shirley@uicsrd.csrd.uiuc.edu (11/15/89)
I have a theoretical question for you rasterheads:
How much filtering should we do on our images? Given a VERY high
resolution display, we should certainly kill all freqencies greater
than 2 pixel widths (especially in the vertical if you have
an interlaced display).
In the real world, we can still see the individual pixels, and sometimes
this is useful. As an example suppose we have a function
f(x) = black floor(x) is even
white floor(x) is odd
Further suppose we have a 1000**2 pixel display, and x is in pixel coordinates.
This would have 1000 alternating vertical stripes of white and black, with
each stripe one pixel wide. If we filter out the high freqency
components then we'd have a grey screen. Intuitively it seems that
we shouldn't filter.
Now suppose we have the function
f(x) = black floor((1.001*x) is even
white floor((1.001*x) is odd
This will give us 999 vertical stripes. If we area sample each pixel,
then we'll have stripes on the left of the screen, fading into grey in the
middle of the screen, going back to stripes on the right. This gives us
a classic aliasing effect, and the party line is that we should filter.
My question is whether this filtering is worth the trouble. Is the
aliasing effect described above that bad in real world scenes? How often
does this come up in real pictures, and how important is it?
Thanks,
pete shirley
shirley@m.cs.uiuc.edugg10@prism.gatech.EDU (Galloway, Greg) (11/16/89)
In article <44100007@uicsrd.csrd.uiuc.edu>, shirley@uicsrd.csrd.uiuc.edu writes: > I have a theoretical question for you rasterheads: > ... > My question is whether this filtering is worth the trouble. Is the > aliasing effect described above that bad in real world scenes? How often > does this come up in real pictures, and how important is it? Rob Cook's "Stochastic Sampling in Computer Graphics" from ACM Transactions on Graphics, January 1986, is about the best reference on aliasing artifacts in point sampling systems. It discusses using a Monte Carlo method of randomizing your samples to substitute aliasing artifacts for noise which is less objectionable to the eye. It also discusses the pros and cons of analytic methods (filtering) versus discrete (sampling) methods. It should have the information you need. Greg Galloway Georgia Institute of Technology gg10@prism.gatech.edu