andypski,
I fully agree that with a tent filter Nyquist's rate doesn't fully apply. But what's the 'correct' number of samples then? As far as I know, there should be an infinite number of samples to avoid aliasing with this filter. So that doesn't make sense. What does make sense is to regard a tent filter as being a good approximation of a sinc filter. And with this assumption, we can perfectly specify that the minimum number of samples should be in accordance to Nyquist's rate.
You simply have to draw the line somewhere. And as far as I can tell from other people's description, keeping very close to that line works out fine for NVIDIA. Crossing the line, by using a negative LOD bias, causes problems, but that's perfectly according to the math. Once you go below Nyquist's rate, no matter if you use a sinc or tent filter, you get aliasing.
If you really want better sampling than what NV40 does, I think a more effective approach is to improve the filter shape. The advantage of using more samples is very minimal. And if more samples is what you want, then you need super-sampling horizontally and vertically.
I fully agree that with a tent filter Nyquist's rate doesn't fully apply. But what's the 'correct' number of samples then? As far as I know, there should be an infinite number of samples to avoid aliasing with this filter. So that doesn't make sense. What does make sense is to regard a tent filter as being a good approximation of a sinc filter. And with this assumption, we can perfectly specify that the minimum number of samples should be in accordance to Nyquist's rate.
You simply have to draw the line somewhere. And as far as I can tell from other people's description, keeping very close to that line works out fine for NVIDIA. Crossing the line, by using a negative LOD bias, causes problems, but that's perfectly according to the math. Once you go below Nyquist's rate, no matter if you use a sinc or tent filter, you get aliasing.
If you really want better sampling than what NV40 does, I think a more effective approach is to improve the filter shape. The advantage of using more samples is very minimal. And if more samples is what you want, then you need super-sampling horizontally and vertically.