// 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
// 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
// Return the size of the histogram for a given cache_bits. staticint GetHistogramSize(int cache_bits) { constint 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;
}
// Resets the pointers of the histograms to point to the bit buffer in the set. staticvoid HistogramSetResetPointers(VP8LHistogramSet* const set, int cache_bits) { int i; constint histo_size = GetHistogramSize(cache_bits);
uint8_t* memory = (uint8_t*) (set->histograms);
memory += set->max_size * sizeof(*set->histograms); for (i = 0; i < set->max_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));
memory += histo_size;
}
}
// Returns the total size of the VP8LHistogramSet. static size_t HistogramSetTotalSize(int size, int cache_bits) { constint histo_size = GetHistogramSize(cache_bits); return (sizeof(VP8LHistogramSet) + size * (sizeof(VP8LHistogram*) +
histo_size + WEBP_ALIGN_CST));
}
VP8LHistogramSet* VP8LAllocateHistogramSet(int size, int cache_bits) { int i;
VP8LHistogramSet* set; const size_t total_size = HistogramSetTotalSize(size, cache_bits);
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;
set->max_size = size;
set->size = size;
HistogramSetResetPointers(set, cache_bits); for (i = 0; i < size; ++i) {
VP8LHistogramInit(set->histograms[i], cache_bits, /*init_arrays=*/ 0);
} return set;
}
// Removes the histogram 'i' from 'set' by setting it to NULL. staticvoid HistogramSetRemoveHistogram(VP8LHistogramSet* const set, int i, int* const num_used) {
assert(set->histograms[i] != NULL);
set->histograms[i] = NULL;
--*num_used; // If we remove the last valid one, shrink until the next valid one. if (i == set->size - 1) { while (set->size >= 1 && set->histograms[set->size - 1] == NULL) {
--set->size;
}
}
}
static WEBP_INLINE uint64_t BitsEntropyRefine(const VP8LBitEntropy* entropy) {
uint64_t 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 DivRound(99 * ((uint64_t)entropy->sum << LOG_2_PRECISION_BITS) +
entropy->entropy,
100);
} // 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 = 950;
} else {
mix = 700; // nonzeros == 4.
}
} else {
mix = 627;
}
uint64_t VP8LBitsEntropy(const uint32_t* const array, int n) {
VP8LBitEntropy entropy;
VP8LBitsEntropyUnrefined(array, n, &entropy);
return BitsEntropyRefine(&entropy);
}
static uint64_t InitialHuffmanCost(void) { // Small bias because Huffman code length is typically not stored in // full length. staticconst uint64_t kHuffmanCodeOfHuffmanCodeSize = CODE_LENGTH_CODES * 3; // Subtract a bias of 9.1. return (kHuffmanCodeOfHuffmanCodeSize << LOG_2_PRECISION_BITS) -
DivRound(91ll << LOG_2_PRECISION_BITS, 10);
}
// Finalize the Huffman cost based on streak numbers and length type (<3 or >=3) static uint64_t FinalHuffmanCost(const VP8LStreaks* const stats) { // The constants in this function are empirical and got rounded from // their original values in 1/8 when switched to 1/1024.
uint64_t retval = InitialHuffmanCost(); // Second coefficient: Many zeros in the histogram are covered efficiently // by a run-length encode. Originally 2/8.
uint32_t retval_extra = stats->counts[0] * 1600 + 240 * stats->streaks[0][1]; // Second coefficient: Constant values are encoded less efficiently, but still // RLE'ed. Originally 6/8.
retval_extra += stats->counts[1] * 2640 + 720 * stats->streaks[1][1]; // 0s are usually encoded more efficiently than non-0s. // Originally 15/8.
retval_extra += 1840 * stats->streaks[0][0]; // Originally 26/8.
retval_extra += 3360 * stats->streaks[1][0]; return retval + ((uint64_t)retval_extra << (LOG_2_PRECISION_BITS - 10));
}
// Get the symbol entropy for the distribution 'population'. // Set 'trivial_sym', if there's only one symbol present in the distribution. static uint64_t PopulationCost(const uint32_t* const population, int length,
uint32_t* const trivial_sym,
uint8_t* const is_used) {
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;
} // The histogram is used if there is at least one non-zero streak.
