2009arXiv (Cornell University)Open access

Fast and Compact Prefix Codes

Travis Gagie, Gonzalo Navarro, Yakov Nekrich

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Abstract

It is well-known that, given a probability distribution over $n$ characters, in the worst case it takes (Θ(n \log n)) bits to store a prefix code with minimum expected codeword length. However, in this paper we first show that, for any $0 1), it takes $\Oh{n^{1 / c} \log n}$ bits to store a prefix code with expected codeword length at most $c$ times the minimum. In both cases, our data structures allow us to encode and decode any character in $\Oh{1}$ time.

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What this paper is about

It is well-known that, given a probability distribution over $n$ characters, in the worst case it takes (Θ(n \log n)) bits to store a prefix code with minimum expected codeword length. However, in this paper we first show that, for any $0 1), it takes $\Oh{n^{1 / c} \log n}$ bits to store a prefix code with expected codeword length at most $c$ times the minimum. In both cases, our data structures allow us to encode and decode any character in $\Oh{1}$ time.

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Available abstract

It is well-known that, given a probability distribution over $n$ characters, in the worst case it takes (Θ(n \log n)) bits to store a prefix code with minimum expected codeword length. However, in this paper we first show that, for any $0 1), it takes $\Oh{n^{1 / c} \log n}$ bits to store a prefix code with expected codeword length at most $c$ times the minimum. In both cases, our data structures allow us to encode and decode any character in $\Oh{1}$ time.

Key concepts: Prefix code, Prefix, Code word, Code (set theory), Universal code, Combinatorics, Computer science, Binary logarithm

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