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.
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