Global Polyline Simplification Under the Fréchet Distance: Theory and Practice

Abstract

Given an input polyline with n vertices, the global polyline simplification problem seeks a simplified polyline with the minimum number of vertices whose distance to the original polyline does not exceed a given bound. For the vertex-restricted variant, where the simplified polyline is required to be a subsequence of the input vertices, an algorithm with a running time of 𝒪(n³) was presented in previous work, using the Fréchet distance as the polyline similarity measure. A closely related variant is the local polyline simplification problem, in which the distance bound is required to hold for every individual shortcut segment replacing a sub-polyline. This condition implies that any locally valid simplification is also globally valid, whereas the converse does not hold. As a consequence, globally optimal simplifications may use substantially fewer vertices than locally optimal ones. Indeed, in previous work, instances were constructed in which the optimal global simplification is smaller by a constant factor. On the algorithmic side, optimal local simplifications can be computed significantly faster, namely in 𝒪(n² log n) under the Fréchet distance, and efficient heuristics are also available. This raises the question of which problem variant is more suitable for practical application.
In this paper, we first show that there exist instances for which the optimal solution sizes of global and local polyline simplification differ by a factor in Θ(n), substantially strengthening the previously known constant-factor separation. We then present the first practical implementations of existing algorithms for global polyline simplification and experimentally evaluate their performance. To this end, we introduce several engineering techniques that considerably accelerate these algorithms. Moreover, we develop an implicit Fréchet framework that allows many Fréchet-related problems to be addressed in a weaker computational model. Within this framework, explicit geometric computations can be reduced to simple comparisons, resulting in significantly more robust implementations. Somewhat surprisingly, our experimental results reveal that, despite the large worst-case gap established by our theoretical result, the difference in solution size between optimal global and local simplifications is negligible in practice. Motivated by this observation, we propose a heuristic for global polyline simplification that is guaranteed to produce solutions of size equal to or smaller than the optimal local simplification. On a benchmark consisting of one million polylines, the heuristic yields suboptimal results on only eight while being significantly faster than the optimal algorithms.