*is_used = (stats.streaks[1][0] != 0 || stats.streaks[1][1] != 0);
// 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 uint64_t GetCombinedEntropy(const uint32_t* const X, const uint32_t* const Y, int length, int is_X_used, int is_Y_used, 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; if (is_X_used) { if (is_Y_used) {
VP8LGetCombinedEntropyUnrefined(X, Y, length, &bit_entropy, &stats);
} else {
VP8LGetEntropyUnrefined(X, length, &bit_entropy, &stats);
}
} else { if (is_Y_used) {
VP8LGetEntropyUnrefined(Y, length, &bit_entropy, &stats);
} else {
memset(&stats, 0, sizeof(stats));
stats.counts[0] = 1;
stats.streaks[0][length > 3] = length;
VP8LBitEntropyInit(&bit_entropy);
}
}
// ----------------------------------------------------------------------------- // Various histogram combine/cost-eval functions
// Set a + b in b, saturating at WEBP_INT64_MAX. static WEBP_INLINE void SaturateAdd(uint64_t a, int64_t* b) { if (*b < 0 || (int64_t)a <= WEBP_INT64_MAX - *b) {
*b += (int64_t)a;
} else {
*b = WEBP_INT64_MAX;
}
}
// Returns 1 if the cost of the combined histogram is less than the threshold. // Otherwise returns 0 and the cost is invalid due to early bail-out.
WEBP_NODISCARD staticint GetCombinedHistogramEntropy( const VP8LHistogram* const a, const VP8LHistogram* const b,
int64_t cost_threshold_in, uint64_t* cost) { constint palette_code_bits = a->palette_code_bits_; int trivial_at_end = 0; const uint64_t cost_threshold = (uint64_t)cost_threshold_in;
assert(a->palette_code_bits_ == b->palette_code_bits_); if (cost_threshold_in <= 0) return 0;
*cost = GetCombinedEntropy(a->literal_, b->literal_,
VP8LHistogramNumCodes(palette_code_bits),
a->is_used_[0], b->is_used_[0], 0);
*cost += (uint64_t)VP8LExtraCostCombined(a->literal_ + NUM_LITERAL_CODES,
b->literal_ + NUM_LITERAL_CODES,
NUM_LENGTH_CODES)
<< LOG_2_PRECISION_BITS; 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;
}
}
// 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. // Returns 1 if the cost is less than the threshold. // Otherwise returns 0 and the cost is invalid due to early bail-out.
WEBP_NODISCARD staticint HistogramAddEval(const VP8LHistogram* const a, const VP8LHistogram* const b,
VP8LHistogram* const out,
int64_t cost_threshold) {
uint64_t cost; const uint64_t sum_cost = a->bit_cost_ + b->bit_cost_;
SaturateAdd(sum_cost, &cost_threshold); if (!GetCombinedHistogramEntropy(a, b, cost_threshold, &cost)) return 0;
// 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. // Returns 1 if the cost is less than the threshold. // Otherwise returns 0 and the cost is invalid due to early bail-out.
WEBP_NODISCARD staticint HistogramAddThresh(const VP8LHistogram* const a, const VP8LHistogram* const b,
int64_t cost_threshold,
int64_t* cost_out) {
uint64_t cost;
assert(a != NULL && b != NULL);
SaturateAdd(a->bit_cost_, &cost_threshold); if (!GetCombinedHistogramEntropy(a, b, cost_threshold, &cost)) return 0;
// The structure to keep track of cost range for the three dominant entropy // symbols. typedefstruct {
uint64_t literal_max_;
uint64_t literal_min_;
uint64_t red_max_;
uint64_t red_min_;
uint64_t blue_max_;
uint64_t blue_min_;
} DominantCostRange;
// Construct the histograms from backward references. staticvoid HistogramBuild( int xsize, int histo_bits, const VP8LBackwardRefs* const backward_refs,
VP8LHistogramSet* const image_histo) { int x = 0, y = 0; constint histo_xsize = VP8LSubSampleSize(xsize, histo_bits);
VP8LHistogram** const histograms = image_histo->histograms;
VP8LRefsCursor c = VP8LRefsCursorInit(backward_refs);
assert(histo_bits > 0);
VP8LHistogramSetClear(image_histo); while (VP8LRefsCursorOk(&c)) { const PixOrCopy* const v = c.cur_pos; constint 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. staticconst uint32_t kInvalidHistogramSymbol = (uint32_t)(-1); staticvoid HistogramCopyAndAnalyze(VP8LHistogramSet* const orig_histo,
VP8LHistogramSet* const image_histo, int* const num_used,
uint32_t* const histogram_symbols) { int i, cluster_id; int num_used_orig = *num_used;
VP8LHistogram** const orig_histograms = orig_histo->histograms;
VP8LHistogram** const histograms = image_histo->histograms;
assert(image_histo->max_size == orig_histo->max_size); for (cluster_id = 0, i = 0; i < orig_histo->max_size; ++i) {
VP8LHistogram* const histo = orig_histograms[i];
UpdateHistogramCost(histo);
// Skip the histogram if it is completely empty, which can happen for tiles // with no information (when they are skipped because of LZ77). if (!histo->is_used_[0] && !histo->is_used_[1] && !histo->is_used_[2]
&& !histo->is_used_[3] && !histo->is_used_[4]) { // The first histogram is always used. If an histogram is empty, we set // its id to be the same as the previous one: this will improve // compressibility for later LZ77.
