1 // Copyright 2012 Google Inc. All Rights Reserved. 2 // 3 // Use of this source code is governed by a BSD-style license 4 // that can be found in the COPYING file in the root of the source 5 // tree. An additional intellectual property rights grant can be found 6 // in the file PATENTS. All contributing project authors may 7 // be found in the AUTHORS file in the root of the source tree. 8 // ----------------------------------------------------------------------------- 9 // 10 // Author: Jyrki Alakuijala (jyrki (at) google.com) 11 // 12 #ifdef HAVE_CONFIG_H 13 #include "src/webp/config.h" 14 #endif 15 16 #include <math.h> 17 18 #include "src/enc/backward_references_enc.h" 19 #include "src/enc/histogram_enc.h" 20 #include "src/dsp/lossless.h" 21 #include "src/dsp/lossless_common.h" 22 #include "src/utils/utils.h" 23 24 #define MAX_COST 1.e38 25 26 // Number of partitions for the three dominant (literal, red and blue) symbol 27 // costs. 28 #define NUM_PARTITIONS 4 29 // The size of the bin-hash corresponding to the three dominant costs. 30 #define BIN_SIZE (NUM_PARTITIONS * NUM_PARTITIONS * NUM_PARTITIONS) 31 // Maximum number of histograms allowed in greedy combining algorithm. 32 #define MAX_HISTO_GREEDY 100 33 34 static void HistogramClear(VP8LHistogram* const p) { 35 uint32_t* const literal = p->literal_; 36 const int cache_bits = p->palette_code_bits_; 37 const int histo_size = VP8LGetHistogramSize(cache_bits); 38 memset(p, 0, histo_size); 39 p->palette_code_bits_ = cache_bits; 40 p->literal_ = literal; 41 } 42 43 // Swap two histogram pointers. 44 static void HistogramSwap(VP8LHistogram** const A, VP8LHistogram** const B) { 45 VP8LHistogram* const tmp = *A; 46 *A = *B; 47 *B = tmp; 48 } 49 50 static void HistogramCopy(const VP8LHistogram* const src, 51 VP8LHistogram* const dst) { 52 uint32_t* const dst_literal = dst->literal_; 53 const int dst_cache_bits = dst->palette_code_bits_; 54 const int literal_size = VP8LHistogramNumCodes(dst_cache_bits); 55 const int histo_size = VP8LGetHistogramSize(dst_cache_bits); 56 assert(src->palette_code_bits_ == dst_cache_bits); 57 memcpy(dst, src, histo_size); 58 dst->literal_ = dst_literal; 59 memcpy(dst->literal_, src->literal_, literal_size * sizeof(*dst->literal_)); 60 } 61 62 int VP8LGetHistogramSize(int cache_bits) { 63 const int literal_size = VP8LHistogramNumCodes(cache_bits); 64 const size_t total_size = sizeof(VP8LHistogram) + sizeof(int) * literal_size; 65 assert(total_size <= (size_t)0x7fffffff); 66 return (int)total_size; 67 } 68 69 void VP8LFreeHistogram(VP8LHistogram* const histo) { 70 WebPSafeFree(histo); 71 } 72 73 void VP8LFreeHistogramSet(VP8LHistogramSet* const histo) { 74 WebPSafeFree(histo); 75 } 76 77 void VP8LHistogramStoreRefs(const VP8LBackwardRefs* const refs, 78 VP8LHistogram* const histo) { 79 VP8LRefsCursor c = VP8LRefsCursorInit(refs); 80 while (VP8LRefsCursorOk(&c)) { 81 VP8LHistogramAddSinglePixOrCopy(histo, c.cur_pos, NULL, 0); 82 VP8LRefsCursorNext(&c); 83 } 84 } 85 86 void VP8LHistogramCreate(VP8LHistogram* const p, 87 const VP8LBackwardRefs* const refs, 88 int palette_code_bits) { 89 if (palette_code_bits >= 0) { 90 p->palette_code_bits_ = palette_code_bits; 91 } 92 HistogramClear(p); 93 VP8LHistogramStoreRefs(refs, p); 94 } 95 96 void VP8LHistogramInit(VP8LHistogram* const p, int palette_code_bits, 97 int init_arrays) { 98 p->palette_code_bits_ = palette_code_bits; 99 if (init_arrays) { 100 HistogramClear(p); 101 } else { 102 p->trivial_symbol_ = 0; 103 p->bit_cost_ = 0.; 104 p->literal_cost_ = 0.; 105 p->red_cost_ = 0.; 106 p->blue_cost_ = 0.; 107 memset(p->is_used_, 0, sizeof(p->is_used_)); 108 } 109 } 110 111 VP8LHistogram* VP8LAllocateHistogram(int cache_bits) { 112 VP8LHistogram* histo = NULL; 113 const int total_size = VP8LGetHistogramSize(cache_bits); 114 uint8_t* const memory = (uint8_t*)WebPSafeMalloc(total_size, sizeof(*memory)); 115 if (memory == NULL) return NULL; 116 histo = (VP8LHistogram*)memory; 117 // literal_ won't necessary be aligned. 118 histo->literal_ = (uint32_t*)(memory + sizeof(VP8LHistogram)); 119 VP8LHistogramInit(histo, cache_bits, /*init_arrays=*/ 0); 120 return histo; 121 } 122 123 // Resets the pointers of the histograms to point to the bit buffer in the set. 124 static void HistogramSetResetPointers(VP8LHistogramSet* const set, 125 int cache_bits) { 126 int i; 127 const int histo_size = VP8LGetHistogramSize(cache_bits); 128 uint8_t* memory = (uint8_t*) (set->histograms); 129 memory += set->max_size * sizeof(*set->histograms); 130 for (i = 0; i < set->max_size; ++i) { 131 memory = (uint8_t*) WEBP_ALIGN(memory); 132 set->histograms[i] = (VP8LHistogram*) memory; 133 // literal_ won't necessary be aligned. 134 set->histograms[i]->literal_ = (uint32_t*)(memory + sizeof(VP8LHistogram)); 135 memory += histo_size; 136 } 137 } 138 139 // Returns the total size of the VP8LHistogramSet. 140 static size_t HistogramSetTotalSize(int size, int cache_bits) { 141 const int histo_size = VP8LGetHistogramSize(cache_bits); 142 return (sizeof(VP8LHistogramSet) + size * (sizeof(VP8LHistogram*) + 143 histo_size + WEBP_ALIGN_CST)); 144 } 145 146 VP8LHistogramSet* VP8LAllocateHistogramSet(int size, int cache_bits) { 147 int i; 148 VP8LHistogramSet* set; 149 const size_t total_size = HistogramSetTotalSize(size, cache_bits); 150 uint8_t* memory = (uint8_t*)WebPSafeMalloc(total_size, sizeof(*memory)); 151 if (memory == NULL) return NULL; 152 153 set = (VP8LHistogramSet*)memory; 154 memory += sizeof(*set); 155 set->histograms = (VP8LHistogram**)memory; 156 set->max_size = size; 157 set->size = size; 158 HistogramSetResetPointers(set, cache_bits); 159 for (i = 0; i < size; ++i) { 160 VP8LHistogramInit(set->histograms[i], cache_bits, /*init_arrays=*/ 0); 161 } 162 return set; 163 } 164 165 void VP8LHistogramSetClear(VP8LHistogramSet* const