zlib/algorithm.doc

1. Compression algorithm (deflate)

The deflation algorithm used by zlib (also zip and gzip) 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 longer match is emitted afterwards.  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)

The real question is, given a Huffman tree, how to decode fast.  The most
important realization 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 set it
for the maximum speed.

inflate() sends new trees relatively often, so it is possibly set for a
smaller first level table than an application that has only one tree for
all the data.  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.  It would be
interesting to see if optimizing the first level table for other
applications gave values within a bit or two of the flat code size.


Jean-loup Gailly        Mark Adler
gzip@prep.ai.mit.edu    madler@alumni.caltech.edu


References:

[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
ftp://ds.internic.net/rfc/rfc1951.txt