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43-430: (Redirected from Rans ) RANS or Rans may refer to: rANS , an entropy coding technique Rans, Jura , a commune in eastern France Rans (Penafiel) , a parish in the municipality of Penafiel , northern Portugal RANS Cilegon F.C. , an Indonesian football club Rans Designs , an aircraft, sail trike, land yacht and bicycle manufacturer, usually styled as "RANS" by

86-751: A cumulative distribution function: Note here that the CDF [ s ] function is not a true CDF in that the current symbol's probability is not included in the expression's value. Instead, the CDF [ s ] represents the total probability of all previous symbols. Example: Instead of the normal definition of CDF [ 0 ] = f [ 0 ] , it is evaluated as CDF [ 0 ] = 0 , since there are no previous symbols. For y ∈ [ 0 , 2 n − 1 ] {\displaystyle y\in [0,2^{n}-1]} denote function (usually tabled) Now coding function is: Decoding: s = symbol ( x & mask ) This way we can encode

129-411: A large alphabet. Intuitively, it divides the set of natural numbers into size 2 n {\displaystyle 2^{n}} ranges, and split each of them in identical way into subranges of proportions given by the assumed probability distribution. We start with quantization of probability distribution to 2 n {\displaystyle 2^{n}} denominator, where n

172-426: A patent application called "Features of range asymmetric number system encoding and decoding". The USPTO issued a final rejection of the application on October 27, 2020. Yet on March 2, 2021, Microsoft gave a USPTO explanatory filing stating "The Applicant respectfully disagrees with the rejections.", seeking to overturn the final rejection under the "After Final Consideration Pilot 2.0" program. After reconsideration,

215-431: A sequence of symbols into a large natural number x . To avoid using large number arithmetic, in practice stream variants are used: which enforce x ∈ [ L , b ⋅ L − 1 ] {\displaystyle x\in [L,b\cdot L-1]} by renormalization: sending the least significant bits of x to or from the bitstream (usually L and b are powers of 2). In rANS variant x

258-463: A sequence of symbols using approximately the Shannon entropy bits per symbol. For example, ANS could be directly used to enumerate combinations: assign a different natural number to every sequence of symbols having fixed proportions in a nearly optimal way. In contrast to encoding combinations, this probability distribution usually varies in data compressors. For this purpose, Shannon entropy can be seen as

301-530: A small denominator N {\displaystyle N} . Imagine there is some information stored in a natural number x {\displaystyle x} , for example as bit sequence of its binary expansion. To add information from a binary variable s {\displaystyle s} , we can use coding function x ′ = C ( x , s ) = 2 x + s {\displaystyle x'=C(x,s)=2x+s} , which shifts all bits one position up, and place

344-538: A source with 3 letters A, B, C, with probability 1/2, 1/4, 1/4. It is simple to construct the optimal prefix code in binary: A = 0, B = 10, C = 11. Then, a message is encoded as ABC -> 01011. We see that an equivalent method for performing the encoding is as follows: Consider a more general source with k letters, with rational probabilities n 1 / N , . . . , n k / N {\displaystyle n_{1}/N,...,n_{k}/N} . Then performing arithmetic coding on

387-552: A symbol of probability p {\displaystyle p} increases this informational content to log 2 ⁡ ( x ) + log 2 ⁡ ( 1 / p ) = log 2 ⁡ ( x / p ) {\displaystyle \log _{2}(x)+\log _{2}(1/p)=\log _{2}(x/p)} . Hence, the new number containing both information should be x ′ ≈ x / p {\displaystyle x'\approx x/p} . Consider

430-740: A table for small values of x {\displaystyle x} : The symbol s = 1 {\displaystyle s=1} corresponds to a subset of natural numbers with density p = 0.3 {\displaystyle p=0.3} , which in this case are positions { 0 , 3 , 6 , 10 , 13 , 16 , 20 , 23 , 26 , … } {\displaystyle \{0,3,6,10,13,16,20,23,26,\ldots \}} . As 1 / 4 < 0.3 < 1 / 3 {\displaystyle 1/4<0.3<1/3} , these positions increase by 3 or 4. Because p = 3 / 10 {\displaystyle p=3/10} here,

473-405: A table which yields a finite-state machine avoiding the need of multiplication. Finally, the step of the decoding loop can be written as: The step of the encoding loop: A specific tANS coding is determined by assigning a symbol to every [ L , 2 L − 1 ] {\displaystyle [L,2L-1]} position, their number of appearances should be proportional to

