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The average redundancy of the Shannon code, $R_n$, as a function of the block length $n$, is known to exhibit two very different types of behavior, depending on the rationality or irrationality of certain parameters of the source: It either…

Information Theory · Computer Science 2011-04-05 Neri Merhav

For any Markov source, there exist universal codes whose normalized codelength approaches the Shannon limit asymptotically as the number of samples goes to infinity. This paper investigates how fast the gap between the normalized codelength…

Information Theory · Computer Science 2018-05-08 Kedar Shriram Tatwawadi , Jiantao Jiao , Tsachy Weissman

The penalty incurred by imposing a finite delay constraint in lossless source coding of a memoryless source is investigated. It is well known that for the so-called block-to-variable and variable-to-variable codes, the redundancy decays at…

Information Theory · Computer Science 2016-11-17 Ofer Shayevitz , Eado Meron , Meir Feder , Ram Zamir

Consider the set of source distributions within a fixed maximum relative entropy with respect to a given nominal distribution. Lossless source coding over this relative entropy ball can be approached in more than one way. A problem…

Information Theory · Computer Science 2011-05-03 Michael B. Baer , Farzad Rezaei , Charalambos D. Charalambous

This paper presents new lower and upper bounds for the compression rate of binary prefix codes optimized over memoryless sources according to various nonlinear codeword length objectives. Like the most well-known redundancy bounds for…

Information Theory · Computer Science 2010-10-08 Michael B. Baer

In this paper we study the redundancy of Huffman codes. In particular, we consider sources for which the probability of one of the source symbols is known. We prove a conjecture of Ye and Yeung regarding the upper bound on the redundancy of…

Information Theory · Computer Science 2016-11-17 Soheil Mohajer , Payam Pakzad , Ali Kakhbod

We study the redundancy of universally compressing strings $X_1,\dots, X_n$ generated by a binary Markov source $p$ without any bound on the memory. To better understand the connection between compression and estimation in the Markov…

Information Theory · Computer Science 2018-06-21 Changlong Wu , Maryam Hosseini , Narayana Santhanam

For variable-length coding with an almost-sure distortion constraint, Zhang et al. show that for discrete sources the redundancy is upper bounded by $\log n/n$ and lower bounded (in most cases) by $\log n/(2n)$, ignoring lower order terms.…

Information Theory · Computer Science 2026-01-21 Sharang M. Sriramu , Aaron B. Wagner

We study finite-length bounds for source coding with side information for Markov sources and channel coding for channels with conditional Markovian additive noise. For this purpose, we propose two criteria for finite-length bounds. One is…

Information Theory · Computer Science 2017-01-10 Masahito Hayashi , Shun Watanabe

In this work, lossy distributed compression of pairs of correlated sources is considered. Conventionally, Shannon's random coding arguments -- using randomly generated unstructured codebooks whose blocklength is taken to be asymptotically…

Information Theory · Computer Science 2020-10-21 Farhad Shirani , S. Sandeep Pradhan

We study the following semi-deterministic setting of the joint source-channel coding problem: a deterministic source sequence (a.k.a. individual sequence) is transmitted via a memoryless channel, using delay-limited encoder and decoder,…

Information Theory · Computer Science 2021-09-29 Neri Merhav

We study the effects of finite-precision representation of source's probabilities on the efficiency of classic source coding algorithms, such as Shannon, Gilbert-Moore, or arithmetic codes. In particular, we establish the following simple…

Information Theory · Computer Science 2007-12-04 Yuriy Reznik

In this paper, we investigate the redundancy of universal coding schemes on smooth parametric sources in the finite-length regime. We derive an upper bound on the probability of the event that a sequence of length $n$, chosen using…

Information Theory · Computer Science 2011-10-27 Ahmad Beirami , Faramarz Fekri

We analyze the Dawid-Rissanen prequential maximum likelihood codes relative to one-parameter exponential family models M. If data are i.i.d. according to an (essentially) arbitrary P, then the redundancy grows at rate c/2 ln n. We show that…

Machine Learning · Computer Science 2007-07-16 Peter Grunwald , Steven de Rooij

Shannon's seminal 1948 work gave rise to two distinct areas of research: information theory and mathematical coding theory. While information theory has had a strong influence on theoretical neuroscience, ideas from mathematical coding…

Neurons and Cognition · Quantitative Biology 2015-02-25 Carina Curto , Vladimir Itskov , Katherine Morrison , Zachary Roth , Judy L. Walker

The efficiency of a code is estimated by its redundancy $R$, while the complexity of a code is estimated by its average delay $\bar N$. In this work we construct word-based codes, for which $R \lesssim \bar N^{-5/3}$. Therefore, word-based…

Information Theory · Computer Science 2007-12-04 G. L. Khodak

We present new lower and upper bounds for the compression rate of binary prefix codes optimized over memoryless sources according to two related exponential codeword length objectives. The objectives explored here are exponential-average…

Information Theory · Computer Science 2011-05-03 Michael B. Baer

We generalize the notion of the stopping redundancy in order to study the smallest size of a trapping set in Tanner graphs of linear block codes. In this context, we introduce the notion of the trapping redundancy of a code, which…

Information Theory · Computer Science 2016-11-17 Stefan Laendner , Thorsten Hehn , Olgica Milenkovic , Johannes B. Huber

Using the concept of discrete noiseless channels, it was shown by Shannon in A Mathematical Theory of Communication that the ultimate performance of an encoder for a constrained system is limited by the combinatorial capacity of the system…

Information Theory · Computer Science 2008-09-09 Georg Böcherer , Valdemar Cardoso da Rocha , Cecilio Pimentel

The nature of neural codes is central to neuroscience. Do neurons encode information through relatively slow changes in the emission rates of individual spikes (rate code), or by the precise timing of every spike (temporal codes)? Here we…

Quantitative Methods · Quantitative Biology 2018-03-08 Agnieszka Pregowska , Ehud Kaplan , Janusz Szczepanski
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