Compressibility measures for two-dimensional data

Lorenzo Carfagna,Giovanni Manzini

CoRR(2023)

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摘要
In this paper we extend to two-dimensional data two recently introduced one-dimensional compressibility measures: the $\gamma$ measure defined in terms of the smallest string attractor, and the $\delta$ measure defined in terms of the number of distinct substrings of the input string. Concretely, we introduce the two-dimensional measures $\gamma_{2D}$ and $\delta_{2D}$ as natural generalizations of $\gamma$ and $\delta$ and study some of their properties. Among other things, we prove that $\delta_{2D}$ is monotone and can be computed in linear time, and we show that although it is still true that $\delta_{2D} \leq \gamma_{2D}$ the gap between the two measures can be $\Omega(\sqrt{n})$ for families of $n\times n$ matrices and therefore asymptotically larger than the gap in one-dimension. Finally, we use the measures $\gamma_{2D}$ and $\delta_{2D}$ to provide the first analysis of the space usage of the two-dimensional block tree introduced in [Brisaboa et al., Two-dimensional block trees, The computer Journal, 2023].
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关键词
measures,data,two-dimensional
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