assert(i > 0);
HistogramSetRemoveHistogram(image_histo, i, num_used);
HistogramSetRemoveHistogram(orig_histo, i, &num_used_orig);
histogram_symbols[i] = kInvalidHistogramSymbol;
} else { // Copy histograms from orig_histo[] to image_histo[].
HistogramCopy(histo, histograms[i]);
histogram_symbols[i] = cluster_id++;
assert(cluster_id <= image_histo->max_size);
}
}
}
// Partition histograms to different entropy bins for three dominant (literal, // red and blue) symbol costs and compute the histogram aggregate bit_cost. staticvoid HistogramAnalyzeEntropyBin(VP8LHistogramSet* const image_histo,
uint16_t* const bin_map, int low_effort) { int i;
VP8LHistogram** const histograms = image_histo->histograms; constint 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) { if (histograms[i] == NULL) continue;
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] is not set to a special value as its use will later be guarded // by another (histograms[i] == NULL). if (histograms[i] == NULL) continue;
bin_map[i] = GetHistoBinIndex(histograms[i], &cost_range, low_effort);
}
}
// Merges some histograms with same bin_id together if it's advantageous. // Sets the remaining histograms to NULL. // 'combine_cost_factor' has to be divided by 100. staticvoid HistogramCombineEntropyBin(
VP8LHistogramSet* const image_histo, int* num_used, const uint32_t* const clusters, uint16_t* const cluster_mappings,
VP8LHistogram* cur_combo, const uint16_t* const bin_map, int num_bins,
int32_t combine_cost_factor, int low_effort) {
VP8LHistogram** const histograms = image_histo->histograms; int idx; 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];
// By default, a cluster matches itself. for (idx = 0; idx < *num_used; ++idx) cluster_mappings[idx] = idx; for (idx = 0; idx < image_histo->size; ++idx) { int bin_id, first; if (histograms[idx] == NULL) continue;
bin_id = bin_map[idx];
first = bin_info[bin_id].first; if (first == -1) {
bin_info[bin_id].first = idx;
} elseif (low_effort) {
HistogramAdd(histograms[idx], histograms[first], histograms[first]);
HistogramSetRemoveHistogram(image_histo, idx, num_used);
cluster_mappings[clusters[idx]] = clusters[first];
} else { // try to merge #idx into #first (both share the same bin_id) const uint64_t bit_cost = histograms[idx]->bit_cost_; const int64_t bit_cost_thresh =
-DivRound((int64_t)bit_cost * combine_cost_factor, 100); if (HistogramAddEval(histograms[first], histograms[idx], cur_combo,
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. constint 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)); constint 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]);
HistogramSetRemoveHistogram(image_histo, idx, num_used);
cluster_mappings[clusters[idx]] = clusters[first];
} else {
++bin_info[bin_id].num_combine_failures;
}
}
}
} if (low_effort) { // for low_effort case, update the final cost when everything is merged for (idx = 0; idx < image_histo->size; ++idx) { if (histograms[idx] == NULL) continue;
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;
}
// Pair of histograms. Negative idx1 value means that pair is out-of-date. typedefstruct { int idx1; int idx2;
int64_t cost_diff;
uint64_t cost_combo;
} HistogramPair;
typedefstruct {
HistogramPair* queue; int size; int max_size;
} HistoQueue;
staticint HistoQueueInit(HistoQueue* const histo_queue, constint max_size) {
histo_queue->size = 0;
histo_queue->max_size = max_size; // 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;
}
// Pop a specific pair in the queue by replacing it with the last one // and shrinking the queue. staticvoid 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. staticvoid 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;
}
}
// Update the cost diff and combo of a pair of histograms. This needs to be // called when the histograms have been merged with a third one. // Returns 1 if the cost diff is less than the threshold. // Otherwise returns 0 and the cost is invalid due to early bail-out.
