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:
在高端市场，AI产业化的加速落地直接引爆了存储需求。不同于传统服务器，AI服务器需要承载大规模数据训练、高频次数据运算，对HBM（高带宽存储）、高端DDR5内存及企业级SSD的需求量呈爆发式增长，单台AI服务器的存储需求量更是达到传统服务器的8-10倍。其中，HBM凭借超高带宽、低延迟的核心优势，成功破解了AI运算中的“内存墙”技术瓶颈，成为AI算力基建的核心战略级资源，目前2026年全球三大存储巨头的HBM产能已全部提前售罄，部分头部AI企业甚至提前签订2027年长期供货协议。
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