Golomb coding: Difference between revisions

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{{distinguish|Exponential-Golomb coding}}

{{Short description|Lossless data compression method}}

{{More citations needed|section|date=October 2024|talk=Many sections unsourced}}

'''Golomb coding''' is a [[lossless data compression]] method using a family of [[data compression]] codes invented by [[Solomon W. &nbsp;Golomb]] in the 1960s. Alphabets following a [[geometric distribution]] will have a Golomb code as an optimal [[prefix code]],<ref>{{Cite journal | last1 = Gallager | first1 = R. G. |last2 = van Voorhis |first2 = D. C.| title = Optimal source codes for geometrically distributed integer alphabets | journal = [[IEEE Transactions on Information Theory]]| volume = 21 | issue = 2 | pages = 228–230 | year = 1975 | doi=10.1109/tit.1975.1055357}}</ref> making Golomb coding highly suitable for situations in which the occurrence of small values in the input stream is significantly more likely than large values.

== Rice coding ==

'''Rice coding''' (invented by [[Robert F. Rice]]) denotes using a subset of the family of Golomb codes to produce a simpler (but possibly suboptimal) prefix code. Rice used this set of codes in an [[adaptive coding]] scheme; "Rice coding" can refer either to that adaptive scheme or to using that subset of Golomb codes. Whereas a Golomb code has a tunable parameter that can be any positive integer value, Rice codes are those in which the tunable parameter is a power of two. This makes Rice codes convenient for use on a computer, since multiplication and division by 2 can be implemented more efficiently in [[binary arithmetic]].

Rice was motivated to propose this simpler subset due to the fact that geometric distributions are often varying with time, not precisely known, or both, so selecting the seemingly optimal code might not be very advantageous.

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Golomb coding uses a tunable parameter {{mvar|M}} to divide an input value {{mvar|x}} into two parts: {{mvar|q}}, the result of a division by {{mvar|M}}, and {{mvar|r}}, the remainder. The quotient is sent in [[unary coding]], followed by the remainder in [[truncated binary encoding]]. When <math>M=1</math>, Golomb coding is equivalent to unary coding.

Golomb–Rice codes can be thought of as codes that indicate a number by the position of the ''bin'' ({{mvar|q}}), and the ''offset'' within the ''bin'' ({{mvar|r}}). The example figure shows the position {{mvar|q}} and offset {{mvar|r}} for the encoding of integer {{mvar|x}} using Golomb–Rice parameter {{math|''M'' {{=}} 3}}, with source probabilities following a geometric distribution with {{math|''p''(0) {{=}} 0.2}}.

Formally, the two parts are given by the following expression, where {{mvar|x}} is the nonnegative integer being encoded:

:<math display="block">q = \left \lfloor \frac{x}{M} \right \rfloor</math>

and

:<math display="block">r = x - qM.</math>.

[[File:GolombCodeRedundancy.svg|thumb|upright 1.5|This image shows the redundancy, in bits, of the Golomb code, when {{mvar|M}} is chosen optimally, for {{math| 1 − ''p''(0) &ge; 0.45}}]]

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The integer {{mvar|x}} treated by Golomb was the run length of a [[Bernoulli process]], which has a [[geometric distribution]] starting at 0. The best choice of parameter {{mvar|M}} is a function of the corresponding Bernoulli process, which is parameterized by <math>p = P(x=0)</math> the probability of success in a given [[Bernoulli trial]]. {{mvar|M}} is either the median of the distribution or the median ±1. It can be determined by these inequalities:

: <math display="block">(1-p)^M + (1-p)^{M+1} \leq 1 < (1-p)^{M-1} + (1-p)^M,</math>

which are solved by

: <math display="block">M = \left\lceil -\frac{\log(2 -p)}{\log(1-p)}\right\rceil.</math>.

For the example with {{math|''p''(0) {{=}} 0.2}}:

: <math display="block">M = \left\lceil -\frac{\log(1.8)}{\log(0.8)}\right\rceil = \left\lceil 2.634 \right\rceil = 3.</math>.