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516-506: A weighted average: a symbol of probability p {\displaystyle p} contains log 2 ⁡ ( 1 / p ) {\displaystyle \log _{2}(1/p)} bits of information. ANS encodes information into a single natural number x {\displaystyle x} , interpreted as containing log 2 ⁡ ( x ) {\displaystyle \log _{2}(x)} bits of information. Adding information from

559-472: Is called uABS and leads to the following decoding and encoding functions: Decoding: Encoding: For p = 1 / 2 {\displaystyle p=1/2} it amounts to the standard binary system (with 0 and 1 inverted), for a different p {\displaystyle p} it becomes optimal for this given probability distribution. For example, for p = 0.3 {\displaystyle p=0.3} these formulas lead to

602-410: Is chosen (usually 8-12 bits): p s ≈ f [ s ] / 2 n {\displaystyle p_{s}\approx f[s]/2^{n}} for some natural f [ s ] {\displaystyle f[s]} numbers (sizes of subranges). Denote mask = 2 n − 1 {\displaystyle {\text{mask}}=2^{n}-1} , and

645-956: Is different from Wikidata All article disambiguation pages All disambiguation pages Asymmetric numeral systems#Range variants (rANS) and streaming Among others, ANS is used in the Facebook Zstandard compressor (also used e.g. in Linux kernel, Google Chrome browser, Android operating system, was published as RFC 8478 for MIME and HTTP ), Apple LZFSE compressor, Google Draco 3D compressor (used e.g. in Pixar Universal Scene Description format ) and PIK image compressor, CRAM DNA compressor from SAMtools utilities, NVIDIA nvCOMP high speed compression library, Dropbox DivANS compressor, Microsoft DirectStorage BCPack texture compressor, and JPEG XL image compressor. The basic idea

688-473: Is for example 32 bit. For 16 bit renormalization, x ∈ [ 2 16 , 2 32 − 1 ] {\displaystyle x\in [2^{16},2^{32}-1]} , decoder refills the least significant bits from the bitstream when needed: tANS variant puts the entire behavior (including renormalization) for x ∈ [ L , 2 L − 1 ] {\displaystyle x\in [L,2L-1]} into

731-457: Is optimal for the uniform (symmetric) probability distribution of symbols Pr ( 0 ) = Pr ( 1 ) = 1 / 2 {\displaystyle \Pr(0)=\Pr(1)=1/2} . ANS generalize it to make it optimal for any chosen (asymmetric) probability distribution of symbols: Pr ( s ) = p s {\displaystyle \Pr(s)=p_{s}} . While s {\displaystyle s} in

774-531: Is optimal if Pr ( 0 ) = Pr ( 1 ) = 1 / 2 {\displaystyle \Pr(0)=\Pr(1)=1/2} . ANS generalizes this process for arbitrary sets of symbols s ∈ S {\displaystyle s\in S} with an accompanying probability distribution ( p s ) s ∈ S {\displaystyle (p_{s})_{s\in S}} . In ANS, if

817-402: Is still n {\displaystyle n} bits if p = 1 / 2 {\displaystyle p=1/2} , however, it can also be much smaller. For example, we need only ≈ n / 2 {\displaystyle \approx n/2} bits for p = 0.11 {\displaystyle p=0.11} . An entropy coder allows the encoding of

860-538: Is the number of bits of information stored in the number x {\displaystyle x} , and log 2 ⁡ ( 1 / p s ) {\displaystyle \log _{2}(1/p_{s})} is the number of bits contained in the symbol s {\displaystyle s} . For the encoding rule, the set of natural numbers is split into disjoint subsets corresponding to different symbols – like into even and odd numbers, but with densities corresponding to

903-570: Is to encode information into a single natural number x {\displaystyle x} . In the standard binary number system, we can add a bit s ∈ { 0 , 1 } {\displaystyle s\in \{0,1\}} of information to x {\displaystyle x} by appending s {\displaystyle s} at the end of x {\displaystyle x} , which gives us x ′ = 2 x + s {\displaystyle x'=2x+s} . For an entropy coder, this

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946-479: Is usually used as a faster replacement for range coding (e.g. CRAM , LZNA, Draco, ). It requires multiplication, but is more memory efficient and is appropriate for dynamically adapting probability distributions. Encoding and decoding of ANS are performed in opposite directions, making it a stack for symbols. This inconvenience is usually resolved by encoding in backward direction, after which decoding can be done forward. For context-dependence, like Markov model ,

989-433: The C {\displaystyle C} function on the successive bits of a finite bit sequence to obtain a final x {\displaystyle x} number storing this entire sequence. Then using D {\displaystyle D} function multiple times until x = 1 {\displaystyle x=1} allows one to retrieve the bit sequence in reversed order. The above procedure

