https://hackerpaper.kaipfister.de/p/1811.12817/Collected HN comments and submissions linking this paper.enMon, 02 Nov 2026 11:16:22 GMThttps://news.ycombinator.com/item?id=32399485urn:hackerpapers:mention:1811.12817:32399485Tue, 09 Aug 2022 15:44:19 GMT<p>Was thinking of work like https://arxiv.org/pdf/1811.12817.pdf although the compression improvements vs classical methods are much more impressive for lossy compression</p><p><a href="https://hackerpaper.kaipfister.de/p/1811.12817/">Paper and discussion history</a>. A mention is not an endorsement.</p>eess.IVhttps://news.ycombinator.com/item?id=24451266urn:hackerpapers:mention:1811.12817:24451266Sat, 12 Sep 2020 09:59:38 GMT<p>As u/londons_explore mentioned, in theory you can train a model for lossless reconstruction - there are several papers about this, e.g. [1] is a good recent example. Lossless compressors need to learn a probability distribution over each input pixel, which amounts to maximum likelihood estimation in the original image space. The model in the demo is a lossy compression method because it first projects the input to a lower dimensional space and performs quantization of this representation to integer values so the result can be ultimately entropy coded. It uses the mean-scale hyperprior model…</p><p><a href="https://hackerpaper.kaipfister.de/p/1811.12817/">Paper and discussion history</a>. A mention is not an endorsement.</p>eess.IV