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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We've changed the offset from \x00\x00 to \x10\x00 (from 0 to 16). We've changed the length from \x10\x00 to \x04\x00 (from 16 to 4). (Remember that all the numbers are in little-endian!)