In computer graphics and related applications, large datasets often lead to increased loading times. In many cases, however, not all data points are required for further processing, which allows the use of data reduction techniques. One such approach is the simplification of linear curve segments through point removal methods.
This thesis presents a comparative analysis of four point removal algorithms: Visvalingam–Whyatt, Reumann–Witkam, Lang, and Zhao–Saalfeld. The methods are evaluated using the border of Slovenia as a reference dataset. The evaluation focuses on approximation quality and computational performance. To examine the scalability of the algorithms, the experiments are repeated on datasets of different sizes. Furthermore, the behavior of the algorithms is analyzed on a curve containing a self-intersection.
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