The N-closest or N-best dithering algorithm is a straightforward solution to the N-candidate problem. As the name suggests, the set of candidates is given by the closest palette colours to the input pixel. To determine their weights, we simply take the inverse of the distance to the input pixel. This is essentially the inverse distance weighting (IDW) method for multivariate interpolation, also known as Shepard’s method. The following pseudocode sketches out a possible implementation:
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Implementing natural neighbour interpolation implies the construction of a geometric Voronoi diagram, however this is not strictly the case. Since the Delaunay triangulation is the dual graph of the Voronoi diagram, all the information needed to perform natural neighbour interpolation is already implicit within the triangulation itself. Algorithms to determine natural neighbours from the Delaunay triangulation can be found in several papers within the literature[4][5]. Unfortunately the relative complexity of natural neighbour interpolation means that it is slower than barycentric interpolation by a considerable margin.