set) { 166 int i; 167 const int cache_bits = set->histograms[0]->palette_code_bits_; 168 const int size = set->max_size; 169 const size_t total_size = HistogramSetTotalSize(size, cache_bits); 170 uint8_t* memory = (uint8_t*)set; 171 172 memset(memory, 0, total_size); 173 memory += sizeof(*set); 174 set->histograms = (VP8LHistogram**)memory; 175 set->max_size = size; 176 set->size = size; 177 HistogramSetResetPointers(set, cache_bits); 178 for (i = 0; i < size; ++i) { 179 set->histograms[i]->palette_code_bits_ = cache_bits; 180 } 181 } 182 183 // Removes the histogram 'i' from 'set' by setting it to NULL. 184 static void HistogramSetRemoveHistogram(VP8LHistogramSet* const set, int i, 185 int* const num_used) { 186 assert(set->histograms[i] != NULL); 187 set->histograms[i] = NULL; 188 --*num_used; 189 // If we remove the last valid one, shrink until the next valid one. 190 if (i == set->size - 1) { 191 while (set->size >= 1 && set->histograms[set->size - 1] == NULL) { 192 --set->size; 193 } 194 } 195 } 196 197 // ----------------------------------------------------------------------------- 198 199 void VP8LHistogramAddSinglePixOrCopy(VP8LHistogram* const histo, 200 const PixOrCopy* const v, 201 int (*const distance_modifier)(int, int), 202 int distance_modifier_arg0) { 203 if (PixOrCopyIsLiteral(v)) { 204 ++histo->alpha_[PixOrCopyLiteral(v, 3)]; 205 ++histo->red_[PixOrCopyLiteral(v, 2)]; 206 ++histo->literal_[PixOrCopyLiteral(v, 1)]; 207 ++histo->blue_[PixOrCopyLiteral(v, 0)]; 208 } else if (PixOrCopyIsCacheIdx(v)) { 209 const int literal_ix = 210 NUM_LITERAL_CODES + NUM_LENGTH_CODES + PixOrCopyCacheIdx(v); 211 ++histo->literal_[literal_ix]; 212 } else { 213 int code, extra_bits; 214 VP8LPrefixEncodeBits(PixOrCopyLength(v), &code, &extra_bits); 215 ++histo->literal_[NUM_LITERAL_CODES + code]; 216 if (distance_modifier == NULL) { 217 VP8LPrefixEncodeBits(PixOrCopyDistance(v), &code, &extra_bits); 218 } else { 219 VP8LPrefixEncodeBits( 220 distance_modifier(distance_modifier_arg0, PixOrCopyDistance(v)), 221 &code, &extra_bits); 222 } 223 ++histo->distance_[code]; 224 } 225 } 226 227 // ----------------------------------------------------------------------------- 228 // Entropy-related functions. 229 230 static WEBP_INLINE double BitsEntropyRefine(const VP8LBitEntropy* entropy) { 231 double mix; 232 if (entropy->nonzeros < 5) { 233 if (entropy->nonzeros <= 1) { 234 return 0; 235 } 236 // Two symbols, they will be 0 and 1 in a Huffman code. 237 // Let's mix in a bit of entropy to favor good clustering when 238 // distributions of these are combined. 239 if (entropy->nonzeros == 2) { 240 return 0.99 * entropy->sum + 0.01 * entropy->entropy; 241 } 242 // No matter what the entropy says, we cannot be better than min_limit 243 // with Huffman coding. I am mixing a bit of entropy into the 244 // min_limit since it produces much better (~0.5 %) compression results 245 // perhaps because of better entropy clustering. 246 if (entropy->nonzeros == 3) { 247 mix = 0.95; 248 } else { 249 mix = 0.7; // nonzeros == 4. 250 } 251 } else { 252 mix = 0.627; 253 } 254 255 { 256 double min_limit = 2 * entropy->sum - entropy->max_val; 257 min_limit = mix * min_limit + (1.0 - mix) * entropy->entropy; 258 return (entropy->entropy < min_limit) ? min_limit : entropy->entropy; 259 } 260 } 261 262 double VP8LBitsEntropy(const uint32_t* const array, int n) { 263 VP8LBitEntropy entropy; 264 VP8LBitsEntropyUnrefined(array, n, &entropy); 265 266 return BitsEntropyRefine(&entropy); 267 } 268 269 static double InitialHuffmanCost(void) { 270 // Small bias because Huffman code length is typically not stored in 271 // full length. 272 static const int kHuffmanCodeOfHuffmanCodeSize = CODE_LENGTH_CODES * 3; 273 static const double kSmallBias = 9.1; 274 return kHuffmanCodeOfHuffmanCodeSize - kSmallBias; 275 } 276 277 // Finalize the Huffman cost based on streak numbers and length type (<3 or >=3) 278 static double FinalHuffmanCost(const VP8LStreaks* const stats) { 279 // The constants in this function are experimental and got rounded from 280 // their original values in 1/8 when switched to 1/1024. 281 double retval = InitialHuffmanCost(); 282 // Second coefficient: Many zeros in the histogram are covered efficiently 283 // by a run-length encode. Originally 2/8. 284 retval += stats->counts[0] * 1.5625 + 0.234375 * stats->streaks[0][1]; 285 // Second coefficient: Constant values are encoded less efficiently, but still 286 // RLE'ed. Originally 6/8. 287 retval += stats->counts[1] * 2.578125 + 0.703125 * stats->streaks[1][1]; 288 // 0s are usually encoded more efficiently than non-0s. 289 // Originally 15/8. 290 retval += 1.796875 * stats->streaks[0][0]; 291 // Originally 26/8. 292 retval += 3.28125 * stats->streaks[1][0]; 293 return retval; 294 } 295 296 // Get the symbol entropy for the distribution 'population'. 297 // Set 'trivial_sym', if there's only one symbol present in the distribution. 298 static double PopulationCost(const uint32_t* const population, int length, 299 uint32_t* const trivial_sym, 300 uint8_t* const is_used) { 301 VP8LBitEntropy bit_entropy; 302 VP8LStreaks stats; 303 VP8LGetEntropyUnrefined(population, length, &bit_entropy, &stats); 304 if (trivial_sym != NULL) { 305 *trivial_sym = (bit_entropy.nonzeros == 1) ? bit_entropy.nonzero_code 306 : VP8L_NON_TRIVIAL_SYM; 307 } 308 // The histogram is used if there is at least one non-zero streak. 309 *is_used = (stats.streaks[1][0] != 0 || stats.streaks[1][1] != 0); 310 311 return BitsEntropyRefine(&bit_entropy) + FinalHuffmanCost(&stats); 312 } 313 314 // trivial_at_end is 1 if the two histograms only have one element that is 315 // non-zero: both the zero-th one, or both the last one. 316 static WEBP_INLINE double GetCombinedEntropy(const uint32_t* const X, 317 const uint32_t* const Y, 318 int length, int is_X_used, 319 int is_Y_used, 320 int trivial_at_end) { 321 VP8LStreaks stats; 322 if (trivial_at_end) { 323 // This configuration is due to palettization that transforms an indexed 324 // pixel into 0xff000000 | (pixel << 8) in VP8LBundleColorMap. 