Arithmetic Coding for Data Compression (1994) [44 citations — 8 self]
by
Jeffrey Scott Vitter
,
Paul G. Howard
,
Paul G. Howard
,
Jeffrey Scott Vitter
Information Processing and Management
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@INPROCEEDINGS{Vitter94arithmeticcoding,
author = {Jeffrey Scott Vitter and Paul G. Howard and Paul G. Howard and Jeffrey Scott Vitter},
title = {Arithmetic Coding for Data Compression},
booktitle = {Information Processing and Management},
year = {1994},
pages = {749--763}
}
author = {Jeffrey Scott Vitter and Paul G. Howard and Paul G. Howard and Jeffrey Scott Vitter},
title = {Arithmetic Coding for Data Compression},
booktitle = {Information Processing and Management},
year = {1994},
pages = {749--763}
}
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Abstract:
.23> , en , and an accurate assessment of the probability distribution P of the events, Shannon [1] proved that the the smallest possible expected number of bits needed to encode an event is the entropy of P , denoted by H(P ) = n X k=1 \Gammapfe k g log 2 pfekg; where pfekg is the probability that event ek occurs. An optimal code outputs \Gamma log 2 p bits to encode an event whose probability of occurrence is p. Pure arithmetic codes supplied with accurate probabilities provides optimal compression. The older and better-known Huffman codes [2] are optimal only among instantaneous codes, that is, those in whi