WEBP_NODISCARD staticint HistoQueueUpdatePair(const VP8LHistogram* const h1, const VP8LHistogram* const h2,
int64_t cost_threshold,
HistogramPair* const pair) { const int64_t sum_cost = h1->bit_cost_ + h2->bit_cost_;
SaturateAdd(sum_cost, &cost_threshold); if (!GetCombinedHistogramEntropy(h1, h2, cost_threshold, &pair->cost_combo)) { return 0;
}
pair->cost_diff = (int64_t)pair->cost_combo - sum_cost; return 1;
}
// 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 int64_t HistoQueuePush(HistoQueue* const histo_queue,
VP8LHistogram** const histograms, int idx1, int idx2, int64_t threshold) { const VP8LHistogram* h1; const VP8LHistogram* h2;
HistogramPair pair;
// Stop here if the queue is full. if (histo_queue->size == histo_queue->max_size) return 0;
assert(threshold <= 0); if (idx1 > idx2) { constint tmp = idx2;
idx2 = idx1;
idx1 = tmp;
}
pair.idx1 = idx1;
pair.idx2 = idx2;
h1 = histograms[idx1];
h2 = histograms[idx2];
// Do not even consider the pair if it does not improve the entropy. if (!HistoQueueUpdatePair(h1, h2, threshold, &pair)) return 0;
// Combines histograms by continuously choosing the one with the highest cost // reduction. staticint HistogramCombineGreedy(VP8LHistogramSet* const image_histo, int* const num_used) { int ok = 0; constint image_histo_size = image_histo->size; int i, j;
VP8LHistogram** const histograms = image_histo->histograms; // Priority queue of histogram pairs.
HistoQueue histo_queue;
// image_histo_size^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: // - image_histo_size*(image_histo_size-1)/2 (the first two for loops) // - image_histo_size - 1 in the last for loop at the first iteration of // the while loop, image_histo_size - 2 at the second iteration ... // therefore image_histo_size*(image_histo_size-1)/2 overall too if (!HistoQueueInit(&histo_queue, image_histo_size * image_histo_size)) { goto End;
}
for (i = 0; i < image_histo_size; ++i) { if (image_histo->histograms[i] == NULL) continue; for (j = i + 1; j < image_histo_size; ++j) { // Initialize queue. if (image_histo->histograms[j] == NULL) continue;
HistoQueuePush(&histo_queue, histograms, i, j, 0);
}
}
// 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 (i == idx1 || image_histo->histograms[i] == NULL) continue;
HistoQueuePush(&histo_queue, image_histo->histograms, idx1, i, 0);
}
}
ok = 1;
End:
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. staticint PairComparison(constvoid* idx1, constvoid* idx2) { // To be used with bsearch: <0 when *idx1<*idx2, >0 if >, 0 when ==. return (*(int*) idx1 - *(int*) idx2);
} staticint HistogramCombineStochastic(VP8LHistogramSet* const image_histo, int* const num_used, int min_cluster_size, int* const do_greedy) { int j, iter;
uint32_t seed = 1; int tries_with_no_success = 0; constint outer_iters = *num_used; constint num_tries_no_success = outer_iters / 2;
VP8LHistogram** const histograms = image_histo->histograms; // Priority queue of histogram pairs. Its size of 'kHistoQueueSize' // impacts the quality of the compression and the speed: the smaller the // faster but the worse for the compression.
