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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在「快速達到流利程度」的承諾之外,各種新方法與科技已經徹底改變我們在時間越來越匱乏的時代如何學習語言。其中之一是「微學習」(microlearning)——這種方法將新資訊拆分成小小片段,讓人們能在極短時間內吸收,有時甚至短至幾分鐘或幾秒鐘。它建基於一項稱為「遺忘曲線」的概念:當人們一次接收大量資訊時,隨著時間推移記住的內容反而會更少。