The Golomb code for this distribution is equivalent to the [[Huffman code]] for the same probabilities, if it were possible to compute the Huffman code for the infinite set of source values.

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# Skip the 0 delimiter

# Let <math>b = \lfloor\log_2(M)\rfloor</math>

## Interpret next ''b'' bits as a binary number ''r'''. If <math>r' < 2^{b+1}-M</math> holds, then the reminderremainder <math> r = r' </math>

## Otherwise interpret ''b + 1'' bits as a binary number ''r''', the reminderremainder is given by <math>r = r' - 2^{b+1} + M</math>

# Compute <math>N = q * M + r</math>

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|}

For example, with a Rice–Golomb encoding using parameter {{math|''M'' {{=}} 10}}, the decimal number 42 would first be split into {{mvar|q}} = 4 and {{mvar|r}} = 2, and would be encoded as qcode({{mvar|q}}),rcode({{mvar|r}}) = qcode(4),rcode(2) = 11110,010 (you don't need to encode the separating comma in the output stream, because the 0 at the end of the {{mvar|q}} code is enough to say when {{mvar|q}} ends and {{mvar|r}} begins ; both the qcode and rcode are self-delimited).

== Use for run-length encoding ==

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:''Note that {{mvar|p}} and {{math|1 – p}} are reversed in this section compared to the use in earlier sections.''

Given an alphabet of two symbols, or a set of two events, ''P'' and ''Q'', with probabilities ''p'' and ({{math|1 &minus; ''p''}}) respectively, where {{math|''p'' ≥ 1/2}}, Golomb coding can be used to encode runs of zero or more ''P''&prime;s separated by single ''Q''&prime;s. In this application, the best setting of the parameter ''M'' is the nearest integer to <math>- \frac{1}{\log_{2}p}</math>. When ''p'' = 1/2, ''M'' = 1, and the Golomb code corresponds to unary ({{math|''n'' ≥ 0}} ''P''&prime;s followed by a ''Q'' is encoded as ''n'' ones followed by a zero). If a simpler code is desired, one can assign Golomb–Rice parameter {{mvar|b}} (i.e., Golomb parameter <math>M=2^b</math>) to the integer nearest to <math>- \log_2(-\log_2 p)</math>; although not always the best parameter, it is usually the best Rice parameter and its compression performance is quite close to the optimal Golomb code. (Rice himself proposed using various codes for the same data to figure out which was best. A later [[Jet Propulsion Laboratory|JPL]] researcher proposed various methods of optimizing or estimating the code parameter.<ref>{{Cite techreporttech report | last1 = Kiely | first1 = A. | title = Selecting the Golomb Parameter in Rice Coding | number = 42-159 | institution = [[Jet Propulsion Laboratory]] | year = 2004}}</ref>)

Consider using a Rice code with a binary portion having {{mvar|b}} bits to run-length encode sequences where ''P'' has a probability {{mvar|p}}. If <math>\mathbb{P}[\text{bit is part of }k\text{-run}]</math> is the probability that a bit will be part of an {{mvar|k}}-bit run (<math>k-1</math> ''P''s and one ''Q'') and <math>(\text{compression ratio of }k\text{-run})</math> is the compression ratio of that run, then the expected compression ratio is

<!-- below mostly comes from above reference (Kiely), but not exactly, so leave uncited for now -->

:<math display="block">\begin{align}

\mathbb{E}[\text{compression ratio}]

&= \sum_{k=1}^\infty (\text{compression ratio of }k\text{-run}) \cdot \mathbb{P}[\text{bit is part of }k\text{-run}] \\

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When a probability distribution for integers is not known, the optimal parameter for a Golomb–Rice encoder cannot be determined. Thus, in many applications, a two-pass approach is used: first, the block of data is scanned to estimate a probability density function (PDF) for the data. The Golomb–Rice parameter is then determined from that estimated PDF. A simpler variation of that approach is to assume that the PDF belongs to a parametrized family, estimate the PDF parameters from the data, and then compute the optimal Golomb–Rice parameter. That is the approach used in most of the applications discussed below.

