annotate Modules/zlib/algorithm.txt @ 87690:66c1c9f32567 default tip

Add use_weakref option to functools.lru_cache The use_weakref option makes lru_cache create weak references to the function arguments when creating the cache key, which prevents them from staying in the memory forever even after all other references to them are gone. This is a proof of concept for http://bugs.python.org/issue19859
author Radomir Dopieralski <devel@sheep.art.pl>
date Tue, 03 Dec 2013 16:02:48 +0100
parents 224aa49f22f1
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 1. Compression algorithm (deflate)
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2
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3 The deflation algorithm used by gzip (also zip and zlib) is a variation of
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4 LZ77 (Lempel-Ziv 1977, see reference below). It finds duplicated strings in
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5 the input data. The second occurrence of a string is replaced by a
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6 pointer to the previous string, in the form of a pair (distance,
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7 length). Distances are limited to 32K bytes, and lengths are limited
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8 to 258 bytes. When a string does not occur anywhere in the previous
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9 32K bytes, it is emitted as a sequence of literal bytes. (In this
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10 description, `string' must be taken as an arbitrary sequence of bytes,
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11 and is not restricted to printable characters.)
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12
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13 Literals or match lengths are compressed with one Huffman tree, and
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14 match distances are compressed with another tree. The trees are stored
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15 in a compact form at the start of each block. The blocks can have any
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16 size (except that the compressed data for one block must fit in
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17 available memory). A block is terminated when deflate() determines that
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18 it would be useful to start another block with fresh trees. (This is
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19 somewhat similar to the behavior of LZW-based _compress_.)
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20
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21 Duplicated strings are found using a hash table. All input strings of
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22 length 3 are inserted in the hash table. A hash index is computed for
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23 the next 3 bytes. If the hash chain for this index is not empty, all
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24 strings in the chain are compared with the current input string, and
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25 the longest match is selected.
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26
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27 The hash chains are searched starting with the most recent strings, to
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28 favor small distances and thus take advantage of the Huffman encoding.
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29 The hash chains are singly linked. There are no deletions from the
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30 hash chains, the algorithm simply discards matches that are too old.
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31
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32 To avoid a worst-case situation, very long hash chains are arbitrarily
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33 truncated at a certain length, determined by a runtime option (level
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34 parameter of deflateInit). So deflate() does not always find the longest
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35 possible match but generally finds a match which is long enough.
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36
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37 deflate() also defers the selection of matches with a lazy evaluation
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38 mechanism. After a match of length N has been found, deflate() searches for
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39 a longer match at the next input byte. If a longer match is found, the
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40 previous match is truncated to a length of one (thus producing a single
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41 literal byte) and the process of lazy evaluation begins again. Otherwise,
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42 the original match is kept, and the next match search is attempted only N
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43 steps later.
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44
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45 The lazy match evaluation is also subject to a runtime parameter. If
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46 the current match is long enough, deflate() reduces the search for a longer
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47 match, thus speeding up the whole process. If compression ratio is more
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48 important than speed, deflate() attempts a complete second search even if
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49 the first match is already long enough.
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50
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51 The lazy match evaluation is not performed for the fastest compression
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52 modes (level parameter 1 to 3). For these fast modes, new strings
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53 are inserted in the hash table only when no match was found, or
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54 when the match is not too long. This degrades the compression ratio
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55 but saves time since there are both fewer insertions and fewer searches.
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56
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57
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58 2. Decompression algorithm (inflate)
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59
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60 2.1 Introduction
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61
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62 The key question is how to represent a Huffman code (or any prefix code) so
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63 that you can decode fast. The most important characteristic is that shorter
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64 codes are much more common than longer codes, so pay attention to decoding the
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65 short codes fast, and let the long codes take longer to decode.
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66
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67 inflate() sets up a first level table that covers some number of bits of
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68 input less than the length of longest code. It gets that many bits from the
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69 stream, and looks it up in the table. The table will tell if the next
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70 code is that many bits or less and how many, and if it is, it will tell
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71 the value, else it will point to the next level table for which inflate()
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72 grabs more bits and tries to decode a longer code.
