRange(histograms[i], &cost_range); } // bin-hash histograms on three of the dominant (literal, red and blue) // symbol costs and store the resulting bin_id for each histogram. for (i = 0; i < histo_size; ++i) { bin_map[i] = GetHistoBinIndex(histograms[i], &cost_range, low_effort); } } // Compact image_histo[] by merging some histograms with same bin_id together if // it's advantageous. static void HistogramCombineEntropyBin(VP8LHistogramSet* const image_histo, VP8LHistogram* cur_combo, const uint16_t* const bin_map, int bin_map_size, int num_bins, double combine_cost_factor, int low_effort) { VP8LHistogram** const histograms = image_histo->histograms; int idx; // Work in-place: processed histograms are put at the beginning of // image_histo[]. At the end, we just have to truncate the array. int size = 0; struct { int16_t first; // position of the histogram that accumulates all // histograms with the same bin_id uint16_t num_combine_failures; // number of combine failures per bin_id } bin_info[BIN_SIZE]; assert(num_bins <= BIN_SIZE); for (idx = 0; idx < num_bins; ++idx) { bin_info[idx].first = -1; bin_info[idx].num_combine_failures = 0; } for (idx = 0; idx < bin_map_size; ++idx) { const int bin_id = bin_map[idx]; const int first = bin_info[bin_id].first; assert(size <= idx); if (first == -1) { // just move histogram #idx to its final position histograms[size] = histograms[idx]; bin_info[bin_id].first = size++; } else if (low_effort) { HistogramAdd(histograms[idx], histograms[first], histograms[first]); } else { // try to merge #idx into #first (both share the same bin_id) const double bit_cost = histograms[idx]->bit_cost_; const double bit_cost_thresh = -bit_cost * combine_cost_factor; const double curr_cost_diff = HistogramAddEval(histograms[first], histograms[idx], cur_combo, bit_cost_thresh); if (curr_cost_diff < bit_cost_thresh) { // Try to merge two histograms only if the combo is a trivial one or // the two candidate histograms are already non-trivial. // For some images, 'try_combine' turns out to be false for a lot of // histogram pairs. In that case, we fallback to combining // histograms as usual to avoid increasing the header size. const int try_combine = (cur_combo->trivial_symbol_ != VP8L_NON_TRIVIAL_SYM) || ((histograms[idx]->trivial_symbol_ == VP8L_NON_TRIVIAL_SYM) && (histograms[first]->trivial_symbol_ == VP8L_NON_TRIVIAL_SYM)); const int max_combine_failures = 32; if (try_combine || bin_info[bin_id].num_combine_failures >= max_combine_failures) { // move the (better) merged histogram to its final slot HistogramSwap(&cur_combo, &histograms[first]); } else { histograms[size++] = histograms[idx]; ++bin_info[bin_id].num_combine_failures; } } else { histograms[size++] = histograms[idx]; } } } image_histo->size = size; if (low_effort) { // for low_effort case, update the final cost when everything is merged for (idx = 0; idx < size; ++idx) { UpdateHistogramCost(histograms[idx]); } } } // Implement a Lehmer random number generator with a multiplicative constant of // 48271 and a modulo constant of 2^31 − 1. static uint32_t MyRand(uint32_t* const seed) { *seed = (uint32_t)(((uint64_t)(*seed) * 48271u) % 2147483647u); assert(*seed > 0); return *seed; } // ----------------------------------------------------------------------------- // Histogram pairs priority queue // Pair of histograms. Negative idx1 value means that pair is out-of-date. typedef struct { int idx1; int idx2; double cost_diff; double cost_combo; } HistogramPair; typedef struct { HistogramPair* queue; int size; int max_size; } HistoQueue; static int HistoQueueInit(HistoQueue* const histo_queue, const int max_index) { histo_queue->size = 0; // max_index^2 for the queue size is safe. If you look at // HistogramCombineGreedy, and imagine that UpdateQueueFront always pushes // data to the queue, you insert at most: // - max_index*(max_index-1)/2 (the first two for loops) // - max_index - 1 in the last for loop at the first iteration of the