325 // BitsEntropyRefine is 0 for histograms with only one non-zero value. 326 // Only FinalHuffmanCost needs to be evaluated. 327 memset(&stats, 0, sizeof(stats)); 328 // Deal with the non-zero value at index 0 or length-1. 329 stats.streaks[1][0] = 1; 330 // Deal with the following/previous zero streak. 331 stats.counts[0] = 1; 332 stats.streaks[0][1] = length - 1; 333 return FinalHuffmanCost(&stats); 334 } else { 335 VP8LBitEntropy bit_entropy; 336 if (is_X_used) { 337 if (is_Y_used) { 338 VP8LGetCombinedEntropyUnrefined(X, Y, length, &bit_entropy, &stats); 339 } else { 340 VP8LGetEntropyUnrefined(X, length, &bit_entropy, &stats); 341 } 342 } else { 343 if (is_Y_used) { 344 VP8LGetEntropyUnrefined(Y, length, &bit_entropy, &stats); 345 } else { 346 memset(&stats, 0, sizeof(stats)); 347 stats.counts[0] = 1; 348 stats.streaks[0][length > 3] = length; 349 VP8LBitEntropyInit(&bit_entropy); 350 } 351 } 352 353 return BitsEntropyRefine(&bit_entropy) + FinalHuffmanCost(&stats); 354 } 355 } 356 357 // Estimates the Entropy + Huffman + other block overhead size cost. 358 double VP8LHistogramEstimateBits(VP8LHistogram* const p) { 359 return 360 PopulationCost(p->literal_, VP8LHistogramNumCodes(p->palette_code_bits_), 361 NULL, &p->is_used_[0]) 362 + PopulationCost(p->red_, NUM_LITERAL_CODES, NULL, &p->is_used_[1]) 363 + PopulationCost(p->blue_, NUM_LITERAL_CODES, NULL, &p->is_used_[2]) 364 + PopulationCost(p->alpha_, NUM_LITERAL_CODES, NULL, &p->is_used_[3]) 365 + PopulationCost(p->distance_, NUM_DISTANCE_CODES, NULL, &p->is_used_[4]) 366 + VP8LExtraCost(p->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES) 367 + VP8LExtraCost(p->distance_, NUM_DISTANCE_CODES); 368 } 369 370 // ----------------------------------------------------------------------------- 371 // Various histogram combine/cost-eval functions 372 373 static int GetCombinedHistogramEntropy(const VP8LHistogram* const a, 374 const VP8LHistogram* const b, 375 double cost_threshold, 376 double* cost) { 377 const int palette_code_bits = a->palette_code_bits_; 378 int trivial_at_end = 0; 379 assert(a->palette_code_bits_ == b->palette_code_bits_); 380 *cost += GetCombinedEntropy(a->literal_, b->literal_, 381 VP8LHistogramNumCodes(palette_code_bits), 382 a->is_used_[0], b->is_used_[0], 0); 383 *cost += VP8LExtraCostCombined(a->literal_ + NUM_LITERAL_CODES, 384 b->literal_ + NUM_LITERAL_CODES, 385 NUM_LENGTH_CODES); 386 if (*cost > cost_threshold) return 0; 387 388 if (a->trivial_symbol_ != VP8L_NON_TRIVIAL_SYM && 389 a->trivial_symbol_ == b->trivial_symbol_) { 390 // A, R and B are all 0 or 0xff. 391 const uint32_t color_a = (a->trivial_symbol_ >> 24) & 0xff; 392 const uint32_t color_r = (a->trivial_symbol_ >> 16) & 0xff; 393 const uint32_t color_b = (a->trivial_symbol_ >> 0) & 0xff; 394 if ((color_a == 0 || color_a == 0xff) && 395 (color_r == 0 || color_r == 0xff) && 396 (color_b == 0 || color_b == 0xff)) { 397 trivial_at_end = 1; 398 } 399 } 400 401 *cost += 402 GetCombinedEntropy(a->red_, b->red_, NUM_LITERAL_CODES, a->is_used_[1], 403 b->is_used_[1], trivial_at_end); 404 if (*cost > cost_threshold) return 0; 405 406 *cost += 407 GetCombinedEntropy(a->blue_, b->blue_, NUM_LITERAL_CODES, a->is_used_[2], 408 b->is_used_[2], trivial_at_end); 409 if (*cost > cost_threshold) return 0; 410 411 *cost += 412 GetCombinedEntropy(a->alpha_, b->alpha_, NUM_LITERAL_CODES, 413 a->is_used_[3], b->is_used_[3], trivial_at_end); 414 if (*cost > cost_threshold) return 0; 415 416 *cost += 417 GetCombinedEntropy(a->distance_, b->distance_, NUM_DISTANCE_CODES, 418 a->is_used_[4], b->is_used_[4], 0); 419 *cost += 420 VP8LExtraCostCombined(a->distance_, b->distance_, NUM_DISTANCE_CODES); 421 if (*cost > cost_threshold) return 0; 422 423 return 1; 424 } 425 426 static WEBP_INLINE void HistogramAdd(const VP8LHistogram* const a, 427 const VP8LHistogram* const b, 428 VP8LHistogram* const out) { 429 VP8LHistogramAdd(a, b, out); 430 out->trivial_symbol_ = (a->trivial_symbol_ == b->trivial_symbol_) 431 ? a->trivial_symbol_ 432 : VP8L_NON_TRIVIAL_SYM; 433 } 434 435 // Performs out = a + b, computing the cost C(a+b) - C(a) - C(b) while comparing 436 // to the threshold value 'cost_threshold'. The score returned is 437 // Score = C(a+b) - C(a) - C(b), where C(a) + C(b) is known and fixed. 438 // Since the previous score passed is 'cost_threshold', we only need to compare 439 // the partial cost against 'cost_threshold + C(a) + C(b)' to possibly bail-out 440 // early. 441 static double HistogramAddEval(const VP8LHistogram* const a, 442 const VP8LHistogram* const b, 443 VP8LHistogram* const out, 444 double cost_threshold) { 445 double cost = 0; 446 const double sum_cost = a->bit_cost_ + b->bit_cost_; 447 cost_threshold += sum_cost; 448 449 if (GetCombinedHistogramEntropy(a, b, cost_threshold, &cost)) { 450 HistogramAdd(a, b, out); 451 out->bit_cost_ = cost; 452 out->palette_code_bits_ = a->palette_code_bits_; 453 } 454 455 return cost - sum_cost; 456 } 457 458 // Same as HistogramAddEval(), except that the resulting histogram 459 // is not stored. Only the cost C(a+b) - C(a) is evaluated. We omit 460 // the term C(b) which is constant over all the evaluations. 461 static double HistogramAddThresh(const VP8LHistogram* const a, 462 const VP8LHistogram* const b, 463 double cost_threshold) { 464 double cost; 465 assert(a != NULL && b != NULL); 466 cost = -a->bit_cost_; 467 GetCombinedHistogramEntropy(a, b, cost_threshold, &cost); 468 return cost; 469 } 470 471 // ----------------------------------------------------------------------------- 472 473 // The structure to keep track of cost range for the three dominant entropy 474 // symbols. 