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73
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74 How many bits to make the first lookup is a tradeoff between the time it
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75 takes to decode and the time it takes to build the table. If building the
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76 table took no time (and if you had infinite memory), then there would only
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77 be a first level table to cover all the way to the longest code. However,
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78 building the table ends up taking a lot longer for more bits since short
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79 codes are replicated many times in such a table. What inflate() does is
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80 simply to make the number of bits in the first table a variable, and then
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81 to set that variable for the maximum speed.
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82
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83 For inflate, which has 286 possible codes for the literal/length tree, the size
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84 of the first table is nine bits. Also the distance trees have 30 possible
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85 values, and the size of the first table is six bits. Note that for each of
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86 those cases, the table ended up one bit longer than the ``average'' code
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87 length, i.e. the code length of an approximately flat code which would be a
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88 little more than eight bits for 286 symbols and a little less than five bits
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89 for 30 symbols.
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90
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91
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92 2.2 More details on the inflate table lookup
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93
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94 Ok, you want to know what this cleverly obfuscated inflate tree actually
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95 looks like. You are correct that it's not a Huffman tree. It is simply a
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96 lookup table for the first, let's say, nine bits of a Huffman symbol. The
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97 symbol could be as short as one bit or as long as 15 bits. If a particular
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98 symbol is shorter than nine bits, then that symbol's translation is duplicated
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99 in all those entries that start with that symbol's bits. For example, if the
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100 symbol is four bits, then it's duplicated 32 times in a nine-bit table. If a
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101 symbol is nine bits long, it appears in the table once.
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102
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103 If the symbol is longer than nine bits, then that entry in the table points
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104 to another similar table for the remaining bits. Again, there are duplicated
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105 entries as needed. The idea is that most of the time the symbol will be short
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106 and there will only be one table look up. (That's whole idea behind data
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107 compression in the first place.) For the less frequent long symbols, there
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108 will be two lookups. If you had a compression method with really long
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109 symbols, you could have as many levels of lookups as is efficient. For
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110 inflate, two is enough.
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111
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112 So a table entry either points to another table (in which case nine bits in
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113 the above example are gobbled), or it contains the translation for the symbol
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114 and the number of bits to gobble. Then you start again with the next
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115 ungobbled bit.
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116
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117 You may wonder: why not just have one lookup table for how ever many bits the
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118 longest symbol is? The reason is that if you do that, you end up spending
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119 more time filling in duplicate symbol entries than you do actually decoding.
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120 At least for deflate's output that generates new trees every several 10's of
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121 kbytes. You can imagine that filling in a 2^15 entry table for a 15-bit code
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122 would take too long if you're only decoding several thousand symbols. At the
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123 other extreme, you could make a new table for every bit in the code. In fact,
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124 that's essentially a Huffman tree. But then you spend too much time
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125 traversing the tree while decoding, even for short symbols.
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126
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127 So the number of bits for the first lookup table is a trade of the time to
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128 fill out the table vs. the time spent looking at the second level and above of
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129 the table.
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130
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131 Here is an example, scaled down:
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132
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133 The code being decoded, with 10 symbols, from 1 to 6 bits long:
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134
81fa47d2d477 Import zlib into Python tree.
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135 A: 0
81fa47d2d477 Import zlib into Python tree.
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136 B: 10
81fa47d2d477 Import zlib into Python tree.
Martin v. Löwis <martin@v.loewis.de>
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137 C: 1100
81fa47d2d477 Import zlib into Python tree.
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138 D: 11010
81fa47d2d477 Import zlib into Python tree.
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139 E: 11011
81fa47d2d477 Import zlib into Python tree.
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140 F: 11100
81fa47d2d477 Import zlib into Python tree.
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141 G: 11101
81fa47d2d477 Import zlib into Python tree.
