<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>rasz</author><text>Picture is not compressed, its hallucinated from vague memory of the real thing, a mere dream. Cars vanish, building change wall structure, even the license plate receives fake text absent from source materia.&lt;p&gt;Its a giant guesswork of what was there originally. Reminds me of Xerox scanners lying about scanned in numbers &lt;a href=&quot;http:&amp;#x2F;&amp;#x2F;www.dkriesel.com&amp;#x2F;en&amp;#x2F;blog&amp;#x2F;2013&amp;#x2F;0802_xerox-workcentres_are_switching_written_numbers_when_scanning&quot; rel=&quot;nofollow&quot;&gt;http:&amp;#x2F;&amp;#x2F;www.dkriesel.com&amp;#x2F;en&amp;#x2F;blog&amp;#x2F;2013&amp;#x2F;0802_xerox-workcentres_...&lt;/a&gt;</text><parent_chain></parent_chain></comment><story><title>Generative Adversarial Networks for Extreme Learned Image Compression</title><url>https://data.vision.ee.ethz.ch/aeirikur/extremecompression/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>bcheung</author><text>I&amp;#x27;d be curious to see how different levels of quantization affect the image.  From the paper it looks like the quantization is applied at the latent feature space.  I wonder if it has similar effects like the celebrity GAN&amp;#x27;s we have seen where interpolating in the latent space results in morphing from one face to another.  Could be funny when compression doesn&amp;#x27;t result in something blocky or distorted, but replacing objects with other objects that look similar to them.&lt;p&gt;This seems to be for static images, but this gets me wondering if an RNN can be used and have better motion prediction that other current &amp;quot;hard coded&amp;quot; solutions.&lt;p&gt;Also, the more specific the domain, the better the compression, since it can specialize.  I&amp;#x27;m wondering about the practical applications of this.  Do we have different baselines that can be used for different use cases?</text><parent_chain></parent_chain></comment><story><title>Generative Adversarial Networks for Extreme Learned Image Compression</title><url>https://data.vision.ee.ethz.ch/aeirikur/extremecompression/</url></story>