Due to this more measured approach, error-diffusion dithering is even better at preserving details and can produce a more organic looking final image. However, the algorithm itself is inherently serial and not easily parallelised. Additionally, the propagation of error can cause small discrepancies in one part of the image to cascade into other distant areas. This is very obvious during animation, where pixels will appear to jitter between frames. It also makes files harder to compress.
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Data flows left to right. Each stage reads input, does its work, writes output. There's no pipe reader to acquire, no controller lock to manage. If a downstream stage is slow, upstream stages naturally slow down as well. Backpressure is implicit in the model, not a separate mechanism to learn (or ignore).