475 // TODO(skal): Evaluate if float can be used here instead of double for 476 // representing the entropy costs. 477 typedef struct { 478 double literal_max_; 479 double literal_min_; 480 double red_max_; 481 double red_min_; 482 double blue_max_; 483 double blue_min_; 484 } DominantCostRange; 485 486 static void DominantCostRangeInit(DominantCostRange* const c) { 487 c->literal_max_ = 0.; 488 c->literal_min_ = MAX_COST; 489 c->red_max_ = 0.; 490 c->red_min_ = MAX_COST; 491 c->blue_max_ = 0.; 492 c->blue_min_ = MAX_COST; 493 } 494 495 static void UpdateDominantCostRange( 496 const VP8LHistogram* const h, DominantCostRange* const c) { 497 if (c->literal_max_ < h->literal_cost_) c->literal_max_ = h->literal_cost_; 498 if (c->literal_min_ > h->literal_cost_) c->literal_min_ = h->literal_cost_; 499 if (c->red_max_ < h->red_cost_) c->red_max_ = h->red_cost_; 500 if (c->red_min_ > h->red_cost_) c->red_min_ = h->red_cost_; 501 if (c->blue_max_ < h->blue_cost_) c->blue_max_ = h->blue_cost_; 502 if (c->blue_min_ > h->blue_cost_) c->blue_min_ = h->blue_cost_; 503 } 504 505 static void UpdateHistogramCost(VP8LHistogram* const h) { 506 uint32_t alpha_sym, red_sym, blue_sym; 507 const double alpha_cost = 508 PopulationCost(h->alpha_, NUM_LITERAL_CODES, &alpha_sym, 509 &h->is_used_[3]); 510 const double distance_cost = 511 PopulationCost(h->distance_, NUM_DISTANCE_CODES, NULL, &h->is_used_[4]) + 512 VP8LExtraCost(h->distance_, NUM_DISTANCE_CODES); 513 const int num_codes = VP8LHistogramNumCodes(h->palette_code_bits_); 514 h->literal_cost_ = 515 PopulationCost(h->literal_, num_codes, NULL, &h->is_used_[0]) + 516 VP8LExtraCost(h->literal_ + NUM_LITERAL_CODES, NUM_LENGTH_CODES); 517 h->red_cost_ = 518 PopulationCost(h->red_, NUM_LITERAL_CODES, &red_sym, &h->is_used_[1]); 519 h->blue_cost_ = 520 PopulationCost(h->blue_, NUM_LITERAL_CODES, &blue_sym, &h->is_used_[2]); 521 h->bit_cost_ = h->literal_cost_ + h->red_cost_ + h->blue_cost_ + 522 alpha_cost + distance_cost; 523 if ((alpha_sym | red_sym | blue_sym) == VP8L_NON_TRIVIAL_SYM) { 524 h->trivial_symbol_ = VP8L_NON_TRIVIAL_SYM; 525 } else { 526 h->trivial_symbol_ = 527 ((uint32_t)alpha_sym << 24) | (red_sym << 16) | (blue_sym << 0); 528 } 529 } 530 531 static int GetBinIdForEntropy(double min, double max, double val) { 532 const double range = max - min; 533 if (range > 0.) { 534 const double delta = val - min; 535 return (int)((NUM_PARTITIONS - 1e-6) * delta / range); 536 } else { 537 return 0; 538 } 539 } 540 541 static int GetHistoBinIndex(const VP8LHistogram* const h, 542 const DominantCostRange* const c, int low_effort) { 543 int bin_id = GetBinIdForEntropy(c->literal_min_, c->literal_max_, 544 h->literal_cost_); 545 assert(bin_id < NUM_PARTITIONS); 546 if (!low_effort) { 547 bin_id = bin_id * NUM_PARTITIONS 548 + GetBinIdForEntropy(c->red_min_, c->red_max_, h->red_cost_); 549 bin_id = bin_id * NUM_PARTITIONS 550 + GetBinIdForEntropy(c->blue_min_, c->blue_max_, h->blue_cost_); 551 assert(bin_id < BIN_SIZE); 552 } 553 return bin_id; 554 } 555 556 // Construct the histograms from backward references. 557 static void HistogramBuild( 558 int xsize, int histo_bits, const VP8LBackwardRefs* const backward_refs, 559 VP8LHistogramSet* const image_histo) { 560 int x = 0, y = 0; 561 const int histo_xsize = VP8LSubSampleSize(xsize, histo_bits); 562 VP8LHistogram** const histograms = image_histo->histograms; 563 VP8LRefsCursor c = VP8LRefsCursorInit(backward_refs); 564 assert(histo_bits > 0); 565 VP8LHistogramSetClear(image_histo); 566 while (VP8LRefsCursorOk(&c)) { 567 const PixOrCopy* const v = c.cur_pos; 568 const int ix = (y >> histo_bits) * histo_xsize + (x >> histo_bits); 569 VP8LHistogramAddSinglePixOrCopy(histograms[ix], v, NULL, 0); 570 x += PixOrCopyLength(v); 571 while (x >= xsize) { 572 x -= xsize; 573 ++y; 574 } 575 VP8LRefsCursorNext(&c); 576 } 577 } 578 579 // Copies the histograms and computes its bit_cost. 580 static const uint16_t kInvalidHistogramSymbol = (uint16_t)(-1); 581 static void HistogramCopyAndAnalyze(VP8LHistogramSet* const orig_histo, 582 VP8LHistogramSet* const image_histo, 583 int* const num_used, 584 uint16_t* const histogram_symbols) { 585 int i, cluster_id; 586 int num_used_orig = *num_used; 587 VP8LHistogram** const orig_histograms = orig_histo->histograms; 588 VP8LHistogram** const histograms = image_histo->histograms; 589 assert(image_histo->max_size == orig_histo->max_size); 590 for (cluster_id = 0, i = 0; i < orig_histo->max_size; ++i) { 591 VP8LHistogram* const histo = orig_histograms[i]; 592 UpdateHistogramCost(histo); 593 594 // Skip the histogram if it is completely empty, which can happen for tiles 595 // with no information (when they are skipped because of LZ77). 596 if (!histo->is_used_[0] && !histo->is_used_[1] && !histo->is_used_[2] 597 && !histo->is_used_[3] && !histo->is_used_[4]) { 598 // The first histogram is always used. If an histogram is empty, we set 599 // its id to be the same as the previous one: this will improve 600 // compressibility for later LZ77. 601 assert(i > 0); 602 HistogramSetRemoveHistogram(image_histo, i, num_used); 603 HistogramSetRemoveHistogram(orig_histo, i, &num_used_orig); 604 histogram_symbols[i] = kInvalidHistogramSymbol; 605 } else { 606 // Copy histograms from orig_histo[] to image_histo[]. 607 HistogramCopy(histo, histograms[i]); 608 histogram_symbols[i] = cluster_id++; 609 assert(cluster_id <= image_histo->max_size); 610 } 611 } 612 } 613 614 // Partition histograms to different entropy bins for three dominant (literal, 615 // red and blue) symbol costs and compute the histogram aggregate bit_cost. 616 static void HistogramAnalyzeEntropyBin(VP8LHistogramSet* const image_histo, 617 uint16_t* const bin_map, 618 int low_effort) { 619 int i; 620 VP8LHistogram** const histograms = image_histo->histograms; 621 const int histo_size = image_histo->size; 622 DominantCostRange cost_range; 623 DominantCostRangeInit(&cost_range); 624 625 // Analyze the dominant (literal, red and blue) entropy costs. 