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Minor road updates (like those in map data that might be a few months old if you're using maps from different regions) usually result in negligible cost differences for shortcuts, so the pre-calculated values remain effective.
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: