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2073ead37e
Nagram/TMessagesProj/jni/libwebp/enc/histogram.c
DrKLO 2073ead37e Update to 2.3.0
2015-01-03 01:15:07 +03:00

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// 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 <math.h>
#include "./backward_references.h"
#include "./histogram.h"
#include "../dsp/lossless.h"
#include "../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)
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 size_t total_size = sizeof(*set)
+ sizeof(*set->histograms) * size
+ (size_t)VP8LGetHistogramSize(cache_bits) * size;
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) {
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);
// There's no padding/alignment between successive histograms.
memory += VP8LGetHistogramSize(cache_bits);
}
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;
}
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