626 for (i = 0; i < histo_size; ++i) { 627 if (histograms[i] == NULL) continue; 628 UpdateDominantCostRange(histograms[i], &cost_range); 629 } 630 631 // bin-hash histograms on three of the dominant (literal, red and blue) 632 // symbol costs and store the resulting bin_id for each histogram. 633 for (i = 0; i < histo_size; ++i) { 634 // bin_map[i] is not set to a special value as its use will later be guarded 635 // by another (histograms[i] == NULL). 636 if (histograms[i] == NULL) continue; 637 bin_map[i] = GetHistoBinIndex(histograms[i], &cost_range, low_effort); 638 } 639 } 640 641 // Merges some histograms with same bin_id together if it's advantageous. 642 // Sets the remaining histograms to NULL. 643 static void HistogramCombineEntropyBin(VP8LHistogramSet* const image_histo, 644 int *num_used, 645 const uint16_t* const clusters, 646 uint16_t* const cluster_mappings, 647 VP8LHistogram* cur_combo, 648 const uint16_t* const bin_map, 649 int num_bins, 650 double combine_cost_factor, 651 int low_effort) { 652 VP8LHistogram** const histograms = image_histo->histograms; 653 int idx; 654 struct { 655 int16_t first; // position of the histogram that accumulates all 656 // histograms with the same bin_id 657 uint16_t num_combine_failures; // number of combine failures per bin_id 658 } bin_info[BIN_SIZE]; 659 660 assert(num_bins <= BIN_SIZE); 661 for (idx = 0; idx < num_bins; ++idx) { 662 bin_info[idx].first = -1; 663 bin_info[idx].num_combine_failures = 0; 664 } 665 666 // By default, a cluster matches itself. 667 for (idx = 0; idx < *num_used; ++idx) cluster_mappings[idx] = idx; 668 for (idx = 0; idx < image_histo->size; ++idx) { 669 int bin_id, first; 670 if (histograms[idx] == NULL) continue; 671 bin_id = bin_map[idx]; 672 first = bin_info[bin_id].first; 673 if (first == -1) { 674 bin_info[bin_id].first = idx; 675 } else if (low_effort) { 676 HistogramAdd(histograms[idx], histograms[first], histograms[first]); 677 HistogramSetRemoveHistogram(image_histo, idx, num_used); 678 cluster_mappings[clusters[idx]] = clusters[first]; 679 } else { 680 // try to merge #idx into #first (both share the same bin_id) 681 const double bit_cost = histograms[idx]->bit_cost_; 682 const double bit_cost_thresh = -bit_cost * combine_cost_factor; 683 const double curr_cost_diff = 684 HistogramAddEval(histograms[first], histograms[idx], 685 cur_combo, bit_cost_thresh); 686 if (curr_cost_diff < bit_cost_thresh) { 687 // Try to merge two histograms only if the combo is a trivial one or 688 // the two candidate histograms are already non-trivial. 689 // For some images, 'try_combine' turns out to be false for a lot of 690 // histogram pairs. In that case, we fallback to combining 691 // histograms as usual to avoid increasing the header size. 692 const int try_combine = 693 (cur_combo->trivial_symbol_ != VP8L_NON_TRIVIAL_SYM) || 694 ((histograms[idx]->trivial_symbol_ == VP8L_NON_TRIVIAL_SYM) && 695 (histograms[first]->trivial_symbol_ == VP8L_NON_TRIVIAL_SYM)); 696 const int max_combine_failures = 32; 697 if (try_combine || 698 bin_info[bin_id].num_combine_failures >= max_combine_failures) { 699 // move the (better) merged histogram to its final slot 700 HistogramSwap(&cur_combo, &histograms[first]); 701 HistogramSetRemoveHistogram(image_histo, idx, num_used); 702 cluster_mappings[clusters[idx]] = clusters[first]; 703 } else { 704 ++bin_info[bin_id].num_combine_failures; 705 } 706 } 707 } 708 } 709 if (low_effort) { 710 // for low_effort case, update the final cost when everything is merged 711 for (idx = 0; idx < image_histo->size; ++idx) { 712 if (histograms[idx] == NULL) continue; 713 UpdateHistogramCost(histograms[idx]); 714 } 715 } 716 } 717 718 // Implement a Lehmer random number generator with a multiplicative constant of 719 // 48271 and a modulo constant of 2^31 - 1. 720 static uint32_t MyRand(uint32_t* const seed) { 721 *seed = (uint32_t)(((uint64_t)(*seed) * 48271u) % 2147483647u); 722 assert(*seed > 0); 723 return *seed; 724 } 725 726 // ----------------------------------------------------------------------------- 727 // Histogram pairs priority queue 728 729 // Pair of histograms. Negative idx1 value means that pair is out-of-date. 730 typedef struct { 731 int idx1; 732 int idx2; 733 double cost_diff; 734 double cost_combo; 735 } HistogramPair; 736 737 typedef struct { 738 HistogramPair* queue; 739 int size; 740 int max_size; 741 } HistoQueue; 742 743 static int HistoQueueInit(HistoQueue* const histo_queue, const int max_size) { 744 histo_queue->size = 0; 745 histo_queue->max_size = max_size; 746 // We allocate max_size + 1 because the last element at index "size" is 747 // used as temporary data (and it could be up to max_size). 748 histo_queue->queue = (HistogramPair*)WebPSafeMalloc( 749 histo_queue->max_size + 1, sizeof(*histo_queue->queue)); 750 return histo_queue->queue != NULL; 751 } 752 753 static void HistoQueueClear(HistoQueue* const histo_queue) { 754 assert(histo_queue != NULL); 755 WebPSafeFree(histo_queue->queue); 756 histo_queue->size = 0; 757 histo_queue->max_size = 0; 758 } 759 760 // Pop a specific pair in the queue by replacing it with the last one 761 // and shrinking the queue. 