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About 10,000 papers for “Arithmetic coding”

Arithmetic coding

Glen G. Langdon · 1979

Arithmetic coding is a data compression technique that encodes data (the data string) by creating a code string which represents a fractional value on the number line between 0 and 1. The coding algorithm is symbolwise recursive; i.e., it operates upon and encodes (decodes) one data symbol per iteration or recursion. On each recursion, the algorithm successively partitions an interval

Cited by 0

Arithmetic coding

Tjalkens, TJ Tjalling, Willems, FMJ Frans · TU/e Research Portal (Eindhoven University of Technology) · 1985

Cited by 0

Arithmetic Coding

Glen G. Langdon · 1979

Arithmetic coding is a data compression technique that encodes data (the data string) by creating a code string which represents a fractional value on the number line between 0 and 1. The coding algorithm is symbolwise recursive; i.e., it operates upon and encodes (decodes) one data symbol per iteration or recursion. On each recursion, the algorithm successively partitions an interval of the number line between 0 and I, and retains one of the partitions as the new interval. Thus, the algorithm successively deals

Cited by 0

Arithmetic Coding

When We Discussed · 2007

6> (xn=a i jx 1 ; : : : ; xn\\Gamma1 ) and uses it to interpret the received message. A binary transmission can be viewed as defining an interval within the real line from 0 to 1. For example, the transmission 01 is interpreted as a binary real number 0.01: : : , which corresponds to the interval [0:01; 0:10) (binary), i.e., the interval [0:25; 0:50] (base ten). A longer transmission 01101 corresponds to the interval [0:01101; 0:01110); because 01101 has the first string 01 as a prefix, the new interval is a sub-interval of the interval [0:01; 0:10]. A one megabyte binary file (2 2 3 bits) is thus viewed as specifying a number between 0 and 1 to a precision of about 10

Cited by 0

Arithmetic coding

Tjalkens, TJ Tjalling, Willems, FMJ Frans · Data Archiving and Networked Services (DANS) · 1985

Cited by 0

Arithmetic coding

Tjalkens, TJ Tjalling, Willems, FMJ Frans · TU/e Research Portal (Eindhoven University of Technology) · 1985

Cited by 0

Arithmetic coding

Tjalkens, T.J., Willems, F.M.J., Vinck, A.J. · Data Archiving and Networked Services (DANS) · 1985

Cited by 0

Arithmetic coding algorithm

E. Desurvire · 2009

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Rational arithmetic coding

Richard Bird · Cambridge University Press eBooks · 2010

This pearl, and the one following, is all about arithmetic coding, a way of doing data compression. Unlike other methods, arithmetic coding does not represent each individual symbol of the text as an integral number of bits; instead, the text as a whole is encoded as a binary fraction in the unit interval. Although the idea can be traced back much earlier, it was not until the publication of an “accessible implementation” by Witten, Neal and Cleary in 1987 that arithmetic coding became a serious competitor in the world of data compression. Over the past two decades the method has been refined and its advantages and disadvantages over rival schemes have been elucidated. Arithmetic coding can be more effective at compression than rivals such as Huffman coding, or Shannon–Fano coding, and is well suited to take account of the statistical properties of the symbols in a text. On the other hand, coding and decoding times are longer than with other methods.

Cited by 0

Distributed Arithmetic Coding

2017

This chapter introduces the concept of the distributed arithmetic coding (DAC) spectrum, which is a promising tool to answer a series of theoretical problems about DAC. AC can be extended rather intuitively to the case of asymmetric Slepian-Wolf (SW) coding. The encoding process is essentially identical to the original arithmetic coding. One of the potential advantages of DAC is that the probability model can be adjusted very flexibly for each coded symbol. However, this needs to be done at both encoder and decoder. Since the emergence of DAC, a thorough analysis of its properties has become an important and interesting topic for the research community. The chapter gives several experiments to verify the effectiveness of the permutation and weighted branching (WB) techniques. It assesses the following four DAC codecs: binary DAC (BDAC) codec; permutated BDAC (PBDAC) codec; BDAC+WB codec; and PBDAC+WB codec.