while // loop, max_index - 2 at the second iteration ... therefore // max_index*(max_index-1)/2 overall too histo_queue->max_size = max_index * max_index; // We allocate max_size + 1 because the last element at index "size" is // used as temporary data (and it could be up to max_size). histo_queue->queue = (HistogramPair*)WebPSafeMalloc( histo_queue->max_size + 1, sizeof(*histo_queue->queue)); return histo_queue->queue != NULL; } static void HistoQueueClear(HistoQueue* const histo_queue) { assert(histo_queue != NULL); WebPSafeFree(histo_queue->queue); histo_queue->size = 0; histo_queue->max_size = 0; } // Pop a specific pair in the queue by replacing it with the last one // and shrinking the queue. static void HistoQueuePopPair(HistoQueue* const histo_queue, HistogramPair* const pair) { assert(pair >= histo_queue->queue && pair < (histo_queue->queue + histo_queue->size)); assert(histo_queue->size > 0); *pair = histo_queue->queue[histo_queue->size - 1]; --histo_queue->size; } // Check whether a pair in the queue should be updated as head or not. static void HistoQueueUpdateHead(HistoQueue* const histo_queue, HistogramPair* const pair) { assert(pair->cost_diff < 0.); assert(pair >= histo_queue->queue && pair < (histo_queue->queue + histo_queue->size)); assert(histo_queue->size > 0); if (pair->cost_diff < histo_queue->queue[0].cost_diff) { // Replace the best pair. const HistogramPair tmp = histo_queue->queue[0]; histo_queue->queue[0] = *pair; *pair = tmp; } } // Create a pair from indices "idx1" and "idx2" provided its cost // is inferior to "threshold", a negative entropy. // It returns the cost of the pair, or 0. if it superior to threshold. static double HistoQueuePush(HistoQueue* const histo_queue, VP8LHistogram** const histograms, int idx1, int idx2, double threshold) { const VP8LHistogram* h1; const VP8LHistogram* h2; HistogramPair pair; double sum_cost; assert(threshold <= 0.); if (idx1 > idx2) { const int tmp = idx2; idx2 = idx1; idx1 = tmp; } pair.idx1 = idx1; pair.idx2 = idx2; h1 = histograms[idx1]; h2 = histograms[idx2]; sum_cost = h1->bit_cost_ + h2->bit_cost_; pair.cost_combo = 0.; GetCombinedHistogramEntropy(h1, h2, sum_cost + threshold, &pair.cost_combo); pair.cost_diff = pair.cost_combo - sum_cost; // Do not even consider the pair if it does not improve the entropy. if (pair.cost_diff >= threshold) return 0.; // We cannot add more elements than the capacity. assert(histo_queue->size < histo_queue->max_size); histo_queue->queue[histo_queue->size++] = pair; HistoQueueUpdateHead(histo_queue, &histo_queue->queue[histo_queue->size - 1]); return pair.cost_diff; } // ----------------------------------------------------------------------------- // Combines histograms by continuously choosing the one with the highest cost // reduction. static int HistogramCombineGreedy(VP8LHistogramSet* const image_histo) { int ok = 0; int image_histo_size = image_histo->size; int i, j; VP8LHistogram** const histograms = image_histo->histograms; // Indexes of remaining histograms. int* const clusters = (int*)WebPSafeMalloc(image_histo_size, sizeof(*clusters)); // Priority queue of histogram pairs. HistoQueue histo_queue; if (!HistoQueueInit(&histo_queue, image_histo_size) || clusters == NULL) { goto End; } for (i = 0; i < image_histo_size; ++i) { // Initialize clusters indexes. clusters[i] = i; for (j = i + 1; j < image_histo_size; ++j) { // Initialize positions array. HistoQueuePush(&histo_queue, histograms, i, j, 0.); } } while (image_histo_size > 1 && histo_queue.size > 0) { const int idx1 = histo_queue.queue[0].idx1; const int idx2 = histo_queue.queue[0].idx2; HistogramAdd(histograms[idx2], histograms[idx1], histograms[idx1]); histograms[idx1]->bit_cost_ = histo_queue.queue[0].cost_combo; // Remove merged histogram. for (i = 0; i + 1 < image_histo_size; ++i) { if (clusters[i] >= idx2) { clusters[i] = clusters[i + 1]; } } --image_histo_size; // Remove pairs intersecting the just combined best pair. for (i = 0; i < histo_queue.size;) { HistogramPair* const p = histo_queue.queue + i; if (p->idx1 == idx1 || p->idx2 == idx1 || p->idx1 == idx2 || p->idx2 == idx2) { HistoQueuePopPair(&histo_queue, p); } else { HistoQueueUpdateHead(&histo_queue, p); ++i; } } // Push new pairs formed with combined histogram to the queue. for (i = 0; i < image_histo_size; ++i) { if (clusters[i] != idx1) { HistoQueuePush(&histo_queue, histograms, idx1, clusters[i], 0.); } } } // Move remaining histograms to the beginning of the array. for (i = 0; i < image_histo_size; ++i) { if (i != clusters[i]) { // swap the two histograms HistogramSwap(&histograms[i], &histograms[clusters[i]]); } } image_histo->size = image_histo_size; ok = 1; End: WebPSafeFree(clusters); HistoQueueClear(&histo_queue); return ok; } // Perform histogram aggregation using a stochastic approach. // 'do_greedy' is set to 1 if a greedy approach needs to be performed // afterwards, 0 otherwise. static int HistogramCombineStochastic(VP8LHistogramSet* const image_histo, int min_cluster_size, int* const do_greedy) { int iter; uint32_t seed = 1; int tries_with_no_success = 0; int image_histo_size = image_histo->size; const int outer_iters = image_histo_size; const int num_tries_no_success = outer_iters / 2; VP8LHistogram** const histograms = image_histo->histograms; // Priority queue of histogram pairs. Its size of "kCostHeapSizeSqrt"^2 // impacts the quality of the compression and the speed: the smaller the // faster but the worse for the compression. HistoQueue histo_queue; const int kHistoQueueSizeSqrt = 3; int ok = 0; if (!HistoQueueInit(&histo_queue, kHistoQueueSizeSqrt)) { goto End; } // Collapse similar histograms in 'image_histo'. ++min_cluster_size; for (iter = 0; iter < outer_iters && image_histo_size >= min_cluster_size && ++tries_with_no_success < num_tries_no_success; ++iter) { double best_cost = (histo_queue.size == 0) ? 0. : histo_queue.queue[0].cost_diff; int best_idx1 = -1, best_idx2 = 1; int j; const uint32_t rand_range = (image_histo_size - 1) * image_histo_size; // image_histo_size / 2 was chosen empirically. Less means faster but worse // compression. const int num_tries = image_histo_size / 2; for (j = 0; j < num_tries; ++j) { double curr_cost; // Choose two different histograms at random and try to combine them. const uint32_t tmp = MyRand(&seed) % rand_range; const uint32_t idx1 = tmp / (image_histo_size - 1); uint32_t idx2 = tmp % (image_histo_size - 1); if (idx2 >= idx1) ++idx2; // Calculate cost reduction on combination. curr_cost = HistoQueuePush(&histo_queue, histograms, idx1, idx2, best_cost); if (curr_cost < 0) { // found a better pair? best_cost = curr_cost; // Empty the queue if we reached full capacity. if (histo_queue.size == histo_queue.max_size) break; } } if (histo_queue.size == 0) continue; // Merge the two best histograms. best_idx1 = histo_queue.queue[0].idx1; best_idx2 = histo_queue.queue[0].idx2; assert(best_idx1 < best_idx2); HistogramAddEval(histograms[best_idx1], histograms[best_idx2], histograms[best_idx1], 0); // Swap the best_idx2 histogram with the last one (which is now unused). --image_histo_size; if (best_idx2 != image_histo_size) { HistogramSwap(&histograms[image_histo_size], &histograms[best_idx2]); } histograms[image_histo_size] = NULL; // Parse the queue and update each pair that deals with best_idx1, // best_idx2 or image_histo_size. for (j = 0; j < histo_queue.size;) { HistogramPair* const p = histo_queue.queue + j; const int is_idx1_best = p->idx1 == best_idx1 || p->idx1 == best_idx2; const int is_idx2_best = p->idx2 == best_idx1 || p->idx2 == best_idx2; int do_eval = 0; // The front pair could have been duplicated by a random pick so // check for it all the time nevertheless. if (is_idx1_best && is_idx2_best) { HistoQueuePopPair(&histo_queue, p); continue; } // Any pair containing one of the two best indices should only refer to // best_idx1. Its cost should also be updated. if (is_idx1_best) { p->idx1 = best_idx1; do_eval = 1; } else