Index: third_party/libwebp/enc/histogram.c |
diff --git a/third_party/libwebp/enc/histogram.c b/third_party/libwebp/enc/histogram.c |
index ca838e064dc10e299e2ed29308d9ae17e86798f0..69e5fa36e1503d2ade6b5996ebe50bbc2e28d000 100644 |
--- a/third_party/libwebp/enc/histogram.c |
+++ b/third_party/libwebp/enc/histogram.c |
@@ -55,9 +55,9 @@ VP8LHistogramSet* VP8LAllocateHistogramSet(int size, int cache_bits) { |
int i; |
VP8LHistogramSet* set; |
VP8LHistogram* bulk; |
- const uint64_t total_size = (uint64_t)sizeof(*set) |
- + size * sizeof(*set->histograms) |
- + size * sizeof(**set->histograms); |
+ const uint64_t total_size = sizeof(*set) |
+ + (uint64_t)size * sizeof(*set->histograms) |
+ + (uint64_t)size * sizeof(**set->histograms); |
uint8_t* memory = (uint8_t*)WebPSafeMalloc(total_size, sizeof(*memory)); |
if (memory == NULL) return NULL; |
@@ -98,8 +98,6 @@ void VP8LHistogramAddSinglePixOrCopy(VP8LHistogram* const histo, |
} |
} |
- |
- |
static double BitsEntropy(const int* const array, int n) { |
double retval = 0.; |
int sum = 0; |
@@ -149,25 +147,6 @@ static double BitsEntropy(const int* const array, int n) { |
} |
} |
-double VP8LHistogramEstimateBitsBulk(const VP8LHistogram* const p) { |
- double retval = BitsEntropy(&p->literal_[0], VP8LHistogramNumCodes(p)) |
- + BitsEntropy(&p->red_[0], 256) |
- + BitsEntropy(&p->blue_[0], 256) |
- + BitsEntropy(&p->alpha_[0], 256) |
- + BitsEntropy(&p->distance_[0], NUM_DISTANCE_CODES); |
- // Compute the extra bits cost. |
- int i; |
- for (i = 2; i < NUM_LENGTH_CODES - 2; ++i) { |
- retval += |
- (i >> 1) * p->literal_[256 + i + 2]; |
- } |
- for (i = 2; i < NUM_DISTANCE_CODES - 2; ++i) { |
- retval += (i >> 1) * p->distance_[i + 2]; |
- } |
- return retval; |
-} |
- |
- |
// Returns the cost encode the rle-encoded entropy code. |
// The constants in this function are experimental. |
static double HuffmanCost(const int* const population, int length) { |
@@ -207,19 +186,150 @@ static double HuffmanCost(const int* const population, int length) { |
return retval; |
} |
-// Estimates the Huffman dictionary + other block overhead size. |
-static double HistogramEstimateBitsHeader(const VP8LHistogram* const p) { |
- return HuffmanCost(&p->alpha_[0], 256) + |
- HuffmanCost(&p->red_[0], 256) + |
- HuffmanCost(&p->literal_[0], VP8LHistogramNumCodes(p)) + |
- HuffmanCost(&p->blue_[0], 256) + |
- HuffmanCost(&p->distance_[0], NUM_DISTANCE_CODES); |
+static double PopulationCost(const int* const population, int length) { |
+ return BitsEntropy(population, length) + HuffmanCost(population, length); |
+} |
+ |
+static double ExtraCost(const int* const population, int length) { |
+ int i; |
+ double cost = 0.; |
+ for (i = 2; i < length - 2; ++i) cost += (i >> 1) * population[i + 2]; |
+ return cost; |
} |
+// Estimates the Entropy + Huffman + other block overhead size cost. |
double VP8LHistogramEstimateBits(const VP8LHistogram* const p) { |
- return HistogramEstimateBitsHeader(p) + VP8LHistogramEstimateBitsBulk(p); |
+ return PopulationCost(p->literal_, VP8LHistogramNumCodes(p)) |
+ + PopulationCost(p->red_, 256) |
+ + PopulationCost(p->blue_, 256) |
+ + PopulationCost(p->alpha_, 256) |
+ + PopulationCost(p->distance_, NUM_DISTANCE_CODES) |
+ + ExtraCost(p->literal_ + 256, NUM_LENGTH_CODES) |
