Great idea to use Stable Diffusion for image compression. There are deep links between machine learning and data compression (which I’m sure the author is aware of).
If you could compute the true conditional Kolmogorov complexity of an image or video file given all visual online media as the prior, I imagine you would obtain mind-blowing compression ratios.
People complain of the biased artifacts that appear when using neural networks for compression, but I’m not concerned in the long term. The ability to extract algorithmic redundancy from images using neural networks is obviously on its way to outclassing manually crafted approaches, and it’s just a matter of time before we are able to tack on a debiasing step to the process (such that the distribution of error between the reconstructed image and the ground truth has certain nice properties).
If you could compute the true conditional Kolmogorov complexity of an image or video file given all visual online media as the prior, I imagine you would obtain mind-blowing compression ratios.
People complain of the biased artifacts that appear when using neural networks for compression, but I’m not concerned in the long term. The ability to extract algorithmic redundancy from images using neural networks is obviously on its way to outclassing manually crafted approaches, and it’s just a matter of time before we are able to tack on a debiasing step to the process (such that the distribution of error between the reconstructed image and the ground truth has certain nice properties).