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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二舅曾被迫加入南越军队,幸得大舅从中斡旋,才侥幸在次日被放归,躲过了“每天死上百人”的战场。当日后被审问为什么加入南方兵时,他反诘审问者:“你们为什么当北方兵?因为你们生在北方,我生在南方。”并认为既然国家统一,应不计前嫌。
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(I find the anachronistic combination of hedcuts and dot matrix printer typography particularly fascinating.)