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> -- would, well, simply violate physics. Nope. Aliasing can be eliminated by low-pass filtering (blurring) the input signal before sampling it. This could be

• ChrisLomont 4y ago
  Yep. >Aliasing can be eliminated by low-pass filtering (blurring) the input signal before sampling it. Nope. I have a PhD in math, and have been doing filtering stuff for decades. You do not eliminate aliasing, you push the error out of the visible spectrum - end of story. The naive, physically impossible undergrad textbooks might say differently, just like they'd teach Newton as gravity and ignore relativity, or teach the ideal gas law and ignore that real gasses are vastly more complex in behavior. This is Wikipedia level knowledge: from the low-pass filter page [1]: "However, the ideal filter is impossible to realize without also having signals of infinite extent in time, and so generally needs to be approximated for real ongoing signals, because the sinc function's support region extends to all past and future times." So no ideal filter, sorry. That means leakage, which happens in every part of engineering (and is forced by physics, despite you not wanting to believe it). Ideal filters are math, not physics. And this level is vastly below what is done in industry, what theory (and physics) forces onto real world filtering. Once you discretize the signal, even after filtering, you have introduce lots of noise in lots of frequency ranges. The simple reason is that you are forced to perturb real sample values (which would require infinitely precise measurements) to the discrete ones your sensor records. These perturbations do not fit your frequency requirements. Don't believe it? Take a photo, apply your "ideal" low pass filter, then fit the resulting image using sin and cos waves of frequency no higher than than your filter - and you'll see it's impossible. You can only approximate it - and that is empirical proof you can do at home. Another entire level that undergrad level understandings miss is that pretty much zero of the theorems you think apply actually do - because pretty much every one uses infinite precision samples (Nyquist, for example), infinite support reconstruction (physically impossible), and so on. In practice all of these things leave noise in various parts of frequency spectrums. So, still want to argue that filters would somehow remove aliasing issues? Especially over a sensor with the number of samples and precision that a modern Sony sensor has? >you are experiencing alias-free sound and image processing all day long You are confusing Nyquist with reality - and ignoring that Nyquist doesn't even apply to audio (or video) - it's only an approximation to what is really happening. As an approximation it's useful, but the requirements of the theorem are not met in reality and the reconstruction is not met in reality. Don't believe me? Go look at the proof for the Nyquist theorem, and pay attention to all the requirements that no physical device can meet. it's the frictionless spherical cow of audio processing. One glaring area it fails is the need for the infinite support sinc filter - which again is why real engineering is not so simplistic. [1] https://en.wikipedia.org/wiki/Low-pass_filter https://en.wikipedia.org/wiki/Low-pass_filter