318 lines
11 KiB
C
318 lines
11 KiB
C
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/* NOLINT(build/header_guard) */
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/* Copyright 2013 Google Inc. All Rights Reserved.
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Distributed under MIT license.
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See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
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*/
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/* template parameters: FN, CODE */
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#define HistogramType FN(Histogram)
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/* Computes the bit cost reduction by combining out[idx1] and out[idx2] and if
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it is below a threshold, stores the pair (idx1, idx2) in the *pairs queue. */
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BROTLI_INTERNAL void FN(BrotliCompareAndPushToQueue)(
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const HistogramType* out, const uint32_t* cluster_size, uint32_t idx1,
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uint32_t idx2, size_t max_num_pairs, HistogramPair* pairs,
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size_t* num_pairs) CODE({
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BROTLI_BOOL is_good_pair = BROTLI_FALSE;
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HistogramPair p;
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p.idx1 = p.idx2 = 0;
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p.cost_diff = p.cost_combo = 0;
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if (idx1 == idx2) {
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return;
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}
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if (idx2 < idx1) {
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uint32_t t = idx2;
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idx2 = idx1;
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idx1 = t;
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}
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p.idx1 = idx1;
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p.idx2 = idx2;
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p.cost_diff = 0.5 * ClusterCostDiff(cluster_size[idx1], cluster_size[idx2]);
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p.cost_diff -= out[idx1].bit_cost_;
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p.cost_diff -= out[idx2].bit_cost_;
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if (out[idx1].total_count_ == 0) {
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p.cost_combo = out[idx2].bit_cost_;
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is_good_pair = BROTLI_TRUE;
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} else if (out[idx2].total_count_ == 0) {
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p.cost_combo = out[idx1].bit_cost_;
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is_good_pair = BROTLI_TRUE;
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} else {
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double threshold = *num_pairs == 0 ? 1e99 :
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BROTLI_MAX(double, 0.0, pairs[0].cost_diff);
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HistogramType combo = out[idx1];
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double cost_combo;
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FN(HistogramAddHistogram)(&combo, &out[idx2]);
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cost_combo = FN(BrotliPopulationCost)(&combo);
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if (cost_combo < threshold - p.cost_diff) {
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p.cost_combo = cost_combo;
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is_good_pair = BROTLI_TRUE;
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}
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}
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if (is_good_pair) {
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p.cost_diff += p.cost_combo;
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if (*num_pairs > 0 && HistogramPairIsLess(&pairs[0], &p)) {
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/* Replace the top of the queue if needed. */
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if (*num_pairs < max_num_pairs) {
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pairs[*num_pairs] = pairs[0];
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++(*num_pairs);
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}
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pairs[0] = p;
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} else if (*num_pairs < max_num_pairs) {
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pairs[*num_pairs] = p;
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++(*num_pairs);
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}
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}
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})
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BROTLI_INTERNAL size_t FN(BrotliHistogramCombine)(HistogramType* out,
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uint32_t* cluster_size,
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uint32_t* symbols,
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uint32_t* clusters,
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HistogramPair* pairs,
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size_t num_clusters,
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size_t symbols_size,
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size_t max_clusters,
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size_t max_num_pairs) CODE({
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double cost_diff_threshold = 0.0;
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size_t min_cluster_size = 1;
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size_t num_pairs = 0;
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{
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/* We maintain a vector of histogram pairs, with the property that the pair
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with the maximum bit cost reduction is the first. */
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size_t idx1;
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for (idx1 = 0; idx1 < num_clusters; ++idx1) {
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size_t idx2;
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for (idx2 = idx1 + 1; idx2 < num_clusters; ++idx2) {
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FN(BrotliCompareAndPushToQueue)(out, cluster_size, clusters[idx1],
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clusters[idx2], max_num_pairs, &pairs[0], &num_pairs);
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}
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}
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}
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while (num_clusters > min_cluster_size) {
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uint32_t best_idx1;
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uint32_t best_idx2;
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size_t i;
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if (pairs[0].cost_diff >= cost_diff_threshold) {
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cost_diff_threshold = 1e99;
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min_cluster_size = max_clusters;
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continue;
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}
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/* Take the best pair from the top of heap. */
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best_idx1 = pairs[0].idx1;
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best_idx2 = pairs[0].idx2;
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FN(HistogramAddHistogram)(&out[best_idx1], &out[best_idx2]);
