11–20 of 51 posts
> A pipedream would be an entirely differentiable image compression pipeline where all the steps can be fine tuned together to optimize a particular image with respect to any differentiable loss function. Neural Image Compression? https://arxiv.org/abs/1908.08988
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.
This seems somewhat similar to the recently published GIFnets[1]. However, I believe GIFnets is training a reusable network to a predict palettes, and pixel assignments, while this post is focusing on optimising the "weights" (i.e. pixel values) for a single image.
I wonder if the loss functions from GIFnets could be applied to this single-image approach to potentially solve the banding problem via something a little more "perceptual" than the variance term mentioned.
[1]: "GIFnets: Differentiable GIF Encoding Framework" https://arxiv.org/abs/2006.13434
> A pipedream would be an entirely differentiable image compression pipeline where all the steps can be fine tuned together to optimize a particular image with respect to any differentiable loss function. Neural Image Compression? https://arxiv.org/abs/1908.08988
I fear that you might end up with hallucinations in your images ...
> A pipedream would be an entirely differentiable image compression pipeline where all the steps can be fine tuned together to optimize a particular image with respect to any differentiable loss function. Neural Image Compression? https://arxiv.org/abs/1908.08988
I fear that you might end up with hallucinations in your images ...
> A pipedream would be an entirely differentiable image compression pipeline where all the steps can be fine tuned together to optimize a particular image with respect to any differentiable loss function. Neural Image Compression? https://arxiv.org/abs/1908.08988