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+1. Compression algorithm (deflate)
+The deflation algorithm used by gzip (also zip and zlib) is a variation of
+LZ77 (Lempel-Ziv 1977, see reference below). It finds duplicated strings in
+the input data. The second occurrence of a string is replaced by a
+pointer to the previous string, in the form of a pair (distance,
+length). Distances are limited to 32K bytes, and lengths are limited
+to 258 bytes. When a string does not occur anywhere in the previous
+32K bytes, it is emitted as a sequence of literal bytes. (In this
+description, `string' must be taken as an arbitrary sequence of bytes,
+and is not restricted to printable characters.)
+Literals or match lengths are compressed with one Huffman tree, and
+match distances are compressed with another tree. The trees are stored
+in a compact form at the start of each block. The blocks can have any
+size (except that the compressed data for one block must fit in
+available memory). A block is terminated when deflate() determines that
+it would be useful to start another block with fresh trees. (This is
+somewhat similar to the behavior of LZW-based _compress_.)
+Duplicated strings are found using a hash table. All input strings of
+length 3 are inserted in the hash table. A hash index is computed for
+the next 3 bytes. If the hash chain for this index is not empty, all
+strings in the chain are compared with the current input string, and
+the longest match is selected.
+The hash chains are searched starting with the most recent strings, to
+favor small distances and thus take advantage of the Huffman encoding.
+The hash chains are singly linked. There are no deletions from the
+hash chains, the algorithm simply discards matches that are too old.
+To avoid a worst-case situation, very long hash chains are arbitrarily
+truncated at a certain length, determined by a runtime option (level
+parameter of deflateInit). So deflate() does not always find the longest
+possible match but generally finds a match which is long enough.
+deflate() also defers the selection of matches with a lazy evaluation
+mechanism. After a match of length N has been found, deflate() searches for
+a longer match at the next input byte. If a longer match is found, the
+previous match is truncated to a length of one (thus producing a single
+literal byte) and the process of lazy evaluation begins again. Otherwise,
+the original match is kept, and the next match search is attempted only N
+steps later.
+The lazy match evaluation is also subject to a runtime parameter. If
+the current match is long enough, deflate() reduces the search for a longer
+match, thus speeding up the whole process. If compression ratio is more
+important than speed, deflate() attempts a complete second search even if
+the first match is already long enough.
+The lazy match evaluation is not performed for the fastest compression
+modes (level parameter 1 to 3). For these fast modes, new strings
+are inserted in the hash table only when no match was found, or
+when the match is not too long. This degrades the compression ratio
+but saves time since there are both fewer insertions and fewer searches.
+2. Decompression algorithm (inflate)
+2.1 Introduction
+The key question is how to represent a Huffman code (or any prefix code) so
+that you can decode fast. The most important characteristic is that shorter
+codes are much more common than longer codes, so pay attention to decoding the
+short codes fast, and let the long codes take longer to decode.
+inflate() sets up a first level table that covers some number of bits of
+input less than the length of longest code. It gets that many bits from the
+stream, and looks it up in the table. The table will tell if the next
+code is that many bits or less and how many, and if it is, it will tell
+the value, else it will point to the next level table for which inflate()
+grabs more bits and tries to decode a longer code.
+How many bits to make the first lookup is a tradeoff between the time it
+takes to decode and the time it takes to build the table. If building the
+table took no time (and if you had infinite memory), then there would only
+be a first level table to cover all the way to the longest code. However,
+building the table ends up taking a lot longer for more bits since short
+codes are replicated many times in such a table. What inflate() does is
+simply to make the number of bits in the first table a variable, and then
+to set that variable for the maximum speed.
+For inflate, which has 286 possible codes for the literal/length tree, the size
+of the first table is nine bits. Also the distance trees have 30 possible
+values, and the size of the first table is six bits. Note that for each of
+those cases, the table ended up one bit longer than the ``average'' code
+length, i.e. the code length of an approximately flat code which would be a
+little more than eight bits for 286 symbols and a little less than five bits
+for 30 symbols.