762 static void HistoQueuePopPair(HistoQueue* const histo_queue, 763 HistogramPair* const pair) { 764 assert(pair >= histo_queue->queue && 765 pair < (histo_queue->queue + histo_queue->size)); 766 assert(histo_queue->size > 0); 767 *pair = histo_queue->queue[histo_queue->size - 1]; 768 --histo_queue->size; 769 } 770 771 // Check whether a pair in the queue should be updated as head or not. 772 static void HistoQueueUpdateHead(HistoQueue* const histo_queue, 773 HistogramPair* const pair) { 774 assert(pair->cost_diff < 0.); 775 assert(pair >= histo_queue->queue && 776 pair < (histo_queue->queue + histo_queue->size)); 777 assert(histo_queue->size > 0); 778 if (pair->cost_diff < histo_queue->queue[0].cost_diff) { 779 // Replace the best pair. 780 const HistogramPair tmp = histo_queue->queue[0]; 781 histo_queue->queue[0] = *pair; 782 *pair = tmp; 783 } 784 } 785 786 // Update the cost diff and combo of a pair of histograms. This needs to be 787 // called when the the histograms have been merged with a third one. 788 static void HistoQueueUpdatePair(const VP8LHistogram* const h1, 789 const VP8LHistogram* const h2, 790 double threshold, 791 HistogramPair* const pair) { 792 const double sum_cost = h1->bit_cost_ + h2->bit_cost_; 793 pair->cost_combo = 0.; 794 GetCombinedHistogramEntropy(h1, h2, sum_cost + threshold, &pair->cost_combo); 795 pair->cost_diff = pair->cost_combo - sum_cost; 796 } 797 798 // Create a pair from indices "idx1" and "idx2" provided its cost 799 // is inferior to "threshold", a negative entropy. 800 // It returns the cost of the pair, or 0. if it superior to threshold. 801 static double HistoQueuePush(HistoQueue* const histo_queue, 802 VP8LHistogram** const histograms, int idx1, 803 int idx2, double threshold) { 804 const VP8LHistogram* h1; 805 const VP8LHistogram* h2; 806 HistogramPair pair; 807 808 // Stop here if the queue is full. 809 if (histo_queue->size == histo_queue->max_size) return 0.; 810 assert(threshold <= 0.); 811 if (idx1 > idx2) { 812 const int tmp = idx2; 813 idx2 = idx1; 814 idx1 = tmp; 815 } 816 pair.idx1 = idx1; 817 pair.idx2 = idx2; 818 h1 = histograms[idx1]; 819 h2 = histograms[idx2]; 820 821 HistoQueueUpdatePair(h1, h2, threshold, &pair); 822 823 // Do not even consider the pair if it does not improve the entropy. 824 if (pair.cost_diff >= threshold) return 0.; 825 826 histo_queue->queue[histo_queue->size++] = pair; 827 HistoQueueUpdateHead(histo_queue, &histo_queue->queue[histo_queue->size - 1]); 828 829 return pair.cost_diff; 830 } 831 832 // ----------------------------------------------------------------------------- 833 834 // Combines histograms by continuously choosing the one with the highest cost 835 // reduction. 836 static int HistogramCombineGreedy(VP8LHistogramSet* const image_histo, 837 int* const num_used) { 838 int ok = 0; 839 const int image_histo_size = image_histo->size; 840 int i, j; 841 VP8LHistogram** const histograms = image_histo->histograms; 842 // Priority queue of histogram pairs. 843 HistoQueue histo_queue; 844 845 // image_histo_size^2 for the queue size is safe. If you look at 846 // HistogramCombineGreedy, and imagine that UpdateQueueFront always pushes 847 // data to the queue, you insert at most: 848 // - image_histo_size*(image_histo_size-1)/2 (the first two for loops) 849 // - image_histo_size - 1 in the last for loop at the first iteration of 850 // the while loop, image_histo_size - 2 at the second iteration ... 851 // therefore image_histo_size*(image_histo_size-1)/2 overall too 852 if (!HistoQueueInit(&histo_queue, image_histo_size * image_histo_size)) { 853 goto End; 854 } 855 856 for (i = 0; i < image_histo_size; ++i) { 857 if (image_histo->histograms[i] == NULL) continue; 858 for (j = i + 1; j < image_histo_size; ++j) { 859 // Initialize queue. 860 if (image_histo->histograms[j] == NULL) continue; 861 HistoQueuePush(&histo_queue, histograms, i, j, 0.); 862 } 863 } 864 865 while (histo_queue.size > 0) { 866 const int idx1 = histo_queue.queue[0].idx1; 867 const int idx2 = histo_queue.queue[0].idx2; 868 HistogramAdd(histograms[idx2], histograms[idx1], histograms[idx1]); 869 histograms[idx1]->bit_cost_ = histo_queue.queue[0].cost_combo; 870 871 // Remove merged histogram. 872 HistogramSetRemoveHistogram(image_histo, idx2, num_used); 873 874 // Remove pairs intersecting the just combined best pair. 875 for (i = 0; i < histo_queue.size;) { 876 HistogramPair* const p = histo_queue.queue + i; 877 if (p->idx1 == idx1 || p->idx2 == idx1 || 878 p->idx1 == idx2 || p->idx2 == idx2) { 879 HistoQueuePopPair(&histo_queue, p); 880 } else { 881 HistoQueueUpdateHead(&histo_queue, p); 882 ++i; 883 } 884 } 885 886 // Push new pairs formed with combined histogram to the queue. 887 for (i = 0; i < image_histo->size; ++i) { 888 if (i == idx1 || image_histo->histograms[i] == NULL) continue; 889 HistoQueuePush(&histo_queue, image_histo->histograms, idx1, i, 0.); 890 } 891 } 892 893 ok = 1; 894 895 End: 896 HistoQueueClear(&histo_queue); 897 return ok; 898 } 899 900 // Perform histogram aggregation using a stochastic approach. 901 // 'do_greedy' is set to 1 if a greedy approach needs to be performed 902 // afterwards, 0 otherwise. 903 static int PairComparison(const void* idx1, const void* idx2) { 904 // To be used with bsearch: <0 when *idx1<*idx2, >0 if >, 0 when ==. 905 return (*(int*) idx1 - *(int*) idx2); 906 } 907 static int HistogramCombineStochastic(VP8LHistogramSet* const image_histo, 908 int* const num_used, int min_cluster_size, 909 int* const do_greedy) { 910 int j, iter; 911 uint32_t seed = 1; 912 int tries_with_no_success = 0; 913 const int outer_iters = *num_used; 914 const int num_tries_no_success = outer_iters / 2; 915 VP8LHistogram** const histograms = image_histo->histograms; 916 // Priority queue of histogram pairs. Its size of 'kHistoQueueSize' 917 // impacts the quality of the compression and the speed: the smaller the 918 // faster but the worse for the compression. 919 HistoQueue histo_queue; 920 const int kHistoQueueSize = 9; 921 int ok = 0; 922 // mapping from an index in image_histo with no NULL histogram to the full 923 // blown image_histo. 