+ + ExtraCost(p->distance_, NUM_DISTANCE_CODES); |
+} |
+ |
+double VP8LHistogramEstimateBitsBulk(const VP8LHistogram* const p) { |
+ return BitsEntropy(p->literal_, VP8LHistogramNumCodes(p)) |
+ + BitsEntropy(p->red_, 256) |
+ + BitsEntropy(p->blue_, 256) |
+ + BitsEntropy(p->alpha_, 256) |
+ + BitsEntropy(p->distance_, NUM_DISTANCE_CODES) |
+ + ExtraCost(p->literal_ + 256, NUM_LENGTH_CODES) |
+ + ExtraCost(p->distance_, NUM_DISTANCE_CODES); |
+} |
+ |
+// ----------------------------------------------------------------------------- |
+// Various histogram combine/cost-eval functions |
+ |
+// Adds 'in' histogram to 'out' |
+static void HistogramAdd(const VP8LHistogram* const in, |
+ VP8LHistogram* const out) { |
+ int i; |
+ for (i = 0; i < PIX_OR_COPY_CODES_MAX; ++i) { |
+ out->literal_[i] += in->literal_[i]; |
+ } |
+ for (i = 0; i < NUM_DISTANCE_CODES; ++i) { |
+ out->distance_[i] += in->distance_[i]; |
+ } |
+ for (i = 0; i < 256; ++i) { |
+ out->red_[i] += in->red_[i]; |
+ out->blue_[i] += in->blue_[i]; |
+ out->alpha_[i] += in->alpha_[i]; |
+ } |
+} |
+ |
+// 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_; |
+ int i; |
+ |
+ cost_threshold += sum_cost; |
+ |
+ // palette_code_bits_ is part of the cost evaluation for literal_. |
+ // TODO(skal): remove/simplify this palette_code_bits_? |
+ out->palette_code_bits_ = |
+ (a->palette_code_bits_ > b->palette_code_bits_) ? a->palette_code_bits_ : |
+ b->palette_code_bits_; |
+ for (i = 0; i < PIX_OR_COPY_CODES_MAX; ++i) { |
+ out->literal_[i] = a->literal_[i] + b->literal_[i]; |
+ } |
+ cost += PopulationCost(out->literal_, VP8LHistogramNumCodes(out)); |
+ cost += ExtraCost(out->literal_ + 256, NUM_LENGTH_CODES); |
+ if (cost > cost_threshold) return cost; |
+ |
+ for (i = 0; i < 256; ++i) out->red_[i] = a->red_[i] + b->red_[i]; |
+ cost += PopulationCost(out->red_, 256); |
+ if (cost > cost_threshold) return cost; |
+ |
+ for (i = 0; i < 256; ++i) out->blue_[i] = a->blue_[i] + b->blue_[i]; |
+ cost += PopulationCost(out->blue_, 256); |
+ if (cost > cost_threshold) return cost; |
+ |
+ for (i = 0; i < NUM_DISTANCE_CODES; ++i) { |
+ out->distance_[i] = a->distance_[i] + b->distance_[i]; |
+ } |
+ cost += PopulationCost(out->distance_, NUM_DISTANCE_CODES); |
+ cost += ExtraCost(out->distance_, NUM_DISTANCE_CODES); |
+ if (cost > cost_threshold) return cost; |
+ |
+ for (i = 0; i < 256; ++i) out->alpha_[i] = a->alpha_[i] + b->alpha_[i]; |
+ cost += PopulationCost(out->alpha_, 256); |
+ |
+ out->bit_cost_ = cost; |
+ 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) { |
+ int tmp[PIX_OR_COPY_CODES_MAX]; // <= max storage we'll need |
+ int i; |
+ double cost = -a->bit_cost_; |
+ |
+ for (i = 0; i < PIX_OR_COPY_CODES_MAX; ++i) { |
+ tmp[i] = a->literal_[i] + b->literal_[i]; |
+ } |
+ // note that the tests are ordered so that the usually largest |
+ // cost shares come first. |
+ cost += PopulationCost(tmp, VP8LHistogramNumCodes(a)); |
+ cost += ExtraCost(tmp + 256, NUM_LENGTH_CODES); |
+ if (cost > cost_threshold) return cost; |
+ |
+ for (i = 0; i < 256; ++i) tmp[i] = a->red_[i] + b->red_[i]; |
+ cost += PopulationCost(tmp, 256); |
+ if (cost > cost_threshold) return cost; |
+ |
+ for (i = 0; i < 256; ++i) tmp[i] = a->blue_[i] + b->blue_[i]; |