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out[best_idx1].bit_cost_ = pairs[0].cost_combo;
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cluster_size[best_idx1] += cluster_size[best_idx2];
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for (i = 0; i < symbols_size; ++i) {
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if (symbols[i] == best_idx2) {
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symbols[i] = best_idx1;
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}
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}
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for (i = 0; i < num_clusters; ++i) {
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if (clusters[i] == best_idx2) {
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memmove(&clusters[i], &clusters[i + 1],
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(num_clusters - i - 1) * sizeof(clusters[0]));
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break;
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}
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}
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--num_clusters;
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{
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/* Remove pairs intersecting the just combined best pair. */
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size_t copy_to_idx = 0;
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for (i = 0; i < num_pairs; ++i) {
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HistogramPair* p = &pairs[i];
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if (p->idx1 == best_idx1 || p->idx2 == best_idx1 ||
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p->idx1 == best_idx2 || p->idx2 == best_idx2) {
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/* Remove invalid pair from the queue. */
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continue;
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}
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if (HistogramPairIsLess(&pairs[0], p)) {
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/* Replace the top of the queue if needed. */
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HistogramPair front = pairs[0];
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pairs[0] = *p;
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pairs[copy_to_idx] = front;
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} else {
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pairs[copy_to_idx] = *p;
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}
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++copy_to_idx;
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}
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num_pairs = copy_to_idx;
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}
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/* Push new pairs formed with the combined histogram to the heap. */
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for (i = 0; i < num_clusters; ++i) {
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FN(BrotliCompareAndPushToQueue)(out, cluster_size, best_idx1, clusters[i],
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max_num_pairs, &pairs[0], &num_pairs);
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}
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}
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return num_clusters;
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})
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/* What is the bit cost of moving histogram from cur_symbol to candidate. */
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BROTLI_INTERNAL double FN(BrotliHistogramBitCostDistance)(
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const HistogramType* histogram, const HistogramType* candidate) CODE({
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if (histogram->total_count_ == 0) {
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return 0.0;
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} else {
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HistogramType tmp = *histogram;
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FN(HistogramAddHistogram)(&tmp, candidate);
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return FN(BrotliPopulationCost)(&tmp) - candidate->bit_cost_;
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}
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})
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/* Find the best 'out' histogram for each of the 'in' histograms.
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When called, clusters[0..num_clusters) contains the unique values from
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symbols[0..in_size), but this property is not preserved in this function.
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Note: we assume that out[]->bit_cost_ is already up-to-date. */
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BROTLI_INTERNAL void FN(BrotliHistogramRemap)(const HistogramType* in,
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size_t in_size, const uint32_t* clusters, size_t num_clusters,
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HistogramType* out, uint32_t* symbols) CODE({
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size_t i;
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for (i = 0; i < in_size; ++i) {
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uint32_t best_out = i == 0 ? symbols[0] : symbols[i - 1];
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double best_bits =
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FN(BrotliHistogramBitCostDistance)(&in[i], &out[best_out]);
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size_t j;
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for (j = 0; j < num_clusters; ++j) {
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const double cur_bits =
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FN(BrotliHistogramBitCostDistance)(&in[i], &out[clusters[j]]);
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if (cur_bits < best_bits) {
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best_bits = cur_bits;
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best_out = clusters[j];
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}
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}
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symbols[i] = best_out;
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}
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/* Recompute each out based on raw and symbols. */
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for (i = 0; i < num_clusters; ++i) {
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FN(HistogramClear)(&out[clusters[i]]);
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}
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for (i = 0; i < in_size; ++i) {
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FN(HistogramAddHistogram)(&out[symbols[i]], &in[i]);
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}
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})
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/* Reorders elements of the out[0..length) array and changes values in
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symbols[0..length) array in the following way:
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* when called, symbols[] contains indexes into out[], and has N unique
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values (possibly N < length)
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* on return, symbols'[i] = f(symbols[i]) and
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out'[symbols'[i]] = out[symbols[i]], for each 0 <= i < length,
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where f is a bijection between the range of symbols[] and [0..N), and
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the first occurrences of values in symbols'[i] come in consecutive
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increasing order.
