Another way to approach dithering is to analyse the input image in order to make informed decisions about how best to perturb pixel values prior to quantisation. Error-diffusion dithering does this by sequentially taking the quantisation error for the current pixel (the difference between the input value and the quantised value) and distributing it to surrounding pixels in variable proportions according to a diffusion kernel . The result is that input pixel values are perturbed just enough to compensate for the error introduced by previous pixels.
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Much of this is powered by a powerful custom conditional language that we created. Designers use this language throughout the game’s systems to configure when and how different parts of the game should change based on player actions that flow through the backend services. This includes the entirety of the game’s quest progression system, defining what actions the player needs to take to progress through the campaign.
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