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Data Compression Using Adaptive Coding and Partial String Matching
 IEEE Transactions on Communications
, 1984
"... The recently developed technique of arithmetic coding, in conjunction with a Markov model of the source, is a powerful method of data compression in situations where a linear treatment is inappropriate. Adaptive coding allows the model to be constructed dynamically by both encoder and decoder during ..."
Abstract

Cited by 331 (20 self)
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The recently developed technique of arithmetic coding, in conjunction with a Markov model of the source, is a powerful method of data compression in situations where a linear treatment is inappropriate. Adaptive coding allows the model to be constructed dynamically by both encoder and decoder during the course of the transmission, and has been shown to incur a smaller coding overhead than explicit transmission of the model's statistics. But there is a basic conflict between the desire to use highorder Markov models and the need to have them formed quickly as the initial part of the message is sent. This paper describes how the conflict can be resolved with partial string matching, and reports experimental results which show that mixedcase English text can be coded in as little as 2.2 bits/ character with no prior knowledge of the source.
Soft decoding and synchronization of arithmetic codes: Application to image transmission over noisy channels
, 2003
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CE 263. Chapter 7 W99 Binary Probability Estimation
"... Introduction This module treats the problem of practical approaches to estimating the relative frequencies of a binary source. In higher order models, there may be many contexts. Each context is treated independently, however, so we can assume a single probability distribution for the adapter techn ..."
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Introduction This module treats the problem of practical approaches to estimating the relative frequencies of a binary source. In higher order models, there may be many contexts. Each context is treated independently, however, so we can assume a single probability distribution for the adapter technique that estimates the relative frequencies. Adaptation function is a nite state machine. Simple adaptation employs count ratios (or scaled counts), and several countratio methods are discussed. The notion of speed of adaptation [4], or adaptation inertia is presented. A section describes the concept of indexbased adaptation, via a class of counting and forgetting techniques. An adaptive Golomb code [6] is designed with a wandering heuristic. 7.1 Dynamic Adaptation of Codes for Nonbinary data Approaches that learn the probability distribution of a data le while compressing the data are called onepass approaches becaus
EURASIP Journal on Applied Signal Processing 2005:4, 510–524 c ○ 2005 Hindawi Publishing Corporation Joint SourceChannel Coding Based on CosineModulated Filter Banks for ErasureResilient Signal Transmission
"... This paper examines erasure resilience of oversampled filter bank (OFB) codes, focusing on two families of codes based on cosinemodulated filter banks (CMFB). We first revisit OFBs in light of filter bank and frame theory. The analogy with channel codes is then shown. In particular, for paraunitary ..."
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This paper examines erasure resilience of oversampled filter bank (OFB) codes, focusing on two families of codes based on cosinemodulated filter banks (CMFB). We first revisit OFBs in light of filter bank and frame theory. The analogy with channel codes is then shown. In particular, for paraunitary filter banks, we show that the signal reconstruction methods derived from the filter bank theory and from coding theory are equivalent, even in the presence of quantization noise. We further discuss frame properties of the considered OFB structures. Perfect reconstruction (PR) for the CMFBbased OFBs with erasures is proven for the case of erasure patterns for which PR depends only on the general structure of the code and not on the prototype filters. For some of these erasure patterns, the expression of the meansquare reconstruction error is also independent of the filter coefficients. It can be expressed in terms of the number of erasures, and of parameters such as the number of channels and the oversampling ratio. The various structures are compared by simulation for the example of an image transmission system.