I am a little confused how this relates to jaggies. What signal is being made with the triangle example, which creates the jaggies?
Mehvix
Undersampling a triangle's slope function results in a step/binary function (low-freq and jagged)
nickjiang2378
Another way to see this is that you sampled more times per pixel (ex. 4 instead of 1), we would be able to tell more precisely what percentage of the pixel is included in the triangle. Because we only have one sample point for the jaggies, we can only say it's fully in or fully out of the triangle, rather than 25% or 50% in the triangle. We can use this later on as an antialiasing method to make the pixel color a shade of the full color (ie. instead of red, it'd be light red, which would reduce the aliasing we see).
lycorisradiatu
In case anyone interested, here's another relevant video explaining frequency aliases and providing real-life examples: https://www.youtube.com/watch?v=v7qjeUFxVwQ. It mentions the waggon wheel effect caused by aliasing, which is when moving wheel seems to stand still.
matthewlee626
Wow! Great visual; I once saw a helicopter take off from UCSF; when taking a video, I noticed that the helicopter propellers appeared to be stationary and/or slowing moving backward. I assume there is some relation lien this!
misha-wu
is there any mathematical connection between the aliased frequencies and the sampling rate?
sjukurnael
I think of one of the earliest times I saw this phenomena was when I watching a youtuber playing with a fidget spinner, and it looks as though the fidget spinner was stationary or moving backwards than its actual motion. This happens because the frame rate of the video camera can interact with the speed of the spinning wheel to create this illusion. This concept of an "illusion" with frequency aliases can also be used to smooth out these edges, creating the illusion of higher resolution.
YiweiIvy
I wanted to search up on whether human sensors are subject to this effect. And it seems that human sensors are analog, not a digital one, so it doesn't involve sampling in the sense that digital systems do.
JunoLee128
Why does this happen mathematically? Is there a connection between the blue and red frequencies? It makes sense that there would be a periodic pattern when sampling (b/c signal is periodic), but e.g. why does it also look like a sine wave?
I am a little confused how this relates to jaggies. What signal is being made with the triangle example, which creates the jaggies?
Undersampling a triangle's slope function results in a step/binary function (low-freq and jagged)
Another way to see this is that you sampled more times per pixel (ex. 4 instead of 1), we would be able to tell more precisely what percentage of the pixel is included in the triangle. Because we only have one sample point for the jaggies, we can only say it's fully in or fully out of the triangle, rather than 25% or 50% in the triangle. We can use this later on as an antialiasing method to make the pixel color a shade of the full color (ie. instead of red, it'd be light red, which would reduce the aliasing we see).
In case anyone interested, here's another relevant video explaining frequency aliases and providing real-life examples: https://www.youtube.com/watch?v=v7qjeUFxVwQ. It mentions the waggon wheel effect caused by aliasing, which is when moving wheel seems to stand still.
Wow! Great visual; I once saw a helicopter take off from UCSF; when taking a video, I noticed that the helicopter propellers appeared to be stationary and/or slowing moving backward. I assume there is some relation lien this!
is there any mathematical connection between the aliased frequencies and the sampling rate?
I think of one of the earliest times I saw this phenomena was when I watching a youtuber playing with a fidget spinner, and it looks as though the fidget spinner was stationary or moving backwards than its actual motion. This happens because the frame rate of the video camera can interact with the speed of the spinning wheel to create this illusion. This concept of an "illusion" with frequency aliases can also be used to smooth out these edges, creating the illusion of higher resolution.
I wanted to search up on whether human sensors are subject to this effect. And it seems that human sensors are analog, not a digital one, so it doesn't involve sampling in the sense that digital systems do.
Why does this happen mathematically? Is there a connection between the blue and red frequencies? It makes sense that there would be a periodic pattern when sampling (b/c signal is periodic), but e.g. why does it also look like a sine wave?