1032-891: The x {\displaystyle x} -th appearance from such subset corresponding to symbol s {\displaystyle s} . The density assumption is equivalent to condition x ′ = C ( x , s ) ≈ x / p s {\displaystyle x'=C(x,s)\approx x/p_{s}} . Assuming that a natural number x {\displaystyle x} contains log 2 ⁡ ( x ) {\displaystyle \log _{2}(x)} bits of information, log 2 ⁡ ( C ( x , s ) ) ≈ log 2 ⁡ ( x ) + log 2 ⁡ ( 1 / p s ) {\displaystyle \log _{2}(C(x,s))\approx \log _{2}(x)+\log _{2}(1/p_{s})} . Hence

1075-758: The above example was choosing between even and odd C ( x , s ) {\displaystyle C(x,s)} , in ANS this even/odd division of natural numbers is replaced with division into subsets having densities corresponding to the assumed probability distribution { p s } s {\displaystyle \{p_{s}\}_{s}} : up to position x {\displaystyle x} , there are approximately x p s {\displaystyle xp_{s}} occurrences of symbol s {\displaystyle s} . The coding function C ( x , s ) {\displaystyle C(x,s)} returns

1118-408: The assumed probabilities. For example, one could choose "abdacdac" assignment for Pr(a)=3/8, Pr(b)=1/8, Pr(c)=2/8, Pr(d)=2/8 probability distribution. If symbols are assigned in ranges of lengths being powers of 2, we would get Huffman coding . For example, a->0, b->100, c->101, d->11 prefix code would be obtained for tANS with "aaaabcdd" symbol assignment. As for Huffman coding, modifying

1161-468: The bitstream. Suppose a sequence of 1,000 zeros and ones would be encoded, which would take 1000 bits to store directly. However, if it is somehow known that it only contains 1 zero and 999 ones, it would be sufficient to encode the zero's position, which requires only ⌈ log 2 ⁡ ( 1000 ) ⌉ ≈ 10 {\displaystyle \lceil \log _{2}(1000)\rceil \approx 10} bits here instead of

1204-488: The company Reynolds-averaged Navier–Stokes equations Topics referred to by the same term [REDACTED] This disambiguation page lists articles associated with the title RANS . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=RANS&oldid=1086566803 " Category : Disambiguation pages Hidden categories: Short description

1247-565: The decoding function D ( x ′ ) {\displaystyle D(x')} on this final x {\displaystyle x} , we can retrieve the symbol sequence. Using the table for this purpose, x {\displaystyle x} in the first row determines the column, then the non-empty row and the written value determine the corresponding s {\displaystyle s} and x {\displaystyle x} . The range variant also uses arithmetic formulas, but allows operation on

1290-457: The encoder needs to use context from the perspective of later decoding. For adaptivity, the encoder should first go forward to find probabilities which will be used (predicted) by decoder and store them in a buffer, then encode in backward direction using the buffered probabilities. The final state of encoding is required to start decoding, hence it needs to be stored in the compressed file. This cost can be compensated by storing some information in

1333-779: The information from s {\displaystyle s} is appended to x {\displaystyle x} to result in x ′ {\displaystyle x'} , then x ′ ≈ x ⋅ p s − 1 {\displaystyle x'\approx x\cdot p_{s}^{-1}} . Equivalently, log 2 ⁡ ( x ′ ) ≈ log 2 ⁡ ( x ) + log 2 ⁡ ( 1 / p s ) {\displaystyle \log _{2}(x')\approx \log _{2}(x)+\log _{2}(1/p_{s})} , where log 2 ⁡ ( x ) {\displaystyle \log _{2}(x)}

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1376-487: The initial state of encoder. For example, instead of starting with "10000" state, start with "1****" state, where "*" are some additional stored bits, which can be retrieved at the end of the decoding. Alternatively, this state can be used as a checksum by starting encoding with a fixed state, and testing if the final state of decoding is the expected one. The author of the novel ANS algorithm and its variants tANS and rANS specifically intended his work to be available freely in

1419-674: The middle to the top row. Imagine we would like to encode the sequence '0100' starting from x = 1 {\displaystyle x=1} . First s = 0 {\displaystyle s=0} takes us to x = 2 {\displaystyle x=2} , then s = 1 {\displaystyle s=1} to x = 6 {\displaystyle x=6} , then s = 0 {\displaystyle s=0} to x = 9 {\displaystyle x=9} , then s = 0 {\displaystyle s=0} to x = 14 {\displaystyle x=14} . By using

