/* Copyright 2013 Google Inc. All Rights Reserved.
Distributed under MIT license.
See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
*/
// Functions for clustering similar histograms together.
#ifndef BROTLI_ENC_CLUSTER_H_
#define BROTLI_ENC_CLUSTER_H_
#include <math.h>
#include <algorithm>
#include <map>
#include <utility>
#include <vector>
#include "./bit_cost.h"
#include "./entropy_encode.h"
#include "./fast_log.h"
#include "./histogram.h"
#include "./port.h"
#include "./types.h"
namespace brotli {
struct HistogramPair {
uint32_t idx1;
uint32_t idx2;
double cost_combo;
double cost_diff;
};
inline bool operator<(const HistogramPair& p1, const HistogramPair& p2) {
if (p1.cost_diff != p2.cost_diff) {
return p1.cost_diff > p2.cost_diff;
}
return (p1.idx2 - p1.idx1) > (p2.idx2 - p2.idx1);
}
// Returns entropy reduction of the context map when we combine two clusters.
inline double ClusterCostDiff(size_t size_a, size_t size_b) {
size_t size_c = size_a + size_b;
return static_cast<double>(size_a) * FastLog2(size_a) +
static_cast<double>(size_b) * FastLog2(size_b) -
static_cast<double>(size_c) * FastLog2(size_c);
}
// Computes the bit cost reduction by combining out[idx1] and out[idx2] and if
// it is below a threshold, stores the pair (idx1, idx2) in the *pairs queue.
template<typename HistogramType>
void CompareAndPushToQueue(const HistogramType* out,
const uint32_t* cluster_size,
uint32_t idx1, uint32_t idx2,
std::vector<HistogramPair>* pairs) {
if (idx1 == idx2) {
return;
}
if (idx2 < idx1) {
uint32_t t = idx2;
idx2 = idx1;
idx1 = t;
}
bool store_pair = false;
HistogramPair p;
p.idx1 = idx1;
p.idx2 = idx2;
p.cost_diff = 0.5 * ClusterCostDiff(cluster_size[idx1], cluster_size[idx2]);
p.cost_diff -= out[idx1].bit_cost_;
p.cost_diff -= out[idx2].bit_cost_;
if (out[idx1].total_count_ == 0) {
p.cost_combo = out[idx2].bit_cost_;
store_pair = true;
} else if (out[idx2].total_count_ == 0) {
p.cost_combo = out[idx1].bit_cost_;
store_pair = true;
} else {
double threshold = pairs->empty() ? 1e99 :
std::max(0.0, (*pairs)[0].cost_diff);
HistogramType combo = out[idx1];
combo.AddHistogram(out[idx2]);
double cost_combo = PopulationCost(combo);
if (cost_combo < threshold - p.cost_diff) {
p.cost_combo = cost_combo;
store_pair = true;
}
}
if (store_pair) {
p.cost_diff += p.cost_combo;
if (!pairs->empty() && (pairs->front() < p)) {
// Replace the top of the queue if needed.
pairs->push_back(pairs->front());
pairs->front() = p;
} else {
pairs->push_back(p);
}
}
}
template<typename HistogramType>
void HistogramCombine(HistogramType* out,
uint32_t* cluster_size,
uint32_t* symbols,
size_t symbols_size,
size_t max_clusters) {
double cost_diff_threshold = 0.0;
size_t min_cluster_size = 1;
// Uniquify the list of symbols.
std::vector<uint32_t> clusters(symbols, symbols + symbols_size);
std::sort(clusters.begin(), clusters.end());
std::vector<uint32_t>::iterator last =
std::unique(clusters.begin(), clusters.end());
clusters.resize(static_cast<size_t>(last - clusters.begin()));
// We maintain a heap of histogram pairs, ordered by the bit cost reduction.
std::vector<HistogramPair> pairs;
for (size_t idx1 = 0; idx1 < clusters.size(); ++idx1) {
for (size_t idx2 = idx1 + 1; idx2 < clusters.size(); ++idx2) {
CompareAndPushToQueue(out, cluster_size, clusters[idx1], clusters[idx2],
&pairs);
}
}
while (clusters.size() > min_cluster_size) {
if (pairs[0].cost_diff >= cost_diff_threshold) {
cost_diff_threshold = 1e99;
min_cluster_size = max_clusters;
continue;
}
// Take the best pair from the top of heap.
uint32_t best_idx1 = pairs[0].idx1;
uint32_t best_idx2 = pairs[0].idx2;
out[best_idx1].AddHistogram(out[best_idx2]);
out[best_idx1].bit_cost_ = pairs[0].cost_combo;
cluster_size[best_idx1] += cluster_size[best_idx2];
for (size_t i = 0; i < symbols_size; ++i) {
if (symbols[i] == best_idx2) {
symbols[i] = best_idx1;
}
}
for (std::vector<uint32_t>::iterator cluster = clusters.begin();
cluster != clusters.end(); ++cluster) {
if (*cluster >= best_idx2) {
clusters.erase(cluster);
break;
}
}
// Remove pairs intersecting the just combined best pair.
size_t copy_to_idx = 0;
for (size_t i = 0; i < pairs.size(); ++i) {
HistogramPair& p = pairs[i];
if (p.idx1 == best_idx1 || p.idx2 == best_idx1 ||
p.idx1 == best_idx2 || p.idx2 == best_idx2) {
// Remove invalid pair from the queue.
continue;
}
if (pairs.front() < p) {
// Replace the top of the queue if needed.
