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New Sampling Techniques Make Up For Lost Data
An unnamed reader writes: "Professors at Vanderbilt and the University of Connneticut have published a non-uniform sampling theory that could yield better quality digital signals than the standard Uniform sampling techniques pioneered by Shannon at Bell Labs.
The Vanderbilt press release and link to the published paper can be found here."
In fact, you're not limited by the Nyquist frequency when you are sampling non-uniformly, so it has some strengths in that respect. However, it has to be more to it than this for it to be news. Can anybody who understands this better than I provide any insights?
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About 7 years ago, I was involved in a research project, trying to use video teleconferencing and doctors for remote diagnosis of patients.
We found that jpeg compression of images made medical diagnosis unreliable. Hairline fractures in x-rays are exactly the kind of small details that tend to get washed away in 'lossy' compression, and the banding caused can lead to false assumptions as well.
The article suggests that this is still a lossy compression with small amounts of data loss. I know Doctors that would take that admission as a condemnation of the technique.
From what I've read, some people seem to be thinking this is some kind of "magic bullet". For example, one comment, which emanated stupidity, was titled something like, "Infinite Zooming" and the implication of the post was that it might be possible with this method to "zoom in" on an image and accurately reconstruct the image. In other words, the idea is you could zoom in on a tiny head on a photograph and accurately reconstruct all of the details.
This, my friends, is complete nonsense. You cannot zoom in on an image and accurately reconstruct further details. To imply that this is possible is to imply that you can add accurately representative data where there was none before.
As for "zooming technology" it is possible to better reconstruct a zoomed-in image, though not any more accurately. For example, when I go into MS Paint and zoom in, it simply blows up all the pixels as larger blocks. This clearly is not good. You could create some kind of algorithm to determine the "shapes" of sharp edges, as well as where gradients where, and scale those up when zooming in...for example, small a circle can be composed of four pixels -- such a technology would scale this up, not as four very large blocks, but as a circle.
But this involves assumptions about what the original pattern was representative of? Was it representative of a circle, or of four large blocks seen from a distance? So you're not really adding data, but just attempting to "zoom in" on an image "better" based on a set of good assumptions which generally work.
Such a thing could be accomplished. Indeed, it already has been accomplished -- in us. When we look at a small photograph and want to draw a poster from it, we don't draw a large, blocky, pixelated image. We are able to tell what things -- such as frecles -- are details to be scaled up in our drawing; what things are gradients -- such as a dark to light gradient going from the near to the far side of a forehead -- to be scaled up and gradiated; and what are sharp borders, to kept sharp -- such as the sides of one's face.
However, even this amazing system we have of reconstructing larger images from smaller one's cannot add detail where there is none. If a woman is freckled with tiny freckles, they won't be visible from 10 feet away; a picture taken from that distance won't show them, and if we wanted to make a portrait of her head based on that picture, we wouldn't know to add freckles.
social sciences can never use experience to verify their statemen
I have a question/theory about nonuniform sampling rates. Okay, sticking with a 44kHz sample rate, will you hear the differeces between 8, 16, and 24 bit samples? Yes, of course. It's common in digital audio to use 16 bit samples to save space, not because it's the ultimate sample size. (While it's arguable the 44kHz rate side of the equation is pretty darn good.) It's subjective and some ears don't need any "more" audio information to be happy, but I see the choice of sample size as more of a variable than the "provable" sufficient rate for 20kHz audio cutoff behing 44kHz. All I'm saying is that there is potentially audible information below 20kHz that isn't getting encoded and recreated not because of sample rate, but because of sample size. For example, if my source material didn't "need" 44kHz througout a song, could the sample rate be trimmed back in places while the sample size was increased? In the end, it's all just a stream of x samples per second, y bits deep. So if a new sampling technique allows us to reproportion (optimize) those two dimensionons in the same amount of overall space, it's possible that better audio will result. Thoughts?