Towards a Definitive Compressibility Measure for Repetitive Sequences

—While the k th order empirical entropy is an ac- cepted measure of the compressibility of individual sequences on classical text collections, it is useful only for small values of k and thus fails to capture the compressibility of repetitive sequences. In the absence of an established way of quantifying the latter, ad-hoc measures like the size z of the Lempel–Ziv parse are frequently used to estimate repetitiveness. The size b ≤ z of the smallest bidirectional macro scheme captures better what can be achieved via copy-paste processes, though it is NP-complete to compute, and it is not monotone upon appending symbols. Recently, a more principled measure, the size γ of the smallest string attractor , was introduced. The measure γ ≤ b lower-bounds all the previous relevant ones, while length- n strings can be represented and efficiently indexed within space O ( γ log nγ ) , which also upper-bounds many measures, including z . Although γ is arguably a better measure of repetitiveness than b , it is also NP-complete to compute and not monotone, and it is unknown if one can represent all strings in o ( γ log n ) space. In this paper, we study an even smaller measure, δ ≤ γ , which can be computed in linear time, is monotone, and allows encoding every string in O ( δ log nδ ) space because z = O ( δ log nδ ) . We argue that δ better captures the compressibility of repetitive strings. Concretely, we show that (1) δ can be strictly smaller than γ , by up to a logarithmic factor; (2) there are string families needing Ω( δ log nδ ) space to be encoded, so this space is optimal for every n and δ ; (3) one can build run-length context-free grammars of size O ( δ log nδ ) , whereas the smallest (non-run-length) grammar can be up to Θ(log n/ log log n ) times larger; and (4) within O ( δ log nδ ) space, we can not only represent a string but also offer logarithmic-time access to its symbols, computation of substring fingerprints, and efficient indexed searches for pattern occurrences. We further refine the above results to account for the alphabet size σ of the string, showing that Θ( δ log n log σ δ log n ) space is necessary and sufficient to represent the string and to efficiently support access, fingerprinting, and pattern matching queries.