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:
描述：nums1 中数字 x 的「下一个更大元素」是指 x 在 nums2 中对应位置右侧的第一个比 x 大的元素。给你两个没有重复元素的数组 nums1 和 nums2，其中 nums1 是 nums2 的子集。对于每个 nums1[i]，找出其在 nums2 中的下一个更大元素；若不存在，返回 -1。
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