An alternative approach to efficiently encode integer data whose PDF is not known, or is varying, is to use a backwards-adaptive encoder. The RLGR encoder [https://www.researchgatemicrosoft.netcom/en-us/research/publication/4230021_Adaptive_runadaptive-lengthGolomb-Rice_encoding_of_quantized_generalized_Gaussian_sources_with_unknown_statistics run-length Golomb–Rice (RLGR) code-golomb-rice-encoding-of-quantized-generalized-gaussian-sources-with-unknown-statistics/] achieves that using a very simple algorithm that adjusts the Golomb–Rice parameter up or down, depending on the last encoded symbol. A decoder can follow the same rule to track the variation of the encoding parameters, so no side information needs to be transmitted, just the encoded data. Assuming a generalized Gaussian PDF, which covers a wide range of statistics seen in data such as prediction errors or transform coefficients in multimedia codecs, the RLGR encoding algorithm can perform very well in such applications.

== Applications ==

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Rice coding is also used in the [[FELICS]] lossless image codec.

The Golomb–Rice coder is used in the entropy coding stage of [[Rice algorithm]] based ''lossless image codecs''. One such experiment yields the compression ratio graph shown.

The [[Lossless JPEG#JPEG-LS|JPEG-LS]] scheme uses Rice–Golomb to encode the prediction residuals.

The adaptive version of Golomb–Rice coding mentioned above, the RLGR encoder [https://www.researchgatemicrosoft.netcom/en-us/research/publication/4230021_Adaptive_runadaptive-lengthGolomb-Rice_encoding_of_quantized_generalized_Gaussian_sources_with_unknown_statistics run-length Golomb–Rice (RLGR)] adaptive version -golomb-rice-encoding-of Golomb–Rice coding-quantized-generalized-gaussian-sources-with-unknown-statistics/], mentioned above, is used for encoding screen content in virtual machines in the [https://msdn.microsoft.com/en-us/library/ff635165.aspx RemoteFX] component of the Microsoft Remote Desktop Protocol.

==See also==

* [[Elias delta coding]]

* [[Variable-length code]]

* [[Exponential-Golomb coding]]

== References ==

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* [[Solomon W. Golomb|Golomb, Solomon W.]] (1966). [http://urchin.earth.li/~twic/Golombs_Original_Paper/ Run-length encodings. IEEE Transactions on Information Theory, IT--12(3):399--401 ]

* {{cite journal | last1 = Rice | first1 = Robert F. | last2 = Plaunt | first2 = R. | date = 1971 | title = Adaptive Variable-Length Coding for Efficient Compression of Spacecraft Television Data | journal = IEEE Transactions on Communications | volume = 16 | issue = 9| pages = 889–897 | doi=10.1109/TCOM.1971.1090789}}

* Robert F. Rice (1979), "[https://ntrs.nasa.gov/search.jsp?R=19790014634 , "Some Practical Universal Noiseless Coding Techniques]", Jet Propulsion Laboratory, Pasadena, California, JPL Publication 79—22, March 1979.]

* Witten, Ian Moffat, Alistair Bell, Timothy. "Managing Gigabytes: Compressing and Indexing Documents and Images." Second Edition. Morgan Kaufmann Publishers, San Francisco CA. 1999 {{ISBN|1-55860-570-3}}

* David Salomon. "Data Compression",{{ISBN|0-387-95045-1}}.

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* [https://msdn.microsoft.com/en-us/library/ff635165.aspx RLGR Entropy Encoding], Microsoft MS-RDPRFX Open Specification, RemoteFX codec for Remote Desktop Protocol.

* S. Büttcher, C. L. A. Clarke, and G. V. Cormack. [http://www.ir.uwaterloo.ca/book/ Information Retrieval: Implementing and Evaluating Search Engines] {{Webarchive|url=https://web.archive.org/web/20201005195805/http://www.ir.uwaterloo.ca/book/ |date=2020-10-05 }}. MIT Press, Cambridge MA, 2010.

{{Compression Methods}}

{{DEFAULTSORT:Golomb Coding}}

[[Category:LosslessEntropy compression algorithmscoding]]

[[Category:Data compression]]