+2.2 More details on the inflate table lookup
+Ok, you want to know what this cleverly obfuscated inflate tree actually
+looks like. You are correct that it's not a Huffman tree. It is simply a
+lookup table for the first, let's say, nine bits of a Huffman symbol. The
+symbol could be as short as one bit or as long as 15 bits. If a particular
+symbol is shorter than nine bits, then that symbol's translation is duplicated
+in all those entries that start with that symbol's bits. For example, if the
+symbol is four bits, then it's duplicated 32 times in a nine-bit table. If a
+symbol is nine bits long, it appears in the table once.
+If the symbol is longer than nine bits, then that entry in the table points
+to another similar table for the remaining bits. Again, there are duplicated
+entries as needed. The idea is that most of the time the symbol will be short
+and there will only be one table look up. (That's whole idea behind data
+compression in the first place.) For the less frequent long symbols, there
+will be two lookups. If you had a compression method with really long
+symbols, you could have as many levels of lookups as is efficient. For
+inflate, two is enough.
+So a table entry either points to another table (in which case nine bits in
+the above example are gobbled), or it contains the translation for the symbol
+and the number of bits to gobble. Then you start again with the next
+ungobbled bit.
+You may wonder: why not just have one lookup table for how ever many bits the
+longest symbol is? The reason is that if you do that, you end up spending
+more time filling in duplicate symbol entries than you do actually decoding.
+At least for deflate's output that generates new trees every several 10's of
+kbytes. You can imagine that filling in a 2^15 entry table for a 15-bit code
+would take too long if you're only decoding several thousand symbols. At the
+other extreme, you could make a new table for every bit in the code. In fact,
+that's essentially a Huffman tree. But then you spend two much time
+traversing the tree while decoding, even for short symbols.
+So the number of bits for the first lookup table is a trade of the time to
+fill out the table vs. the time spent looking at the second level and above of
+the table.
+Here is an example, scaled down:
+The code being decoded, with 10 symbols, from 1 to 6 bits long:
+A: 0
+B: 10
+C: 1100
+D: 11010
+E: 11011
+F: 11100
+G: 11101
+H: 11110
+I: 111110
+J: 111111
+Let's make the first table three bits long (eight entries):
+000: A,1
+001: A,1
+010: A,1
+011: A,1
+100: B,2
+101: B,2
+110: -> table X (gobble 3 bits)
+111: -> table Y (gobble 3 bits)
+Each entry is what the bits decode as and how many bits that is, i.e. how
+many bits to gobble. Or the entry points to another table, with the number of
+bits to gobble implicit in the size of the table.
+Table X is two bits long since the longest code starting with 110 is five bits
+00: C,1
+01: C,1
+10: D,2
+11: E,2
+Table Y is three bits long since the longest code starting with 111 is six
+bits long:
+000: F,2
+001: F,2
+010: G,2
+011: G,2
+100: H,2
+101: H,2
+110: I,3
+111: J,3
+So what we have here are three tables with a total of 20 entries that had to
+be constructed. That's compared to 64 entries for a single table. Or
+compared to 16 entries for a Huffman tree (six two entry tables and one four
+entry table). Assuming that the code ideally represents the probability of
+the symbols, it takes on the average 1.25 lookups per symbol. That's compared
+to one lookup for the single table, or 1.66 lookups per symbol for the
+Huffman tree.
+There, I think that gives you a picture of what's going on. For inflate, the
+meaning of a particular symbol is often more than just a letter. It can be a
+byte (a "literal"), or it can be either a length or a distance which
+indicates a base value and a number of bits to fetch after the code that is
+added to the base value. Or it might be the special end-of-block code. The
+data structures created in inftrees.c try to encode all that information
+compactly in the tables.
+Jean-loup Gailly Mark Adler
+jloup@gzip.org madler@alumni.caltech.edu
+[LZ77] Ziv J., Lempel A., ``A Universal Algorithm for Sequential Data
+Compression,'' IEEE Transactions on Information Theory, Vol. 23, No. 3,
+pp. 337-343.
+``DEFLATE Compressed Data Format Specification'' available in