Edge detection doesn’t explain line drawing
Ten years or so ago I was working on a video chip that had an upscaler feature. While prototyping and simulating it, we first started by applying a mathematically-correct (i.e. information preserving) FIR filter to do the upscale. Then we compared the result with other solutions and found that ours looked worse. We asked our colleagues to blind-test it and they all picked third-party-scaled images over ours.<p>At first we assumed that we must have had a bug somewhere because the Fourrier transform told us that our approach was optimal, but after more testing everything matched the expected output. Yet it looked worse.<p>So we started reverse-engineering the other solutions and, long story short, what they did better is that they added some form of edge-enhancement to the upscaling. Information-theory-wise it actually degraded the image, but subjectively the sharper outlines were just so much nicer to look at and looked correct-er. You felt like you could more easily tell the details even though, again, in a mathematical sense you actually lost information that way.<p>I don't think it makes a lot of sense to reduce human vision to edge detection (we can still make sense of a blurry image like this one after all: <a href="https://static0.makeuseofimages.com/wordpress/wp-content/uploads/2013/01/blurred-city.jpg" rel="nofollow noreferrer">https://static0.makeuseofimages.com/wordpress/wp-content/upl...</a> ) but it's clear to me from empirical evidence that edge-detection is a core aspect of how we parse visual stimuli.<p>As such I'm a bit confused as to why the author seems to see this as a binary proposition. That being said, I could just be misunderstanding completely the point the author is trying to make.