HistoQueue histo_queue; constint kHistoQueueSize = 9; int ok = 0; // mapping from an index in image_histo with no NULL histogram to the full // blown image_histo. int* mappings;
mappings = (int*) WebPSafeMalloc(*num_used, sizeof(*mappings)); if (mappings == NULL) return 0; if (!HistoQueueInit(&histo_queue, kHistoQueueSize)) goto End; // Fill the initial mapping. for (j = 0, iter = 0; iter < image_histo->size; ++iter) { if (histograms[iter] == NULL) continue;
mappings[j++] = iter;
}
assert(j == *num_used);
// Collapse similar histograms in 'image_histo'. for (iter = 0;
iter < outer_iters && *num_used >= min_cluster_size &&
++tries_with_no_success < num_tries_no_success;
++iter) { int* mapping_index;
int64_t best_cost =
(histo_queue.size == 0) ? 0 : histo_queue.queue[0].cost_diff; int best_idx1 = -1, best_idx2 = 1; const uint32_t rand_range = (*num_used - 1) * (*num_used); // (*num_used) / 2 was chosen empirically. Less means faster but worse // compression. constint num_tries = (*num_used) / 2;
// Pick random samples. for (j = 0; *num_used >= 2 && j < num_tries; ++j) {
int64_t curr_cost; // Choose two different histograms at random and try to combine them. const uint32_t tmp = MyRand(&seed) % rand_range;
uint32_t idx1 = tmp / (*num_used - 1);
uint32_t idx2 = tmp % (*num_used - 1); if (idx2 >= idx1) ++idx2;
idx1 = mappings[idx1];
idx2 = mappings[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;
// Get the best histograms.
best_idx1 = histo_queue.queue[0].idx1;
best_idx2 = histo_queue.queue[0].idx2;
assert(best_idx1 < best_idx2); // Pop best_idx2 from mappings.
mapping_index = (int*) bsearch(&best_idx2, mappings, *num_used, sizeof(best_idx2), &PairComparison);
assert(mapping_index != NULL);
memmove(mapping_index, mapping_index + 1, sizeof(*mapping_index) *
((*num_used) - (mapping_index - mappings) - 1)); // Merge the histograms and remove best_idx2 from the queue.
HistogramAdd(histograms[best_idx2], histograms[best_idx1],
histograms[best_idx1]);
histograms[best_idx1]->bit_cost_ = histo_queue.queue[0].cost_combo;
HistogramSetRemoveHistogram(image_histo, best_idx2, num_used); // 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; constint is_idx1_best = p->idx1 == best_idx1 || p->idx1 == best_idx2; constint 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;
} elseif (is_idx2_best) {
p->idx2 = best_idx1;
do_eval = 1;
} // Make sure the index order is respected. if (p->idx1 > p->idx2) { constint tmp = p->idx2;
p->idx2 = p->idx1;
p->idx1 = tmp;
} if (do_eval) { // Re-evaluate the cost of an updated pair. if (!HistoQueueUpdatePair(histograms[p->idx1], histograms[p->idx2], 0,
p)) {
HistoQueuePopPair(&histo_queue, p); continue;
}
}
HistoQueueUpdateHead(&histo_queue, p);
++j;
}
tries_with_no_success = 0;
}
*do_greedy = (*num_used <= min_cluster_size);
ok = 1;
// Find the best 'out' histogram for each of the 'in' histograms. // At call-time, 'out' contains the histograms of the clusters. // Note: we assume that out[]->bit_cost_ is already up-to-date. staticvoid HistogramRemap(const VP8LHistogramSet* const in,
VP8LHistogramSet* const out,
uint32_t* const symbols) { int i;
VP8LHistogram** const in_histo = in->histograms;
VP8LHistogram** const out_histo = out->histograms; constint in_size = out->max_size; constint out_size = out->size; if (out_size > 1) { for (i = 0; i < in_size; ++i) { int best_out = 0;
int64_t best_bits = WEBP_INT64_MAX; int k; if (in_histo[i] == NULL) { // Arbitrarily set to the previous value if unused to help future LZ77.
symbols[i] = symbols[i - 1]; continue;
} for (k = 0; k < out_size; ++k) {
int64_t cur_bits; if (HistogramAddThresh(out_histo[k], in_histo[i], best_bits,
&cur_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.
VP8LHistogramSetClear(out);
out->size = out_size;
for (i = 0; i < in_size; ++i) { int idx; if (in_histo[i] == NULL) continue;
idx = symbols[i];
HistogramAdd(in_histo[i], out_histo[idx], out_histo[idx]);
}
}
static int32_t GetCombineCostFactor(int histo_size, int quality) {
int32_t combine_cost_factor = 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;
}
// Given a HistogramSet 'set', the mapping of clusters 'cluster_mapping' and the // current assignment of the cells in 'symbols', merge the clusters and // assign the smallest possible clusters values. staticvoid OptimizeHistogramSymbols(const VP8LHistogramSet* const set,
uint16_t* const cluster_mappings,
uint32_t num_clusters,
uint16_t* const cluster_mappings_tmp,
uint32_t* const symbols) {
uint32_t i, cluster_max; int do_continue = 1; // First, assign the lowest cluster to each pixel. while (do_continue) {
do_continue = 0; for (i = 0; i < num_clusters; ++i) { int k;
k = cluster_mappings[i]; while (k != cluster_mappings[k]) {
cluster_mappings[k] = cluster_mappings[cluster_mappings[k]];
k = cluster_mappings[k];
} if (k != cluster_mappings[i]) {
do_continue = 1;
cluster_mappings[i] = k;
}
}
} // Create a mapping from a cluster id to its minimal version.
