A notable resource on the topic of ordered dithering using arbitrary palettes is Joel Yliluoma’s Arbitrary-Palette Positional Dithering Algorithm. One key difference of Yliluoma’s approach is in the use of error metrics beyond the minimisation of . Yliluoma notes that the perceptual or psychovisual quality of the dither must be taken into account in addition to its mathematical accuracy. This is determined by use of some cost function which considers the relationship between a set of candidate colours. The number of candidates, the particular cost function, and the thoroughness of the selection process itself give rise to a number of possible implementations, each offering varying degrees of quality and time complexity.
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成本端的劣势只是其一，更让蔚来焦虑的，是数据闭环上的差距。智能驾驶的下半场，算力只是基础，数据才是燃料。截至2026年初，蔚来累计保有量约70万辆，数据规模与头部对手相距甚远——而智能驾驶的迭代速度，最终取决于路上跑的车有多少。