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Linear KL-Optimal Frequency Normalisation
[Submitted on 1 May 2026 (v1), last revised 21 Aug 2026 (this ve · 2026-05-01 · via math updates on arXiv.org

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Abstract:Fast implementations of range coding and asymmetric numeral systems (ANS) owe their excellent performance to replacing slow division instructions by bit-shifts in their encoding and decoding algorithms. This is possible when the frequency distribution of symbols is normalised such that it sums to a power of two. However, such normalisation typically introduces a marginal increase in the Kullback-Leibler divergence between the original and the normalised distribution, leading to a worse compression ratio. We show that the currently used methods for frequency normalisation are suboptimal in both their running time and the achieved Kullback-Leibler divergence. We propose a new method for frequency normalisation that is asymptotically linear in the number of symbols and achieves the smallest possible Kullback-Leibler divergence between the original and the normalised distribution. The method is based on a solution to a separable concave optimisation problem, which may be of independent interest.

Submission history

From: Kamila Szewczyk [view email]
[v1] Fri, 1 May 2026 11:28:11 UTC (28 KB)
[v2] Fri, 21 Aug 2026 17:17:28 UTC (8 KB)