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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

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

This paper describes universal lossless coding strategies for compressing sources on countably infinite alphabets. Classes of memoryless sources defined by an envelope condition on the marginal distribution provide benchmarks for coding…

Statistics Theory · Mathematics 2015-01-05 Stéphane Boucheron , Aurélien Garivier , Elisabeth Gassiat

The minimum average number of bits need to describe a random variable is its entropy, assuming knowledge of the underlying statistics On the other hand, universal compression supposes that the distribution of the random variable, while…

Information Theory · Computer Science 2014-04-02 Maryam Hosseini , Narayana Santhanam

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

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 redundancy for universal lossless compression of discrete memoryless sources in Campbell's setting is characterized as a minimax R\'enyi divergence, which is shown to be equal to the maximal $\alpha$-mutual information via a generalized…

Information Theory · Computer Science 2019-03-12 Semih Yagli , Yücel Altuğ , Sergio Verdú

Consider the problem of predicting the next symbol given a sample path of length n, whose joint distribution belongs to a distribution class that may have long-term memory. The goal is to compete with the conditional predictor that knows…

Statistics Theory · Mathematics 2024-04-25 Yanjun Han , Tianze Jiang , Yihong Wu

This paper presents new lower and upper bounds for the optimal compression of binary prefix codes in terms of the most probable input symbol, where compression efficiency is determined by the nonlinear codeword length objective of…

Information Theory · Computer Science 2008-09-09 Michael Baer

We study universal compression of sequences generated by monotonic distributions. We show that for a monotonic distribution over an alphabet of size $k$, each probability parameter costs essentially $0.5 \log (n/k^3)$ bits, where $n$ is the…

Information Theory · Computer Science 2007-07-13 Gil I. Shamir

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

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

We apply so-called tree straight-line programs to the problem of lossless compression of binary trees. We derive upper bound on the maximal pointwise redundancy (or worst-case redundancy) that improve previous bounds obtained by Zhang,…

Information Theory · Computer Science 2017-02-01 Danny Hucke , Markus Lohrey

A new framework is introduced for examining and evaluating the fundamental limits of lossless data compression, that emphasizes genuinely non-asymptotic results. The {\em sample complexity} of compressing a given source is defined as the…

Information Theory · Computer Science 2026-04-16 Terence Viaud , Ioannis Kontoyiannis

This paper considers the problem of guessing the realization of a finite alphabet source when some side information is provided. The only knowledge the guesser has about the source and the correlated side information is that the joint…

Information Theory · Computer Science 2007-07-16 Rajesh Sundaresan

It is known that for memoryless sources, the average and maximal redundancy of fixed-to-variable length codes, such as the Shannon and Huffman codes, exhibit two modes of behavior for long blocks. It either converges to a limit or it has an…

Information Theory · Computer Science 2012-08-07 Neri Merhav , Wojciech Szpankowski

The problem of variable-rate lossless data compression is considered, for codes with and without prefix constraints. Sharp bounds are derived for the best achievable compression rate of memoryless sources, when the excess-rate probability…

Information Theory · Computer Science 2025-11-13 Andreas Theocharous , Lampros Gavalakis , Ioannis Kontoyiannis

In this paper, we propose {\em distributed network compression via memory}. We consider two spatially separated sources with correlated unknown source parameters. We wish to study the universal compression of a sequence of length $n$ from…

Information Theory · Computer Science 2012-10-09 Ahmad Beirami , Faramarz Fekri

Maximal repetition of a string is the maximal length of a repeated substring. This paper investigates maximal repetition of strings drawn from stochastic processes. Strengthening previous results, two new bounds for the almost sure growth…

Information Theory · Computer Science 2020-03-11 Łukasz Dębowski

The trapping redundancy of a linear code is the number of rows of a smallest parity-check matrix such that no submatrix forms an $(a,b)$-trapping set. This concept was first introduced in the context of low-density parity-check (LDPC) codes…

Information Theory · Computer Science 2016-11-15 Yu Tsunoda , Yuichiro Fujiwara
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