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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昨天，铁路部门对网传「半夜候补成功 1700 元车票作废」传闻进行了回应，称相关报道并不属实。
SSIM (Structural Similarity Index Measure) compares two images by evaluating luminance, contrast, and structural patterns across local windows. It returns a score from -1 to 1: 1.0 means the images are pixel-identical, 0 means no structural correlation, and negative values mean the images are anti-correlated (less alike than random noise). For glyph comparison, it answers the question: do these two rendered characters share the same visual structure?