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Looks cool! Two questions: - Is this approach also learning the palette? It is kind presented as a given here but it is of course very important for a good di
by SimplyUnknown 6y ago
Looks cool!
Two questions:
- Is this approach also learning the palette? It is kind presented as a given here but it is of course very important for a good dithering.
- The loss function might work better on spatially downsampled images. The downsampling causes a mix of the image colors making the dithered image look more like the original given a good dithering. This also naturally removes the variance that is now penalized in the loss function as this is blurred away.
- underanalyzer 6y agoThis blew up while I was asleep so I’ll try my best to answer now! 1. Yes the palette is being optimized for as well which is imho what makes it different from a quantization approach 2. That’s a good point. I cite a reference blog post which does use blur in the loss function towards the end of the post. Unfortunately I think pure blur would still produce a noisy image as it would remove variance in the eyes of the loss function but not the final image. I would guess something like the example I give with purple, red, blue pixels would still be a problem for blurred loss