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Quantum arithmetic coding

Isaac L. Chuang, Dharmendra S. Modha · 2002

We study the problem of compressing a block of symbols (a block quantum state) emitted by a memoryless quantum Bernoulli source. We present a simple-to-implement quantum algorithm for projecting, with high probability, the block quantum state onto the typical subspace spanned by the leading eigenstates of its density matrix. We propose a fixed-rate quantum Shannon-Fano code to compress the projected block quantum state using a per symbol code rate that is slightly higher than the von Neumann entropy limit. Finally, we propose quantum arithmetic codes to efficiently implement quantum Shannon-Fano codes.

Cited by 0

Embedding channel coding in arithmetic coding

George F. Elmasry · IEE Proceedings - Communications · 1999

An arithmetic coding algorithm that incorporates channel coding is presented. For a source alphabet with m symbols, the selected m code points over the [0-1) interval have a desired minimum Hamming distance dmin between them. The decoder utilises this dmin in conjunction with the self-synchronisation property of arithmetic coding and knowledge of the source statistics to recover the original sequence of symbols, in the presence of channel errors, without the use of known channel coding techniques. The selected dmin provides a desired error protection level. The introduced scheme is suitable for bandwidth-limited binary symmetric channels (BSCs) with moderate to high error rates.

Cited by 18

P-adic arithmetic coding

A. Ya. Rodionov, Sergey Volkov · ArXiv.org · 2007

A new incremental algorithm for data compression is presented. For a sequence of input symbols algorithm incrementally constructs a p-adic integer number as an output. Decoding process starts with less significant part of a p-adic integer and incrementally reconstructs a sequence of input symbols. Algorithm is based on certain features of p-adic numbers and p-adic norm. p-adic coding algorithm may be considered as of generalization a popular compression technique - arithmetic coding algorithms. It is shown that for p = 2 the algorithm works as integer variant of arithmetic coding; for a special class of models it gives exactly the same codes as Huffman's algorithm, for another special model and a specific alphabet it gives Golomb-Rice codes.

Cited by 1Open access

Tutorial on Arithmetic Coding

Harry Printz · 1994

This document explains background material for the filing of two patents, Efficient interval width update in arithmetic coding, and Design of fast circuits for code point update in arithmetic coding. The aim is to give the reader the necessary technical foundation to understand the ideas in the patents, and appreciate their significance. 1 Introduction Arithmetic coding is a well-known method for lossless data compression [2, 9]. The idea can be traced back to the work of Shannon [8], but it was first explicitly elucidated by Elias, and described in a footnote in reference [1]. In this tutorial, we first explain the basic arithmetic coding algorithm. This algorithm assumes that the necessary arithmetic operations are performed with infinite precision, so it is not suitable for direct implementation. In the next part of the tutorial, we explain how to modify the algorithm to render it suitable for implementation in standard computer arithmetic. In the last part of the tutorial, we iden...

Cited by 0

𝑝-adic arithmetic coding

A. Ya. Rodionov, Sergey Volkov · Contemporary mathematics - American Mathematical Society · 2010

A new incremental algorithm for data compression is presented. For a sequence of input symbols algorithm incrementally constructs a p-adic integer number as an output. Decoding process starts with less significant part of a p-adic integer and incrementally reconstructs a sequence of input symbols. Algorithm is based on certain features of p-adic numbers and p-adic norm. p-adic coding algorithm may be considered as of generalization a popular compression technique - arithmetic coding algorithms. It is shown that for p = 2 the algorithm works as integer variant of arithmetic coding; for a special class of models it gives exactly the same codes as Huffman's algorithm, for another special model and a specific alphabet it gives Golomb-Rice codes.

Cited by 3