924 int* mappings; 925 926 if (*num_used < min_cluster_size) { 927 *do_greedy = 1; 928 return 1; 929 } 930 931 mappings = (int*) WebPSafeMalloc(*num_used, sizeof(*mappings)); 932 if (mappings == NULL) return 0; 933 if (!HistoQueueInit(&histo_queue, kHistoQueueSize)) goto End; 934 // Fill the initial mapping. 935 for (j = 0, iter = 0; iter < image_histo->size; ++iter) { 936 if (histograms[iter] == NULL) continue; 937 mappings[j++] = iter; 938 } 939 assert(j == *num_used); 940 941 // Collapse similar histograms in 'image_histo'. 942 for (iter = 0; 943 iter < outer_iters && *num_used >= min_cluster_size && 944 ++tries_with_no_success < num_tries_no_success; 945 ++iter) { 946 int* mapping_index; 947 double best_cost = 948 (histo_queue.size == 0) ? 0. : histo_queue.queue[0].cost_diff; 949 int best_idx1 = -1, best_idx2 = 1; 950 const uint32_t rand_range = (*num_used - 1) * (*num_used); 951 // (*num_used) / 2 was chosen empirically. Less means faster but worse 952 // compression. 953 const int num_tries = (*num_used) / 2; 954 955 // Pick random samples. 956 for (j = 0; *num_used >= 2 && j < num_tries; ++j) { 957 double curr_cost; 958 // Choose two different histograms at random and try to combine them. 959 const uint32_t tmp = MyRand(&seed) % rand_range; 960 uint32_t idx1 = tmp / (*num_used - 1); 961 uint32_t idx2 = tmp % (*num_used - 1); 962 if (idx2 >= idx1) ++idx2; 963 idx1 = mappings[idx1]; 964 idx2 = mappings[idx2]; 965 966 // Calculate cost reduction on combination. 967 curr_cost = 968 HistoQueuePush(&histo_queue, histograms, idx1, idx2, best_cost); 969 if (curr_cost < 0) { // found a better pair? 970 best_cost = curr_cost; 971 // Empty the queue if we reached full capacity. 972 if (histo_queue.size == histo_queue.max_size) break; 973 } 974 } 975 if (histo_queue.size == 0) continue; 976 977 // Get the best histograms. 978 best_idx1 = histo_queue.queue[0].idx1; 979 best_idx2 = histo_queue.queue[0].idx2; 980 assert(best_idx1 < best_idx2); 981 // Pop best_idx2 from mappings. 982 mapping_index = (int*) bsearch(&best_idx2, mappings, *num_used, 983 sizeof(best_idx2), &PairComparison); 984 assert(mapping_index != NULL); 985 memmove(mapping_index, mapping_index + 1, sizeof(*mapping_index) * 986 ((*num_used) - (mapping_index - mappings) - 1)); 987 // Merge the histograms and remove best_idx2 from the queue. 988 HistogramAdd(histograms[best_idx2], histograms[best_idx1], 989 histograms[best_idx1]); 990 histograms[best_idx1]->bit_cost_ = histo_queue.queue[0].cost_combo; 991 HistogramSetRemoveHistogram(image_histo, best_idx2, num_used); 992 // Parse the queue and update each pair that deals with best_idx1, 993 // best_idx2 or image_histo_size. 994 for (j = 0; j < histo_queue.size;) { 995 HistogramPair* const p = histo_queue.queue + j; 996 const int is_idx1_best = p->idx1 == best_idx1 || p->idx1 == best_idx2; 997 const int is_idx2_best = p->idx2 == best_idx1 || p->idx2 == best_idx2; 998 int do_eval = 0; 999 // The front pair could have been duplicated by a random pick so 1000 // check for it all the time nevertheless. 1001 if (is_idx1_best && is_idx2_best) { 1002 HistoQueuePopPair(&histo_queue, p); 1003 continue; 1004 } 1005 // Any pair containing one of the two best indices should only refer to 1006 // best_idx1. Its cost should also be updated. 1007 if (is_idx1_best) { 1008 p->idx1 = best_idx1; 1009 do_eval = 1; 1010 } else if (is_idx2_best) { 1011 p->idx2 = best_idx1; 1012 do_eval = 1; 1013 } 1014 // Make sure the index order is respected. 1015 if (p->idx1 > p->idx2) { 1016 const int tmp = p->idx2; 1017 p->idx2 = p->idx1; 1018 p->idx1 = tmp; 1019 } 1020 if (do_eval) { 1021 // Re-evaluate the cost of an updated pair. 1022 HistoQueueUpdatePair(histograms[p->idx1], histograms[p->idx2], 0., p); 1023 if (p->cost_diff >= 0.) { 1024 HistoQueuePopPair(&histo_queue, p); 1025 continue; 1026 } 1027 } 1028 HistoQueueUpdateHead(&histo_queue, p); 1029 ++j; 1030 } 1031 tries_with_no_success = 0; 1032 } 1033 *do_greedy = (*num_used <= min_cluster_size); 1034 ok = 1; 1035 1036 End: 1037 HistoQueueClear(&histo_queue); 1038 WebPSafeFree(mappings); 1039 return ok; 1040 } 1041 1042 // ----------------------------------------------------------------------------- 1043 // Histogram refinement 1044 1045 // Find the best 'out' histogram for each of the 'in' histograms. 1046 // At call-time, 'out' contains the histograms of the clusters. 1047 // Note: we assume that out[]->bit_cost_ is already up-to-date. 1048 static void HistogramRemap(const VP8LHistogramSet* const in, 1049 VP8LHistogramSet* const out, 1050 uint16_t* const symbols) { 1051 int i; 1052 VP8LHistogram** const in_histo = in->histograms; 1053 VP8LHistogram** const out_histo = out->histograms; 1054 const int in_size = out->max_size; 1055 const int out_size = out->size; 1056 if (out_size > 1) { 1057 for (i = 0; i < in_size; ++i) { 1058 int best_out = 0; 1059 double best_bits = MAX_COST; 1060 int k; 1061 if (in_histo[i] == NULL) { 1062 // Arbitrarily set to the previous value if unused to help future LZ77. 1063 symbols[i] = symbols[i - 1]; 1064 continue; 1065 } 1066 for (k = 0; k < out_size; ++k) { 1067 double cur_bits; 1068 cur_bits = HistogramAddThresh(out_histo[k], in_histo[i], best_bits); 1069 if (k == 0 || cur_bits < best_bits) { 1070 best_bits = cur_bits; 1071 best_out = k; 1072 } 1073 } 1074 symbols[i] = best_out; 1075 } 1076 } else { 1077 assert(out_size == 1); 1078 for (i = 0; i < in_size; ++i) { 1079 symbols[i] = 0; 1080 } 1081 } 1082 1083 // Recompute each out based on raw and symbols. 