if (is_idx2_best) { p->idx2 = best_idx1; do_eval = 1; } if (p->idx2 == image_histo_size) { // No need to re-evaluate here as it does not involve a pair // containing best_idx1 or best_idx2. p->idx2 = best_idx2; } assert(p->idx2 < image_histo_size); // Make sure the index order is respected. if (p->idx1 > p->idx2) { const int tmp = p->idx2; p->idx2 = p->idx1; p->idx1 = tmp; } if (do_eval) { // Re-evaluate the cost of an updated pair. GetCombinedHistogramEntropy(histograms[p->idx1], histograms[p->idx2], 0, &p->cost_diff); if (p->cost_diff >= 0.) { HistoQueuePopPair(&histo_queue, p); continue; } } HistoQueueUpdateHead(&histo_queue, p); ++j; } tries_with_no_success = 0; } image_histo->size = image_histo_size; *do_greedy = (image_histo->size <= min_cluster_size); ok = 1; End: HistoQueueClear(&histo_queue); return ok; } // ----------------------------------------------------------------------------- // 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 in, const VP8LHistogramSet* const out, uint16_t* const symbols) { int i; VP8LHistogram** const in_histo = in->histograms; VP8LHistogram** const out_histo = out->histograms; const int in_size = in->size; const int out_size = out->size; if (out_size > 1) { for (i = 0; i < in_size; ++i) { int best_out = 0; double best_bits = MAX_COST; int k; for (k = 0; k < out_size; ++k) { const double cur_bits = HistogramAddThresh(out_histo[k], in_histo[i], best_bits); if (k == 0 || cur_bits < best_bits) { best_bits = cur_bits; best_out = k; } } symbols[i] = best_out; } } else { assert(out_size == 1); for (i = 0; i < in_size; ++i) { symbols[i] = 0; } } // Recompute each out based on raw and symbols. for (i = 0; i < out_size; ++i) { HistogramClear(out_histo[i]); } for (i = 0; i < in_size; ++i) { const int idx = symbols[i]; HistogramAdd(in_histo[i], out_histo[idx], out_histo[idx]); } } static double GetCombineCostFactor(int histo_size, int quality) { double combine_cost_factor = 0.16; if (quality < 90) { 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 low_effort, int histo_bits, int cache_bits, VP8LHistogramSet* const image_histo, VP8LHistogram* const tmp_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; VP8LHistogramSet* const orig_histo = VP8LAllocateHistogramSet(image_histo_raw_size, cache_bits); // Don't attempt linear bin-partition heuristic for // histograms of small sizes (as bin_map will be very sparse) and // maximum quality q==100 (to preserve the compression gains at that level). const int entropy_combine_num_bins = low_effort ? NUM_PARTITIONS : BIN_SIZE; const int entropy_combine = (orig_histo->size > entropy_combine_num_bins * 2) && (quality < 100); if (orig_histo == 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 (entropy_combine) { const int bin_map_size = orig_histo->size; // Reuse histogram_symbols storage. By definition, it's guaranteed to be ok. uint16_t* const bin_map = histogram_symbols; const double combine_cost_factor = GetCombineCostFactor(image_histo_raw_size, quality); HistogramAnalyzeEntropyBin(orig_histo, bin_map, low_effort); // Collapse histograms with similar entropy. HistogramCombineEntropyBin(image_histo, tmp_histo, bin_map, bin_map_size, entropy_combine_num_bins, combine_cost_factor, low_effort); } // Don't combine the histograms using stochastic and greedy heuristics for // low-effort compression mode. if (!low_effort || !entropy_combine) { const float x = quality / 100.f; // cubic ramp between 1 and MAX_HISTO_GREEDY: const int threshold_size = (int)(1 + (x * x * x) * (MAX_HISTO_GREEDY - 1)); int do_greedy; if (!HistogramCombineStochastic(image_histo, threshold_size, &do_greedy)) { goto Error; } if (do_greedy && !HistogramCombineGreedy(image_histo)) { goto Error; } } // TODO(vikasa): Optimize HistogramRemap for low-effort compression mode also. // Find the optimal map from original histograms to the final ones. HistogramRemap(orig_histo, image_histo, histogram_symbols); ok = 1; Error: VP8LFreeHistogramSet(orig_histo); return ok; }