1 /* NOLINT(build/header_guard) */ 2 /* Copyright 2013 Google Inc. All Rights Reserved. 3 4 Distributed under MIT license. 5 See file LICENSE for detail or copy at https://opensource.org/licenses/MIT 6 */ 7 8 /* template parameters: FN, CODE */ 9 10 #define HistogramType FN(Histogram) 11 12 /* Computes the bit cost reduction by combining out[idx1] and out[idx2] and if 13 it is below a threshold, stores the pair (idx1, idx2) in the *pairs queue. */ 14 BROTLI_INTERNAL void FN(BrotliCompareAndPushToQueue)( 15 const HistogramType* out, const uint32_t* cluster_size, uint32_t idx1, 16 uint32_t idx2, size_t max_num_pairs, HistogramPair* pairs, 17 size_t* num_pairs) CODE({ 18 BROTLI_BOOL is_good_pair = BROTLI_FALSE; 19 HistogramPair p; 20 p.idx1 = p.idx2 = 0; 21 p.cost_diff = p.cost_combo = 0; 22 if (idx1 == idx2) { 23 return; 24 } 25 if (idx2 < idx1) { 26 uint32_t t = idx2; 27 idx2 = idx1; 28 idx1 = t; 29 } 30 p.idx1 = idx1; 31 p.idx2 = idx2; 32 p.cost_diff = 0.5 * ClusterCostDiff(cluster_size[idx1], cluster_size[idx2]); 33 p.cost_diff -= out[idx1].bit_cost_; 34 p.cost_diff -= out[idx2].bit_cost_; 35 36 if (out[idx1].total_count_ == 0) { 37 p.cost_combo = out[idx2].bit_cost_; 38 is_good_pair = BROTLI_TRUE; 39 } else if (out[idx2].total_count_ == 0) { 40 p.cost_combo = out[idx1].bit_cost_; 41 is_good_pair = BROTLI_TRUE; 42 } else { 43 double threshold = *num_pairs == 0 ? 1e99 : 44 BROTLI_MAX(double, 0.0, pairs[0].cost_diff); 45 HistogramType combo = out[idx1]; 46 double cost_combo; 47 FN(HistogramAddHistogram)(&combo, &out[idx2]); 48 cost_combo = FN(BrotliPopulationCost)(&combo); 49 if (cost_combo < threshold - p.cost_diff) { 50 p.cost_combo = cost_combo; 51 is_good_pair = BROTLI_TRUE; 52 } 53 } 54 if (is_good_pair) { 55 p.cost_diff += p.cost_combo; 56 if (*num_pairs > 0 && HistogramPairIsLess(&pairs[0], &p)) { 57 /* Replace the top of the queue if needed. */ 58 if (*num_pairs < max_num_pairs) { 59 pairs[*num_pairs] = pairs[0]; 60 ++(*num_pairs); 61 } 62 pairs[0] = p; 63 } else if (*num_pairs < max_num_pairs) { 64 pairs[*num_pairs] = p; 65 ++(*num_pairs); 66 } 67 } 68 }) 69 70 BROTLI_INTERNAL size_t FN(BrotliHistogramCombine)(HistogramType* out, 71 uint32_t* cluster_size, 72 uint32_t* symbols, 73 uint32_t* clusters, 74 HistogramPair* pairs, 75 size_t num_clusters, 76 size_t symbols_size, 77 size_t max_clusters, 78 size_t max_num_pairs) CODE({ 79 double cost_diff_threshold = 0.0; 80 size_t min_cluster_size = 1; 81 size_t num_pairs = 0; 82 83 { 84 /* We maintain a vector of histogram pairs, with the property that the pair 85 with the maximum bit cost reduction is the first. */ 86 size_t idx1; 87 for (idx1 = 0; idx1 < num_clusters; ++idx1) { 88 size_t idx2; 89 for (idx2 = idx1 + 1; idx2 < num_clusters; ++idx2) { 90 FN(BrotliCompareAndPushToQueue)(out, cluster_size, clusters[idx1], 91 clusters[idx2], max_num_pairs, &pairs[0], &num_pairs); 92 } 93 } 94 } 95 96 while (num_clusters > min_cluster_size) { 97 uint32_t best_idx1; 98 uint32_t best_idx2; 99 size_t i; 100 if (pairs[0].cost_diff >= cost_diff_threshold) { 101 cost_diff_threshold = 1e99; 102 min_cluster_size = max_clusters; 103 continue; 104 } 105 /* Take the best pair from the top of heap. */ 106 best_idx1 = pairs[0].idx1; 107 best_idx2 = pairs[0].idx2; 108 FN(HistogramAddHistogram)(&out[best_idx1], &out[best_idx2]); 109 out[best_idx1].bit_cost_ = pairs[0].cost_combo; 110 cluster_size[best_idx1] += cluster_size[best_idx2]; 111 for (i = 0; i < symbols_size; ++i) { 112 if (symbols[i] == best_idx2) { 113 symbols[i] = best_idx1; 114 } 115 } 116 for (i = 0; i < num_clusters; ++i) { 117 if (clusters[i] == best_idx2) { 118 memmove(&clusters[i], &clusters[i + 1], 119 (num_clusters - i - 1) * sizeof(clusters[0])); 120 break; 121 } 122 } 123 --num_clusters; 124 { 125 /* Remove pairs intersecting the just combined best pair. */ 126 size_t copy_to_idx = 0; 127 for (i = 0; i < num_pairs; ++i) { 128 HistogramPair* p = &pairs[i]; 129 if (p->idx1 == best_idx1 || p->idx2 == best_idx1 || 130 p->idx1 == best_idx2 || p->idx2 == best_idx2) { 131 /* Remove invalid pair from the queue. */ 132 continue; 133 } 134 if (HistogramPairIsLess(&pairs[0], p)) { 135 /* Replace the top of the queue if needed. */ 136 HistogramPair front = pairs[0]; 137 pairs[0] = *p; 138 pairs[copy_to_idx] = front; 139 } else { 140 pairs[copy_to_idx] = *p; 141 } 142 ++copy_to_idx; 143 } 144 num_pairs = copy_to_idx; 145 } 146 147 /* Push new pairs formed with the combined histogram to the heap. */ 148 for (i = 0; i < num_clusters; ++i) { 149 FN(BrotliCompareAndPushToQueue)(out, cluster_size, best_idx1, clusters[i], 150 max_num_pairs, &pairs[0], &num_pairs); 151 } 152 } 153 return num_clusters; 154 }) 155 156 /* What is the bit cost of moving histogram from cur_symbol to candidate. */ 157 BROTLI_INTERNAL double FN(BrotliHistogramBitCostDistance)( 158 const HistogramType* histogram, const HistogramType* candidate) CODE({ 159 if (histogram->total_count_ == 0) { 160 return 0.0; 161 } else { 162 HistogramType tmp = *histogram; 163 FN(HistogramAddHistogram)(&tmp, candidate); 164 return FN(BrotliPopulationCost)(&tmp) - candidate->bit_cost_; 165 } 166 }) 167 168 /* Find the best 'out' histogram for each of the 'in' histograms. 169 When called, clusters[0..num_clusters) contains the unique values from 170 symbols[0..in_size), but this property is not preserved in this function. 171 Note: we assume that out[]->bit_cost_ is already up-to-date. */ 172 BROTLI_INTERNAL void FN(BrotliHistogramRemap)(const HistogramType* in, 173 size_t in_size, const uint32_t* clusters, size_t num_clusters, 174 HistogramType* out, uint32_t* symbols) CODE({ 175 size_t i; 176 for (i = 0; i < in_size; ++i) { 177 uint32_t best_out = i == 0 ? symbols[0] : symbols[i - 1]; 178 double best_bits = 179 FN(BrotliHistogramBitCostDistance)(&in[i], &out[best_out]); 180 size_t j; 181 for (j = 0; j < num_clusters; ++j) { 182 const double cur_bits = 183 FN(BrotliHistogramBitCostDistance)(&in[i], &out[clusters[j]]); 184 if (cur_bits < best_bits) { 185 best_bits = cur_bits; 186 best_out = clusters[j]; 187 } 188 } 189 symbols[i] = best_out; 190 } 191 192 /* Recompute each out based on raw and symbols. */ 193 for (i = 0; i < num_clusters; ++i) { 194 FN(HistogramClear)(&out[clusters[i]]); 195 } 196 for (i = 0; i < in_size; ++i) { 197 FN(HistogramAddHistogram)(&out[symbols[i]], &in[i]); 198 } 199 }) 200 201 /* Reorders elements of the out[0..length) array and changes values in 202 symbols[0..length) array in the following way: 203 * when called, symbols[] contains indexes into out[], and has N unique 204 values (possibly N < length) 205 * on return, symbols'[i] = f(symbols[i]) and 206 out'[symbols'[i]] = out[symbols[i]], for each 0 <= i < length, 207 where f is a bijection between the range of symbols[] and [0..N), and 208 the first occurrences of values in symbols'[i] come in consecutive 209 increasing order. 