Exploring Repetitiveness Measures for Two-Dimensional Strings

TL;DR

This paper extends measures of repetitiveness to two-dimensional strings, proposing new metrics and showing that 2D macro schemes and straight line programs can be more compact than existing measures.

Contribution

It introduces new 2D repetitiveness measures and generalizes macro schemes and straight line programs, revealing their potential for better compression than traditional measures.

Findings

2D macro schemes and 2D SLPs can be asymptotically smaller than δ and γ measures.

Proposed extensions of δ and γ for 2D strings differ from previous square-based definitions.

Results can be extended to higher-dimensional strings.

Abstract

Detecting and measuring repetitiveness of strings is a problem that has been extensively studied in data compression and text indexing. However, when the data are structured in a non-linear way, like in the context of two-dimensional strings, inherent redundancy offers a rich source for compression, yet systematic studies on repetitiveness measures are still lacking. In the paper we introduce extensions of repetitiveness measures to general two-dimensional strings. In particular, we propose a new extension of the measures $\delta$ and $\gamma$, diverging from previous square based definitions proposed in [Carfagna and Manzini, SPIRE 2023]. We further consider generalizations of macro schemes and straight line programs for the 2D setting and show that, in contrast to what happens on strings, 2D macro schemes and 2D SLPs can be both asymptotically smaller than $\delta$ and $\gamma$. The results of the paper can be easily extended to $d$-dimensional strings with $d > 2$.