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Returns N, the number of unique values in symbols[]. */
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BROTLI_INTERNAL size_t FN(BrotliHistogramReindex)(MemoryManager* m,
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HistogramType* out, uint32_t* symbols, size_t length) CODE({
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static const uint32_t kInvalidIndex = BROTLI_UINT32_MAX;
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uint32_t* new_index = BROTLI_ALLOC(m, uint32_t, length);
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uint32_t next_index;
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HistogramType* tmp;
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size_t i;
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if (BROTLI_IS_OOM(m)) return 0;
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for (i = 0; i < length; ++i) {
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new_index[i] = kInvalidIndex;
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}
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next_index = 0;
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for (i = 0; i < length; ++i) {
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if (new_index[symbols[i]] == kInvalidIndex) {
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new_index[symbols[i]] = next_index;
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++next_index;
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}
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}
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/* TODO: by using idea of "cycle-sort" we can avoid allocation of
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tmp and reduce the number of copying by the factor of 2. */
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tmp = BROTLI_ALLOC(m, HistogramType, next_index);
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if (BROTLI_IS_OOM(m)) return 0;
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next_index = 0;
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for (i = 0; i < length; ++i) {
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if (new_index[symbols[i]] == next_index) {
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tmp[next_index] = out[symbols[i]];
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++next_index;
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}
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symbols[i] = new_index[symbols[i]];
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}
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BROTLI_FREE(m, new_index);
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for (i = 0; i < next_index; ++i) {
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out[i] = tmp[i];
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}
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BROTLI_FREE(m, tmp);
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return next_index;
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})
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BROTLI_INTERNAL void FN(BrotliClusterHistograms)(
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MemoryManager* m, const HistogramType* in, const size_t in_size,
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size_t max_histograms, HistogramType* out, size_t* out_size,
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uint32_t* histogram_symbols) CODE({
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uint32_t* cluster_size = BROTLI_ALLOC(m, uint32_t, in_size);
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uint32_t* clusters = BROTLI_ALLOC(m, uint32_t, in_size);
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size_t num_clusters = 0;
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const size_t max_input_histograms = 64;
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size_t pairs_capacity = max_input_histograms * max_input_histograms / 2;
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/* For the first pass of clustering, we allow all pairs. */
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HistogramPair* pairs = BROTLI_ALLOC(m, HistogramPair, pairs_capacity + 1);
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size_t i;
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if (BROTLI_IS_OOM(m)) return;
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for (i = 0; i < in_size; ++i) {
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cluster_size[i] = 1;
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}
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for (i = 0; i < in_size; ++i) {
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out[i] = in[i];
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out[i].bit_cost_ = FN(BrotliPopulationCost)(&in[i]);
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histogram_symbols[i] = (uint32_t)i;
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}
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for (i = 0; i < in_size; i += max_input_histograms) {
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size_t num_to_combine =
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BROTLI_MIN(size_t, in_size - i, max_input_histograms);
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size_t num_new_clusters;
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size_t j;
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for (j = 0; j < num_to_combine; ++j) {
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clusters[num_clusters + j] = (uint32_t)(i + j);
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}
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num_new_clusters =
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FN(BrotliHistogramCombine)(out, cluster_size,
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&histogram_symbols[i],
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&clusters[num_clusters], pairs,
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num_to_combine, num_to_combine,
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max_histograms, pairs_capacity);
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num_clusters += num_new_clusters;
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}
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{
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/* For the second pass, we limit the total number of histogram pairs.
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After this limit is reached, we only keep searching for the best pair. */
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size_t max_num_pairs = BROTLI_MIN(size_t,
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64 * num_clusters, (num_clusters / 2) * num_clusters);
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BROTLI_ENSURE_CAPACITY(
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m, HistogramPair, pairs, pairs_capacity, max_num_pairs + 1);
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if (BROTLI_IS_OOM(m)) return;
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/* Collapse similar histograms. */
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num_clusters = FN(BrotliHistogramCombine)(out, cluster_size,
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histogram_symbols, clusters,
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pairs, num_clusters, in_size,
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max_histograms, max_num_pairs);
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}
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BROTLI_FREE(m, pairs);
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BROTLI_FREE(m, cluster_size);
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/* Find the optimal map from original histograms to the final ones. */
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FN(BrotliHistogramRemap)(in, in_size, clusters, num_clusters,
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out, histogram_symbols);
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BROTLI_FREE(m, clusters);
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/* Convert the context map to a canonical form. */
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*out_size = FN(BrotliHistogramReindex)(m, out, histogram_symbols, in_size);
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if (BROTLI_IS_OOM(m)) return;
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})
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#undef HistogramType
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