1084 VP8LHistogramSetClear(out); 1085 out->size = out_size; 1086 1087 for (i = 0; i < in_size; ++i) { 1088 int idx; 1089 if (in_histo[i] == NULL) continue; 1090 idx = symbols[i]; 1091 HistogramAdd(in_histo[i], out_histo[idx], out_histo[idx]); 1092 } 1093 } 1094 1095 static double GetCombineCostFactor(int histo_size, int quality) { 1096 double combine_cost_factor = 0.16; 1097 if (quality < 90) { 1098 if (histo_size > 256) combine_cost_factor /= 2.; 1099 if (histo_size > 512) combine_cost_factor /= 2.; 1100 if (histo_size > 1024) combine_cost_factor /= 2.; 1101 if (quality <= 50) combine_cost_factor /= 2.; 1102 } 1103 return combine_cost_factor; 1104 } 1105 1106 // Given a HistogramSet 'set', the mapping of clusters 'cluster_mapping' and the 1107 // current assignment of the cells in 'symbols', merge the clusters and 1108 // assign the smallest possible clusters values. 1109 static void OptimizeHistogramSymbols(const VP8LHistogramSet* const set, 1110 uint16_t* const cluster_mappings, 1111 int num_clusters, 1112 uint16_t* const cluster_mappings_tmp, 1113 uint16_t* const symbols) { 1114 int i, cluster_max; 1115 int do_continue = 1; 1116 // First, assign the lowest cluster to each pixel. 1117 while (do_continue) { 1118 do_continue = 0; 1119 for (i = 0; i < num_clusters; ++i) { 1120 int k; 1121 k = cluster_mappings[i]; 1122 while (k != cluster_mappings[k]) { 1123 cluster_mappings[k] = cluster_mappings[cluster_mappings[k]]; 1124 k = cluster_mappings[k]; 1125 } 1126 if (k != cluster_mappings[i]) { 1127 do_continue = 1; 1128 cluster_mappings[i] = k; 1129 } 1130 } 1131 } 1132 // Create a mapping from a cluster id to its minimal version. 1133 cluster_max = 0; 1134 memset(cluster_mappings_tmp, 0, 1135 set->max_size * sizeof(*cluster_mappings_tmp)); 1136 assert(cluster_mappings[0] == 0); 1137 // Re-map the ids. 1138 for (i = 0; i < set->max_size; ++i) { 1139 int cluster; 1140 if (symbols[i] == kInvalidHistogramSymbol) continue; 1141 cluster = cluster_mappings[symbols[i]]; 1142 assert(symbols[i] < num_clusters); 1143 if (cluster > 0 && cluster_mappings_tmp[cluster] == 0) { 1144 ++cluster_max; 1145 cluster_mappings_tmp[cluster] = cluster_max; 1146 } 1147 symbols[i] = cluster_mappings_tmp[cluster]; 1148 } 1149 1150 // Make sure all cluster values are used. 1151 cluster_max = 0; 1152 for (i = 0; i < set->max_size; ++i) { 1153 if (symbols[i] == kInvalidHistogramSymbol) continue; 1154 if (symbols[i] <= cluster_max) continue; 1155 ++cluster_max; 1156 assert(symbols[i] == cluster_max); 1157 } 1158 } 1159 1160 static void RemoveEmptyHistograms(VP8LHistogramSet* const image_histo) { 1161 uint32_t size; 1162 int i; 1163 for (i = 0, size = 0; i < image_histo->size; ++i) { 1164 if (image_histo->histograms[i] == NULL) continue; 1165 image_histo->histograms[size++] = image_histo->histograms[i]; 1166 } 1167 image_histo->size = size; 1168 } 1169 1170 int VP8LGetHistoImageSymbols(int xsize, int ysize, 1171 const VP8LBackwardRefs* const refs, 1172 int quality, int low_effort, 1173 int histo_bits, int cache_bits, 1174 VP8LHistogramSet* const image_histo, 1175 VP8LHistogram* const tmp_histo, 1176 uint16_t* const histogram_symbols) { 1177 int ok = 0; 1178 const int histo_xsize = histo_bits ? VP8LSubSampleSize(xsize, histo_bits) : 1; 1179 const int histo_ysize = histo_bits ? VP8LSubSampleSize(ysize, histo_bits) : 1; 1180 const int image_histo_raw_size = histo_xsize * histo_ysize; 1181 VP8LHistogramSet* const orig_histo = 1182 VP8LAllocateHistogramSet(image_histo_raw_size, cache_bits); 1183 // Don't attempt linear bin-partition heuristic for 1184 // histograms of small sizes (as bin_map will be very sparse) and 1185 // maximum quality q==100 (to preserve the compression gains at that level). 1186 const int entropy_combine_num_bins = low_effort ? NUM_PARTITIONS : BIN_SIZE; 1187 int entropy_combine; 1188 uint16_t* const map_tmp = 1189 WebPSafeMalloc(2 * image_histo_raw_size, sizeof(map_tmp)); 1190 uint16_t* const cluster_mappings = map_tmp + image_histo_raw_size; 1191 int num_used = image_histo_raw_size; 1192 if (orig_histo == NULL || map_tmp == NULL) goto Error; 1193 1194 // Construct the histograms from backward references. 1195 HistogramBuild(xsize, histo_bits, refs, orig_histo); 1196 // Copies the histograms and computes its bit_cost. 1197 // histogram_symbols is optimized 1198 HistogramCopyAndAnalyze(orig_histo, image_histo, &num_used, 1199 histogram_symbols); 1200 1201 entropy_combine = 1202 (num_used > entropy_combine_num_bins * 2) && (quality < 100); 1203 1204 if (entropy_combine) { 1205 uint16_t* const bin_map = map_tmp; 1206 const double combine_cost_factor = 1207 GetCombineCostFactor(image_histo_raw_size, quality); 1208 const uint32_t num_clusters = num_used; 1209 1210 HistogramAnalyzeEntropyBin(image_histo, bin_map, low_effort); 1211 // Collapse histograms with similar entropy. 1212 HistogramCombineEntropyBin(image_histo, &num_used, histogram_symbols, 1213 cluster_mappings, tmp_histo, bin_map, 1214 entropy_combine_num_bins, combine_cost_factor, 1215 low_effort); 1216 OptimizeHistogramSymbols(image_histo, cluster_mappings, num_clusters, 1217 map_tmp, histogram_symbols); 1218 } 1219 1220 // Don't combine the histograms using stochastic and greedy heuristics for 1221 // low-effort compression mode. 1222 if (!low_effort || !entropy_combine) { 1223 const float x = quality / 100.f; 1224 // cubic ramp between 1 and MAX_HISTO_GREEDY: 1225 const int threshold_size = (int)(1 + (x * x * x) * (MAX_HISTO_GREEDY - 1)); 1226 int do_greedy; 1227 if (!HistogramCombineStochastic(image_histo, &num_used, threshold_size, 1228 &do_greedy)) { 1229 goto Error; 1230 } 1231 if (do_greedy) { 1232 RemoveEmptyHistograms(image_histo); 1233 if (!HistogramCombineGreedy(image_histo, &num_used)) { 1234 goto Error; 1235 } 1236 } 1237 } 1238 1239 // Find the optimal map from original histograms to the final ones. 1240 RemoveEmptyHistograms(image_histo); 1241 HistogramRemap(orig_histo, image_histo, histogram_symbols); 1242 1243 ok = 1; 1244 1245 Error: 1246 VP8LFreeHistogramSet(orig_histo); 1247 WebPSafeFree(map_tmp); 1248 return ok; 1249 } 1250