+ cost += PopulationCost(tmp, 256); |
+ if (cost > cost_threshold) return cost; |
+ |
+ for (i = 0; i < NUM_DISTANCE_CODES; ++i) { |
+ tmp[i] = a->distance_[i] + b->distance_[i]; |
+ } |
+ cost += PopulationCost(tmp, NUM_DISTANCE_CODES); |
+ cost += ExtraCost(tmp, NUM_DISTANCE_CODES); |
+ if (cost > cost_threshold) return cost; |
+ |
+ for (i = 0; i < 256; ++i) tmp[i] = a->alpha_[i] + b->alpha_[i]; |
+ cost += PopulationCost(tmp, 256); |
+ |
+ return cost; |
+} |
+ |
+// ----------------------------------------------------------------------------- |
+ |
static void HistogramBuildImage(int xsize, int histo_bits, |
const VP8LBackwardRefs* const backward_refs, |
VP8LHistogramSet* const image) { |
@@ -249,14 +359,15 @@ static uint32_t MyRand(uint32_t *seed) { |
} |
static int HistogramCombine(const VP8LHistogramSet* const in, |
- VP8LHistogramSet* const out, int num_pairs) { |
+ VP8LHistogramSet* const out, int iter_mult, |
+ int num_pairs, int num_tries_no_success) { |
int ok = 0; |
int i, iter; |
uint32_t seed = 0; |
int tries_with_no_success = 0; |
- const int min_cluster_size = 2; |
int out_size = in->size; |
- const int outer_iters = in->size * 3; |
+ const int outer_iters = in->size * iter_mult; |
+ const int min_cluster_size = 2; |
VP8LHistogram* const histos = (VP8LHistogram*)malloc(2 * sizeof(*histos)); |
VP8LHistogram* cur_combo = histos + 0; // trial merged histogram |
VP8LHistogram* best_combo = histos + 1; // best merged histogram so far |
@@ -271,29 +382,26 @@ static int HistogramCombine(const VP8LHistogramSet* const in, |
// Collapse similar histograms in 'out'. |
for (iter = 0; iter < outer_iters && out_size >= min_cluster_size; ++iter) { |
- // We pick the best pair to be combined out of 'inner_iters' pairs. |
double best_cost_diff = 0.; |
- int best_idx1 = 0, best_idx2 = 1; |
+ int best_idx1 = -1, best_idx2 = 1; |
int j; |
+ const int num_tries = (num_pairs < out_size) ? num_pairs : out_size; |
seed += iter; |
- for (j = 0; j < num_pairs; ++j) { |
+ 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) % out_size; |
- const uint32_t tmp = ((j & 7) + 1) % (out_size - 1); |
+ const uint32_t tmp = (j & 7) + 1; |
const uint32_t diff = (tmp < 3) ? tmp : MyRand(&seed) % (out_size - 1); |
const uint32_t idx2 = (idx1 + diff + 1) % out_size; |
if (idx1 == idx2) { |
continue; |
} |
- *cur_combo = *out->histograms[idx1]; |
- VP8LHistogramAdd(cur_combo, out->histograms[idx2]); |
- cur_combo->bit_cost_ = VP8LHistogramEstimateBits(cur_combo); |
// Calculate cost reduction on combining. |
- curr_cost_diff = cur_combo->bit_cost_ |
- - out->histograms[idx1]->bit_cost_ |
- - out->histograms[idx2]->bit_cost_; |
- if (best_cost_diff > curr_cost_diff) { // found a better pair? |
+ curr_cost_diff = HistogramAddEval(out->histograms[idx1], |
+ out->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; |
@@ -305,7 +413,7 @@ static int HistogramCombine(const VP8LHistogramSet* const in, |
} |
} |
- if (best_cost_diff < 0.0) { |
+ if (best_idx1 >= 0) { |
*out->histograms[best_idx1] = *best_combo; |
// swap best_idx2 slot with last one (which is now unused) |
--out_size; |
@@ -315,7 +423,7 @@ static int HistogramCombine(const VP8LHistogramSet* const in, |
} |
tries_with_no_success = 0; |
} |
- if (++tries_with_no_success >= 50) { |
