This algorithm attempts to minimise numerically. Because of this, the quality of the dither produced by Knoll’s algorithm is much higher than any other of the N-candidate methods we have covered so far. It is also the slowest however, as it requires a greater per-pixel to be really effective. More details are given in Knoll’s now expired patent[3]. I have put together a GPU implementation of Knoll’s algorithm on Shadertoy here.
Consider the energy crunch: Global data-center power demand will more than double by 2030, per the International Energy Agency, forcing upgrades to grids, water systems, and connectivity. China’s state grids are embarking on a 5 trillion yuan ($722 billion) expansion explicitly for AI and data centers that is equivalent to 4% of GDP, according to Moody’s. The Qatar Investment Authority has announced a project worth $20 billion (9% of the nation’s GDP), to develop AI data centers and computing infrastructure. And in Korea, despite AI-related spending only accounting for 0.4% of GDP, the country’s recently established sovereign wealth fund is almost exclusively targeted at high-tech industries including AI and chips, while planning to deploy a war chest worth 5.7% of GDP over the next five years.
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22:36, 27 февраля 2026Бывший СССР