1462-773: The new bit in the least significant position. Now decoding function D ( x ′ ) = ( ⌊ x ′ / 2 ⌋ , m o d ( x ′ , 2 ) ) {\displaystyle D(x')=(\lfloor x'/2\rfloor ,\mathrm {mod} (x',2))} allows one to retrieve the previous x {\displaystyle x} and this added bit: D ( C ( x , s ) ) = ( x , s ) ,   C ( D ( x ′ ) ) = x ′ {\displaystyle D(C(x,s))=(x,s),\ C(D(x'))=x'} . We can start with x = 1 {\displaystyle x=1} initial state, then use

1505-648: The original 1000 bits. Generally, such sequences of length n {\displaystyle n} containing p n {\displaystyle pn} zeros and ( 1 − p ) n {\displaystyle (1-p)n} ones, for some probability p ∈ ( 0 , 1 ) {\displaystyle p\in (0,1)} , are called combinations . Using Stirling's approximation we get their asymptotic number being called Shannon entropy . Hence, to choose one such sequence we need approximately n h ( p ) {\displaystyle nh(p)} bits. It

1548-494: The original author assisting them. Duda was not pleased by (accidentally) discovering Google's patent intentions, given he had been clear he wanted it as public domain, and had assisted Google specifically on that basis. Duda subsequently filed a third-party application to the US Patent office seeking a rejection. The USPTO rejected its application in 2018, and Google subsequently abandoned the patent. In June 2019 Microsoft lodged

1591-542: The pattern of symbols repeats every 10 positions. The coding C ( x , s ) {\displaystyle C(x,s)} can be found by taking the row corresponding to a given symbol s {\displaystyle s} , and choosing the given x {\displaystyle x} in this row. Then the top row provides C ( x , s ) {\displaystyle C(x,s)} . For example, C ( 7 , 0 ) = 11 {\displaystyle C(7,0)=11} from

1634-525: The position of the x {\displaystyle x} -th appearance from the s {\displaystyle s} -th subset. There are alternative ways to apply it in practice – direct mathematical formulas for encoding and decoding steps (uABS and rANS variants), or one can put the entire behavior into a table (tANS variant). Renormalization is used to prevent x {\displaystyle x} going to infinity – transferring accumulated bits to or from

1677-415: The probability distribution of tANS is relatively costly, hence it is mainly used in static situations, usually with some Lempel–Ziv scheme (e.g. ZSTD, LZFSE). In this case, the file is divided into blocks - for each of them symbol frequencies are independently counted, then after approximation (quantization) written in the block header and used as static probability distribution for tANS. In contrast, rANS

1720-411: The probability distribution of the symbols to encode. Then to add information from symbol s {\displaystyle s} into the information already stored in the current number x {\displaystyle x} , we go to number x ′ = C ( x , s ) ≈ x / p {\displaystyle x'=C(x,s)\approx x/p} being

1763-437: The public domain, for altruistic reasons. He has not sought to profit from them and took steps to ensure they would not become a "legal minefield", or restricted by, or profited from by others. In 2015, Google published a US and then worldwide patent for "Mixed boolean-token ans coefficient coding". At the time, Professor Duda had been asked by Google to help it with video compression, so was intimately aware of this domain, having

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1806-445: The source requires only exact arithmetic with integers. In general, ANS is an approximation of arithmetic coding that approximates the real probabilities r 1 , . . . , r k {\displaystyle r_{1},...,r_{k}} by rational numbers n 1 / N , . . . , n k / N {\displaystyle n_{1}/N,...,n_{k}/N} with

1849-1233: The symbol of probability p s {\displaystyle p_{s}} is encoded as containing ≈ log 2 ⁡ ( 1 / p s ) {\displaystyle \approx \log _{2}(1/p_{s})} bits of information as it is required from entropy coders . Let us start with the binary alphabet and a probability distribution Pr ( 1 ) = p {\displaystyle \Pr(1)=p} , Pr ( 0 ) = 1 − p {\displaystyle \Pr(0)=1-p} . Up to position x {\displaystyle x} we want approximately p ⋅ x {\displaystyle p\cdot x} analogues of odd numbers (for s = 1 {\displaystyle s=1} ). We can choose this number of appearances as ⌈ x ⋅ p ⌉ {\displaystyle \lceil x\cdot p\rceil } , getting s = ⌈ ( x + 1 ) ⋅ p ⌉ − ⌈ x ⋅ p ⌉ {\displaystyle s=\lceil (x+1)\cdot p\rceil -\lceil x\cdot p\rceil } . This variant

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