210 Returns N, the number of unique values in symbols[]. */ 211 BROTLI_INTERNAL size_t FN(BrotliHistogramReindex)(MemoryManager* m, 212 HistogramType* out, uint32_t* symbols, size_t length) CODE({ 213 static const uint32_t kInvalidIndex = BROTLI_UINT32_MAX; 214 uint32_t* new_index = BROTLI_ALLOC(m, uint32_t, length); 215 uint32_t next_index; 216 HistogramType* tmp; 217 size_t i; 218 if (BROTLI_IS_OOM(m)) return 0; 219 for (i = 0; i < length; ++i) { 220 new_index[i] = kInvalidIndex; 221 } 222 next_index = 0; 223 for (i = 0; i < length; ++i) { 224 if (new_index[symbols[i]] == kInvalidIndex) { 225 new_index[symbols[i]] = next_index; 226 ++next_index; 227 } 228 } 229 /* TODO: by using idea of "cycle-sort" we can avoid allocation of 230 tmp and reduce the number of copying by the factor of 2. */ 231 tmp = BROTLI_ALLOC(m, HistogramType, next_index); 232 if (BROTLI_IS_OOM(m)) return 0; 233 next_index = 0; 234 for (i = 0; i < length; ++i) { 235 if (new_index[symbols[i]] == next_index) { 236 tmp[next_index] = out[symbols[i]]; 237 ++next_index; 238 } 239 symbols[i] = new_index[symbols[i]]; 240 } 241 BROTLI_FREE(m, new_index); 242 for (i = 0; i < next_index; ++i) { 243 out[i] = tmp[i]; 244 } 245 BROTLI_FREE(m, tmp); 246 return next_index; 247 }) 248 249 BROTLI_INTERNAL void FN(BrotliClusterHistograms)( 250 MemoryManager* m, const HistogramType* in, const size_t in_size, 251 size_t max_histograms, HistogramType* out, size_t* out_size, 252 uint32_t* histogram_symbols) CODE({ 253 uint32_t* cluster_size = BROTLI_ALLOC(m, uint32_t, in_size); 254 uint32_t* clusters = BROTLI_ALLOC(m, uint32_t, in_size); 255 size_t num_clusters = 0; 256 const size_t max_input_histograms = 64; 257 size_t pairs_capacity = max_input_histograms * max_input_histograms / 2; 258 /* For the first pass of clustering, we allow all pairs. */ 259 HistogramPair* pairs = BROTLI_ALLOC(m, HistogramPair, pairs_capacity + 1); 260 size_t i; 261 262 if (BROTLI_IS_OOM(m)) return; 263 264 for (i = 0; i < in_size; ++i) { 265 cluster_size[i] = 1; 266 } 267 268 for (i = 0; i < in_size; ++i) { 269 out[i] = in[i]; 270 out[i].bit_cost_ = FN(BrotliPopulationCost)(&in[i]); 271 histogram_symbols[i] = (uint32_t)i; 272 } 273 274 for (i = 0; i < in_size; i += max_input_histograms) { 275 size_t num_to_combine = 276 BROTLI_MIN(size_t, in_size - i, max_input_histograms); 277 size_t num_new_clusters; 278 size_t j; 279 for (j = 0; j < num_to_combine; ++j) { 280 clusters[num_clusters + j] = (uint32_t)(i + j); 281 } 282 num_new_clusters = 283 FN(BrotliHistogramCombine)(out, cluster_size, 284 &histogram_symbols[i], 285 &clusters[num_clusters], pairs, 286 num_to_combine, num_to_combine, 287 max_histograms, pairs_capacity); 288 num_clusters += num_new_clusters; 289 } 290 291 { 292 /* For the second pass, we limit the total number of histogram pairs. 293 After this limit is reached, we only keep searching for the best pair. */ 294 size_t max_num_pairs = BROTLI_MIN(size_t, 295 64 * num_clusters, (num_clusters / 2) * num_clusters); 296 BROTLI_ENSURE_CAPACITY( 297 m, HistogramPair, pairs, pairs_capacity, max_num_pairs + 1); 298 if (BROTLI_IS_OOM(m)) return; 299 300 /* Collapse similar histograms. */ 301 num_clusters = FN(BrotliHistogramCombine)(out, cluster_size, 302 histogram_symbols, clusters, 303 pairs, num_clusters, in_size, 304 max_histograms, max_num_pairs); 305 } 306 BROTLI_FREE(m, pairs); 307 BROTLI_FREE(m, cluster_size); 308 /* Find the optimal map from original histograms to the final ones. */ 309 FN(BrotliHistogramRemap)(in, in_size, clusters, num_clusters, 310 out, histogram_symbols); 311 BROTLI_FREE(m, clusters); 312 /* Convert the context map to a canonical form. */ 313 *out_size = FN(BrotliHistogramReindex)(m, out, histogram_symbols, in_size); 314 if (BROTLI_IS_OOM(m)) return; 315 }) 316 317 #undef HistogramType 318