+ if (++tries_with_no_success >= num_tries_no_success) { |
break; |
} |
} |
@@ -330,20 +438,11 @@ static int HistogramCombine(const VP8LHistogramSet* const in, |
// ----------------------------------------------------------------------------- |
// Histogram refinement |
-// What is the bit cost of moving square_histogram from |
-// cur_symbol to candidate_symbol. |
-// TODO(skal): we don't really need to copy the histogram and Add(). Instead |
-// we just need VP8LDualHistogramEstimateBits(A, B) estimation function. |
+// What is the bit cost of moving square_histogram from cur_symbol to candidate. |
static double HistogramDistance(const VP8LHistogram* const square_histogram, |
- const VP8LHistogram* const candidate) { |
- const double previous_bit_cost = candidate->bit_cost_; |
- double new_bit_cost; |
- VP8LHistogram modified_histo; |
- modified_histo = *candidate; |
- VP8LHistogramAdd(&modified_histo, square_histogram); |
- new_bit_cost = VP8LHistogramEstimateBits(&modified_histo); |
- |
- return new_bit_cost - previous_bit_cost; |
+ const VP8LHistogram* const candidate, |
+ double cost_threshold) { |
+ return HistogramAddThresh(candidate, square_histogram, cost_threshold); |
} |
// Find the best 'out' histogram for each of the 'in' histograms. |
@@ -354,11 +453,12 @@ static void HistogramRemap(const VP8LHistogramSet* const in, |
int i; |
for (i = 0; i < in->size; ++i) { |
int best_out = 0; |
- double best_bits = HistogramDistance(in->histograms[i], out->histograms[0]); |
+ double best_bits = |
+ HistogramDistance(in->histograms[i], out->histograms[0], 1.e38); |
int k; |
for (k = 1; k < out->size; ++k) { |
const double cur_bits = |
- HistogramDistance(in->histograms[i], out->histograms[k]); |
+ HistogramDistance(in->histograms[i], out->histograms[k], best_bits); |
if (cur_bits < best_bits) { |
best_bits = cur_bits; |
best_out = k; |
@@ -372,7 +472,7 @@ static void HistogramRemap(const VP8LHistogramSet* const in, |
HistogramClear(out->histograms[i]); |
} |
for (i = 0; i < in->size; ++i) { |
- VP8LHistogramAdd(out->histograms[symbols[i]], in->histograms[i]); |
+ HistogramAdd(in->histograms[i], out->histograms[symbols[i]]); |
} |
} |
@@ -384,8 +484,13 @@ int VP8LGetHistoImageSymbols(int xsize, int ysize, |
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 num_histo_pairs = 10 + quality / 2; // For HistogramCombine(). |
const int histo_image_raw_size = histo_xsize * histo_ysize; |
+ |
+ // Heuristic params for HistogramCombine(). |
+ const int num_tries_no_success = 8 + (quality >> 1); |
+ const int iter_mult = (quality < 27) ? 1 : 1 + ((quality - 27) >> 4); |
+ const int num_pairs = (quality < 25) ? 10 : (5 * quality) >> 3; |
+ |
VP8LHistogramSet* const image_out = |
VP8LAllocateHistogramSet(histo_image_raw_size, cache_bits); |
if (image_out == NULL) return 0; |
@@ -393,7 +498,8 @@ int VP8LGetHistoImageSymbols(int xsize, int ysize, |
// Build histogram image. |
HistogramBuildImage(xsize, histo_bits, refs, image_out); |
// Collapse similar histograms. |
- if (!HistogramCombine(image_out, image_in, num_histo_pairs)) { |
+ if (!HistogramCombine(image_out, image_in, iter_mult, num_pairs, |
+ num_tries_no_success)) { |
goto Error; |
} |
// Find the optimal map from original histograms to the final ones. |