Polyline Simplification under the Local Fr\'echet Distance has Almost-Quadratic Runtime in 2D

TL;DR

This paper presents an improved algorithm for polyline simplification under the local Fréchet distance, achieving almost-quadratic runtime in 2D, and explores properties of the wavefront data structure.

Contribution

It adapts existing techniques to reduce the complexity of polyline simplification under the local Fréchet distance from cubic to nearly quadratic time in 2D.

Findings

Algorithm runs in O(n^2 log n) time for general case.

Under L1 and L-infinity norms, the algorithm simplifies to O(n^2) time.

Certain polyline classes guarantee the O(n^2) runtime in Euclidean norm.

Abstract

Given a polyline on $n$ vertices, the polyline simplification problem asks for a minimum size subsequence of these vertices defining a new polyline whose distance to the original polyline is at most a given threshold under some distance measure, usually the local Hausdorff or the local Fr\'echet distance. Here, local means that, for each line segment of the simplified polyline, only the distance to the corresponding sub-curve in the original polyline is measured. Melkman and O'Rourke [Computational Morphology '88] introduced a geometric data structure to solve polyline simplification under the local Hausdorff distance in $O(n^2 \log n)$ time, and Guibas, Hershberger, Mitchell, and Snoeyink [Int. J. Comput. Geom. Appl. '93] considered polyline simplification under the Fr\'echet distance as ordered stabbing and provided an algorithm with a running time of $O(n^2 \log^2 n)$, but they did not restrict the simplified polyline to use only vertices of the original polyline. We show that their techniques can be adjusted to solve polyline simplification under the local Fr\'echet distance in $O(n^2 \log n)$ time instead of $O(n^3)$ when applying the Imai--Iri algorithm. Our algorithm may serve as a more efficient subroutine for multiple other algorithms. We provide a simple algorithm description as well as rigorous proofs to substantiate this theorem. We also investigate the geometric data structure introduced by Melkman and O'Rourke, which we refer to as wavefront, in more detail and feature some interesting properties. As a result, we can prove that under the L$_1$ and the L$_\infty$ norm, the algorithm can be significantly simplified and then only requires a running time of $O(n^2)$. We also define a natural class of polylines where our algorithm always achieves this running time also in the Euclidean norm L$_2$.