Mercurial > hgrepos > Python2 > PyMuPDF
comparison mupdf-source/thirdparty/brotli/c/enc/bit_cost_inc.h @ 2:b50eed0cc0ef upstream
ADD: MuPDF v1.26.7: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.4.
The directory name has changed: no version number in the expanded directory now.
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Mon, 15 Sep 2025 11:43:07 +0200 |
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| 1:1d09e1dec1d9 | 2:b50eed0cc0ef |
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| 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 */ | |
| 9 | |
| 10 #define HistogramType FN(Histogram) | |
| 11 | |
| 12 double FN(BrotliPopulationCost)(const HistogramType* histogram) { | |
| 13 static const double kOneSymbolHistogramCost = 12; | |
| 14 static const double kTwoSymbolHistogramCost = 20; | |
| 15 static const double kThreeSymbolHistogramCost = 28; | |
| 16 static const double kFourSymbolHistogramCost = 37; | |
| 17 const size_t data_size = FN(HistogramDataSize)(); | |
| 18 int count = 0; | |
| 19 size_t s[5]; | |
| 20 double bits = 0.0; | |
| 21 size_t i; | |
| 22 if (histogram->total_count_ == 0) { | |
| 23 return kOneSymbolHistogramCost; | |
| 24 } | |
| 25 for (i = 0; i < data_size; ++i) { | |
| 26 if (histogram->data_[i] > 0) { | |
| 27 s[count] = i; | |
| 28 ++count; | |
| 29 if (count > 4) break; | |
| 30 } | |
| 31 } | |
| 32 if (count == 1) { | |
| 33 return kOneSymbolHistogramCost; | |
| 34 } | |
| 35 if (count == 2) { | |
| 36 return (kTwoSymbolHistogramCost + (double)histogram->total_count_); | |
| 37 } | |
| 38 if (count == 3) { | |
| 39 const uint32_t histo0 = histogram->data_[s[0]]; | |
| 40 const uint32_t histo1 = histogram->data_[s[1]]; | |
| 41 const uint32_t histo2 = histogram->data_[s[2]]; | |
| 42 const uint32_t histomax = | |
| 43 BROTLI_MAX(uint32_t, histo0, BROTLI_MAX(uint32_t, histo1, histo2)); | |
| 44 return (kThreeSymbolHistogramCost + | |
| 45 2 * (histo0 + histo1 + histo2) - histomax); | |
| 46 } | |
| 47 if (count == 4) { | |
| 48 uint32_t histo[4]; | |
| 49 uint32_t h23; | |
| 50 uint32_t histomax; | |
| 51 for (i = 0; i < 4; ++i) { | |
| 52 histo[i] = histogram->data_[s[i]]; | |
| 53 } | |
| 54 /* Sort */ | |
| 55 for (i = 0; i < 4; ++i) { | |
| 56 size_t j; | |
| 57 for (j = i + 1; j < 4; ++j) { | |
| 58 if (histo[j] > histo[i]) { | |
| 59 BROTLI_SWAP(uint32_t, histo, j, i); | |
| 60 } | |
| 61 } | |
| 62 } | |
| 63 h23 = histo[2] + histo[3]; | |
| 64 histomax = BROTLI_MAX(uint32_t, h23, histo[0]); | |
| 65 return (kFourSymbolHistogramCost + | |
| 66 3 * h23 + 2 * (histo[0] + histo[1]) - histomax); | |
| 67 } | |
| 68 | |
| 69 { | |
| 70 /* In this loop we compute the entropy of the histogram and simultaneously | |
| 71 build a simplified histogram of the code length codes where we use the | |
| 72 zero repeat code 17, but we don't use the non-zero repeat code 16. */ | |
| 73 size_t max_depth = 1; | |
| 74 uint32_t depth_histo[BROTLI_CODE_LENGTH_CODES] = { 0 }; | |
| 75 const double log2total = FastLog2(histogram->total_count_); | |
| 76 for (i = 0; i < data_size;) { | |
| 77 if (histogram->data_[i] > 0) { | |
| 78 /* Compute -log2(P(symbol)) = -log2(count(symbol)/total_count) = | |
| 79 = log2(total_count) - log2(count(symbol)) */ | |
| 80 double log2p = log2total - FastLog2(histogram->data_[i]); | |
| 81 /* Approximate the bit depth by round(-log2(P(symbol))) */ | |
| 82 size_t depth = (size_t)(log2p + 0.5); | |
| 83 bits += histogram->data_[i] * log2p; | |
| 84 if (depth > 15) { | |
| 85 depth = 15; | |
| 86 } | |
| 87 if (depth > max_depth) { | |
| 88 max_depth = depth; | |
| 89 } | |
| 90 ++depth_histo[depth]; | |
| 91 ++i; | |
| 92 } else { | |
| 93 /* Compute the run length of zeros and add the appropriate number of 0 | |
| 94 and 17 code length codes to the code length code histogram. */ | |
| 95 uint32_t reps = 1; | |
| 96 size_t k; | |
| 97 for (k = i + 1; k < data_size && histogram->data_[k] == 0; ++k) { | |
| 98 ++reps; | |
| 99 } | |
| 100 i += reps; | |
| 101 if (i == data_size) { | |
| 102 /* Don't add any cost for the last zero run, since these are encoded | |
| 103 only implicitly. */ | |
| 104 break; | |
| 105 } | |
| 106 if (reps < 3) { | |
| 107 depth_histo[0] += reps; | |
| 108 } else { | |
| 109 reps -= 2; | |
| 110 while (reps > 0) { | |
| 111 ++depth_histo[BROTLI_REPEAT_ZERO_CODE_LENGTH]; | |
| 112 /* Add the 3 extra bits for the 17 code length code. */ | |
| 113 bits += 3; | |
| 114 reps >>= 3; | |
| 115 } | |
| 116 } | |
| 117 } | |
| 118 } | |
| 119 /* Add the estimated encoding cost of the code length code histogram. */ | |
| 120 bits += (double)(18 + 2 * max_depth); | |
| 121 /* Add the entropy of the code length code histogram. */ | |
| 122 bits += BitsEntropy(depth_histo, BROTLI_CODE_LENGTH_CODES); | |
| 123 } | |
| 124 return bits; | |
| 125 } | |
| 126 | |
| 127 #undef HistogramType |