HistogramPair front = pairs.front();
pairs.front() = p;
pairs[copy_to_idx] = front;
} else {
pairs[copy_to_idx] = p;
}
++copy_to_idx;
}
pairs.resize(copy_to_idx);
// Push new pairs formed with the combined histogram to the heap.
for (size_t i = 0; i < clusters.size(); ++i) {
CompareAndPushToQueue(out, cluster_size, best_idx1, clusters[i], &pairs);
}
}
}
// -----------------------------------------------------------------------------
// Histogram refinement
// What is the bit cost of moving histogram from cur_symbol to candidate.
template<typename HistogramType>
double HistogramBitCostDistance(const HistogramType& histogram,
const HistogramType& candidate) {
if (histogram.total_count_ == 0) {
return 0.0;
}
HistogramType tmp = histogram;
tmp.AddHistogram(candidate);
return PopulationCost(tmp) - candidate.bit_cost_;
}
// Find the best 'out' histogram for each of the 'in' histograms.
// Note: we assume that out[]->bit_cost_ is already up-to-date.
template<typename HistogramType>
void HistogramRemap(const HistogramType* in, size_t in_size,
HistogramType* out, uint32_t* symbols) {
// Uniquify the list of symbols.
std::vector<uint32_t> all_symbols(symbols, symbols + in_size);
std::sort(all_symbols.begin(), all_symbols.end());
std::vector<uint32_t>::iterator last =
std::unique(all_symbols.begin(), all_symbols.end());
all_symbols.resize(static_cast<size_t>(last - all_symbols.begin()));
for (size_t i = 0; i < in_size; ++i) {
uint32_t best_out = i == 0 ? symbols[0] : symbols[i - 1];
double best_bits = HistogramBitCostDistance(in[i], out[best_out]);
for (std::vector<uint32_t>::const_iterator k = all_symbols.begin();
k != all_symbols.end(); ++k) {
const double cur_bits = HistogramBitCostDistance(in[i], out[*k]);
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 (std::vector<uint32_t>::const_iterator k = all_symbols.begin();
k != all_symbols.end(); ++k) {
out[*k].Clear();
}
for (size_t i = 0; i < in_size; ++i) {
out[symbols[i]].AddHistogram(in[i]);
}
}
// Reorder histograms in *out so that the new symbols in *symbols come in
// increasing order.
template<typename HistogramType>
void HistogramReindex(std::vector<HistogramType>* out,
std::vector<uint32_t>* symbols) {
std::vector<HistogramType> tmp(*out);
std::map<uint32_t, uint32_t> new_index;
uint32_t next_index = 0;
for (size_t i = 0; i < symbols->size(); ++i) {
if (new_index.find((*symbols)[i]) == new_index.end()) {
new_index[(*symbols)[i]] = next_index;
(*out)[next_index] = tmp[(*symbols)[i]];
++next_index;
}
}
out->resize(next_index);
for (size_t i = 0; i < symbols->size(); ++i) {
(*symbols)[i] = new_index[(*symbols)[i]];
}
}
// Clusters similar histograms in 'in' together, the selected histograms are
// placed in 'out', and for each index in 'in', *histogram_symbols will
// indicate which of the 'out' histograms is the best approximation.
template<typename HistogramType>
void ClusterHistograms(const std::vector<HistogramType>& in,
size_t num_contexts, size_t num_blocks,
size_t max_histograms,
std::vector<HistogramType>* out,
std::vector<uint32_t>* histogram_symbols) {
const size_t in_size = num_contexts * num_blocks;
assert(in_size == in.size());
std::vector<uint32_t> cluster_size(in_size, 1);
out->resize(in_size);
histogram_symbols->resize(in_size);
for (size_t i = 0; i < in_size; ++i) {
(*out)[i] = in[i];
(*out)[i].bit_cost_ = PopulationCost(in[i]);
(*histogram_symbols)[i] = static_cast<uint32_t>(i);
}
const size_t max_input_histograms = 64;
for (size_t i = 0; i < in_size; i += max_input_histograms) {
size_t num_to_combine = std::min(in_size - i, max_input_histograms);
HistogramCombine(&(*out)[0], &cluster_size[0],
&(*histogram_symbols)[i], num_to_combine,
max_histograms);
}
// Collapse similar histograms.
HistogramCombine(&(*out)[0], &cluster_size[0],
&(*histogram_symbols)[0], in_size,
max_histograms);
// Find the optimal map from original histograms to the final ones.
HistogramRemap(&in[0], in_size, &(*out)[0], &(*histogram_symbols)[0]);
// Convert the context map to a canonical form.
HistogramReindex(out, histogram_symbols);
}
} // namespace brotli
#endif // BROTLI_ENC_CLUSTER_H_