cluster_max = 0;
memset(cluster_mappings_tmp, 0,
set->max_size * sizeof(*cluster_mappings_tmp));
assert(cluster_mappings[0] == 0); // Re-map the ids. for (i = 0; i < (uint32_t)set->max_size; ++i) { int cluster; if (symbols[i] == kInvalidHistogramSymbol) continue;
cluster = cluster_mappings[symbols[i]];
assert(symbols[i] < num_clusters); if (cluster > 0 && cluster_mappings_tmp[cluster] == 0) {
++cluster_max;
cluster_mappings_tmp[cluster] = cluster_max;
}
symbols[i] = cluster_mappings_tmp[cluster];
}
// Make sure all cluster values are used.
cluster_max = 0; for (i = 0; i < (uint32_t)set->max_size; ++i) { if (symbols[i] == kInvalidHistogramSymbol) continue; if (symbols[i] <= cluster_max) continue;
++cluster_max;
assert(symbols[i] == cluster_max);
}
}
staticvoid RemoveEmptyHistograms(VP8LHistogramSet* const image_histo) {
uint32_t size; int i; for (i = 0, size = 0; i < image_histo->size; ++i) { if (image_histo->histograms[i] == NULL) continue;
image_histo->histograms[size++] = image_histo->histograms[i];
}
image_histo->size = size;
}
int VP8LGetHistoImageSymbols(int xsize, int ysize, const VP8LBackwardRefs* const refs, int quality, int low_effort, int histogram_bits, int cache_bits,
VP8LHistogramSet* const image_histo,
VP8LHistogram* const tmp_histo,
uint32_t* const histogram_symbols, const WebPPicture* const pic, int percent_range, int* const percent) { constint histo_xsize =
histogram_bits ? VP8LSubSampleSize(xsize, histogram_bits) : 1; constint histo_ysize =
histogram_bits ? VP8LSubSampleSize(ysize, histogram_bits) : 1; constint 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). constint entropy_combine_num_bins = low_effort ? NUM_PARTITIONS : BIN_SIZE; int entropy_combine;
uint16_t* const map_tmp =
(uint16_t*)WebPSafeMalloc(2 * image_histo_raw_size, sizeof(*map_tmp));
uint16_t* const cluster_mappings = map_tmp + image_histo_raw_size; int num_used = image_histo_raw_size; if (orig_histo == NULL || map_tmp == NULL) {
WebPEncodingSetError(pic, VP8_ENC_ERROR_OUT_OF_MEMORY); goto Error;
}
// Construct the histograms from backward references.
HistogramBuild(xsize, histogram_bits, refs, orig_histo); // Copies the histograms and computes its bit_cost. // histogram_symbols is optimized
HistogramCopyAndAnalyze(orig_histo, image_histo, &num_used,
histogram_symbols);
// Don't combine the histograms using stochastic and greedy heuristics for // low-effort compression mode. if (!low_effort || !entropy_combine) { // cubic ramp between 1 and MAX_HISTO_GREEDY: constint threshold_size =
(int)(1 + DivRound(quality * quality * quality * (MAX_HISTO_GREEDY - 1),
100 * 100 * 100)); int do_greedy; if (!HistogramCombineStochastic(image_histo, &num_used, threshold_size,
&do_greedy)) {
WebPEncodingSetError(pic, VP8_ENC_ERROR_OUT_OF_MEMORY); goto Error;
} if (do_greedy) {
RemoveEmptyHistograms(image_histo); if (!HistogramCombineGreedy(image_histo, &num_used)) {
WebPEncodingSetError(pic, VP8_ENC_ERROR_OUT_OF_MEMORY); goto Error;
}
}
}
// Find the optimal map from original histograms to the final ones.
RemoveEmptyHistograms(image_histo);
HistogramRemap(orig_histo, image_histo, histogram_symbols);
if (!WebPReportProgress(pic, *percent + percent_range, percent)) { goto Error;
}
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