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Diffstat (limited to 'thirdparty/libwebp/src/enc/histogram_enc.c')
| -rw-r--r-- | thirdparty/libwebp/src/enc/histogram_enc.c | 1043 |
1 files changed, 1043 insertions, 0 deletions
diff --git a/thirdparty/libwebp/src/enc/histogram_enc.c b/thirdparty/libwebp/src/enc/histogram_enc.c new file mode 100644 index 0000000000..056a972dda --- /dev/null +++ b/thirdparty/libwebp/src/enc/histogram_enc.c @@ -0,0 +1,1043 @@ +// 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; +} |