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In computing, Deflate (stylized as DEFLATE) is a lossless data compression file format that uses a combination of LZ77 and Huffman coding. It was designed by Phil Katz, for version 2 of his PKZIP archiving tool. Deflate was later specified in RFC 1951 (1996).[1]

Katz also designed the original algorithm used to construct Deflate streams. This algorithm was patented as U.S. Patent 5,051,745, and assigned to PKWARE, Inc.[2][3] As stated in the RFC document, an algorithm producing Deflate files was widely thought to be implementable in a manner not covered by patents.[1] This led to its widespread use – for example, in gzip compressed files and PNG image files, in addition to the ZIP file format for which Katz originally designed it. The patent has since expired.

Stream format

A Deflate stream consists of a series of blocks. Each block is preceded by a 3-bit header:

The stored block option adds minimal overhead and is used for data that is incompressible.

Most compressible data will end up being encoded using method 10, the dynamic Huffman encoding, which produces an optimized Huffman tree customized for each block of data individually. Instructions to generate the necessary Huffman tree immediately follow the block header. The static Huffman option is used for short messages, where the fixed saving gained by omitting the tree outweighs the percentage compression loss due to using a non-optimal (thus, not technically Huffman) code.

Compression is achieved through two steps:


Duplicate string elimination

Main articles: LZ77 and LZ78 and LZSS

Within compressed blocks, if a duplicate series of bytes is spotted (a repeated string), then a back-reference is inserted, linking to the previous location of that identical string instead. An encoded match to an earlier string consists of an 8-bit length (3–258 bytes) and a 15-bit distance (1–32,768 bytes) to the beginning of the duplicate. Relative back-references can be made across any number of blocks, as long as the distance appears within the last 32 KiB of uncompressed data decoded (termed the sliding window).

If the distance is less than the length, the duplicate overlaps itself, indicating repetition. For example, a run of 10 identical bytes can be encoded as one byte, followed by a duplicate of length 9, beginning with the previous byte.

Searching the preceding text for duplicate substrings is the most computationally expensive part of the DEFLATE algorithm, and the operation which compression level settings affect.


Bit reduction

Main article: Huffman coding

The second compression stage consists of replacing commonly used symbols with shorter representations and less commonly used symbols with longer representations. The method used is Huffman coding which creates an unprefixed tree of non-overlapping intervals, where the length of each sequence is inversely proportional to the logarithm of the probability of that symbol needing to be encoded. The more likely it is that a symbol has to be encoded, the shorter its bit-sequence will be.

A tree is created, containing space for 288 symbols:

A match length code will always be followed by a distance code. Based on the distance code read, further "extra" bits may be read in order to produce the final distance. The distance tree contains space for 32 symbols:

Note that for the match distance symbols 2–29, the number of extra bits can be calculated as .

The two codes (the 288-symbol length/literal tree and the 32-symbol distance tree) are themselves encoded as canonical Huffman codes by giving the bit length of the code for each symbol. The bit lengths are themselves run-length encoded to produce as compact a representation as possible. As an alternative to including the tree representation, the "static tree" option provides standard fixed Huffman trees. The compressed size using the static trees can be computed using the same statistics (the number of times each symbol appears) as are used to generate the dynamic trees, so it is easy for a compressor to choose whichever is smaller.

Encoder/compressor

During the compression stage, it is the encoder that chooses the amount of time spent looking for matching strings. The zlib/gzip reference implementation allows the user to select from a sliding scale of likely resulting compression-level vs. speed of encoding. Options range from 0 (do not attempt compression, just store uncompressed) to 9 representing the maximum capability of the reference implementation in zlib/gzip.

Other Deflate encoders have been produced, all of which will also produce a compatible bitstream capable of being decompressed by any existing Deflate decoder. Differing implementations will likely produce variations on the final encoded bit-stream produced. The focus with non-zlib versions of an encoder has normally been to produce a more efficiently compressed and smaller encoded stream.

Deflate64/Enhanced Deflate

Deflate64, specified by PKWARE, is a proprietary variant of Deflate. It's fundamentally the same algorithm. What has changed is the increase in dictionary size from 32 KB to 64 KB, an extension of the distance codes to 16 bits so that they may address a range of 64 KB, and the length code, which is extended to 16 bits so that it may define lengths of three to 65,538 bytes.[4] This leads to Deflate64 having a longer compression time, and potentially a slightly higher compression ratio, than Deflate.[5] Several free and/or open source projects support Deflate64, such as 7-Zip,[6] while others, such as zlib, do not, as a result of the proprietary nature of the procedure[7] and the very modest performance increase over Deflate.[8]

Using Deflate in new software

Implementations of Deflate are freely available in many languages. Apps written in C typically use the zlib library (under the permissive zlib License). Apps in Borland Pascal and (compatible languages) can use paszlib. Apps in C++ can take advantage of the improved Deflate library in 7-Zip. Both Java and .NET Framework offer out-of-the-box support for Deflate in their libraries (respectively, java.util.zip and System.IO.Compression). Apps in Ada can use Zip-Ada (pure) or ZLib-Ada.

Encoder implementations

AdvanceCOMP uses the higher compression ratio version of Deflate as implemented by 7-Zip (or optionally Zopfli in recent versions) to enable recompression of gzip, PNG, MNG and ZIP files with the possibility of smaller file sizes than zlib is able to achieve at maximum settings.

Hardware encoders

Decoder/decompressor

Inflate is the decoding process that takes a Deflate bitstream for decompression and correctly produces the original full-size data or file.

Inflate-only implementations

The normal intent with an alternative Inflate implementation is highly optimized decoding speed, or extremely predictable RAM usage for micro-controller embedded systems.

Hardware decoders

See also

References

  1. ^ a b Deutsch, L. Peter (May 1996). DEFLATE Compressed Data Format Specification version 1.3. IETF. p. 1. sec. Abstract. doi:10.17487/RFC1951. RFC 1951. Retrieved 2014-04-23.
  2. ^ US patent 5051745, Katz, Phillip W., "String Searcher, and Compressor Using Same", published 1991-09-24, issued 1991-09-24, assigned to PKWare Inc. 
  3. ^ David, Salomon (2007). Data Compression: The Complete Reference (4 ed.). Springer. p. 241. ISBN 978-1-84628-602-5.
  4. ^ "Binary Essence – Deflate64". Archived from the original on 21 June 2017. Retrieved 22 May 2011.((cite web)): CS1 maint: bot: original URL status unknown (link)
  5. ^ "Binary Essence – "Calgary Corpus" compression comparisons". Archived from the original on 27 December 2017. Retrieved 22 May 2011.((cite web)): CS1 maint: bot: original URL status unknown (link)
  6. ^ 7-Zip Manual and Documentation – compression Method
  7. ^ History of Lossless Data Compression Algorithms – Deflate64
  8. ^ zlib FAQ – Does zlib support the new "Deflate64" format introduced by PKWare?
  9. ^ "Plan 9 from Bell Labs's /n/sources/plan9/sys/src/libflate". plan9.bell-labs.com. Lucent Technologies. Archived from the original on 2006-03-15.
  10. ^ "High Performance DEFLATE Compression with Optimizations for Genomic Data Sets". Intel Software. 1 October 2019. Retrieved 18 January 2020.
  11. ^ "Intel® Xeon® Processor E5-2600 and E5-2400 Series with Intel® Communications Chipset 89xx Series". Retrieved 2016-05-18.
  12. ^ a b "Introducing the IBM z15 - The enterprise platform for mission-critical hybrid multicloud". Retrieved 2021-11-01.
  13. ^ a b Lascu, Octavian (28 April 2021). IBM z15 (8562) Technical Guide, Page 97. ISBN 9780738458991. Retrieved 2021-11-01.
  14. ^ a b "Data compression by using the zlibNX library - IBM Documentation". Retrieved 2021-11-01.
  15. ^ a b "Exploitation of In-Core Acceleration of POWER Processors for AIX". Retrieved 2021-11-01.