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142 H: 11110
81fa47d2d477 Import zlib into Python tree.
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143 I: 111110
81fa47d2d477 Import zlib into Python tree.
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144 J: 111111
81fa47d2d477 Import zlib into Python tree.
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145
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146 Let's make the first table three bits long (eight entries):
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147
81fa47d2d477 Import zlib into Python tree.
Martin v. Löwis <martin@v.loewis.de>
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148 000: A,1
81fa47d2d477 Import zlib into Python tree.
Martin v. Löwis <martin@v.loewis.de>
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149 001: A,1
81fa47d2d477 Import zlib into Python tree.
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150 010: A,1
81fa47d2d477 Import zlib into Python tree.
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151 011: A,1
81fa47d2d477 Import zlib into Python tree.
Martin v. Löwis <martin@v.loewis.de>
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152 100: B,2
81fa47d2d477 Import zlib into Python tree.
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153 101: B,2
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154 110: -> table X (gobble 3 bits)
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155 111: -> table Y (gobble 3 bits)
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156
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157 Each entry is what the bits decode as and how many bits that is, i.e. how
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158 many bits to gobble. Or the entry points to another table, with the number of
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159 bits to gobble implicit in the size of the table.
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160
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161 Table X is two bits long since the longest code starting with 110 is five bits
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162 long:
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163
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164 00: C,1
81fa47d2d477 Import zlib into Python tree.
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165 01: C,1
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166 10: D,2
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167 11: E,2
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168
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169 Table Y is three bits long since the longest code starting with 111 is six
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170 bits long:
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171
81fa47d2d477 Import zlib into Python tree.
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172 000: F,2
81fa47d2d477 Import zlib into Python tree.
Martin v. Löwis <martin@v.loewis.de>
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173 001: F,2
81fa47d2d477 Import zlib into Python tree.
Martin v. Löwis <martin@v.loewis.de>
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174 010: G,2
81fa47d2d477 Import zlib into Python tree.
Martin v. Löwis <martin@v.loewis.de>
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175 011: G,2
81fa47d2d477 Import zlib into Python tree.
Martin v. Löwis <martin@v.loewis.de>
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176 100: H,2
81fa47d2d477 Import zlib into Python tree.
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177 101: H,2
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178 110: I,3
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179 111: J,3
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180
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181 So what we have here are three tables with a total of 20 entries that had to
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182 be constructed. That's compared to 64 entries for a single table. Or
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183 compared to 16 entries for a Huffman tree (six two entry tables and one four
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184 entry table). Assuming that the code ideally represents the probability of
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185 the symbols, it takes on the average 1.25 lookups per symbol. That's compared
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186 to one lookup for the single table, or 1.66 lookups per symbol for the
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187 Huffman tree.
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188
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189 There, I think that gives you a picture of what's going on. For inflate, the
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190 meaning of a particular symbol is often more than just a letter. It can be a
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191 byte (a "literal"), or it can be either a length or a distance which
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192 indicates a base value and a number of bits to fetch after the code that is
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193 added to the base value. Or it might be the special end-of-block code. The
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194 data structures created in inftrees.c try to encode all that information
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195 compactly in the tables.
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196
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197
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198 Jean-loup Gailly Mark Adler
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199 jloup@gzip.org madler@alumni.caltech.edu
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200
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201
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202 References:
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203
81fa47d2d477 Import zlib into Python tree.
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204 [LZ77] Ziv J., Lempel A., ``A Universal Algorithm for Sequential Data
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205 Compression,'' IEEE Transactions on Information Theory, Vol. 23, No. 3,
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206 pp. 337-343.
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207
81fa47d2d477 Import zlib into Python tree.
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208 ``DEFLATE Compressed Data Format Specification'' available in
86546
224aa49f22f1 Issue #18527: Upgrade internal copy of zlib to 1.2.8
Christian Heimes <christian@cheimes.de>
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209 http://tools.ietf.org/html/rfc1951