4 ms·

I'm assuming this is predicated on the drone being absolutely still and that nothing in the scene (leaves on trees) is moving? Is that even realistic?

by Sargos 9y ago

godelski 9y ago
If you do a regular sequence of major changes, you could pick that signal out. This would create a larger and more regular signal in the data than the random noise from leaves. I don't think you could accurately predict observation with just one set of environmental changes.
Kalium 9y ago
Generally speaking, inter-frame video compression uses an amount of data directly related to the amount that things change from one frame to the next. So while noise like leaves moving shows up as small changes moment to moment, larger changes are still realistically visible. My younger years spent screwing around with video for a hobby are coming back to me now.
kmm 9y ago
Shouldn't simple linear movement of a scene still compress really well?
- godelski 9y ago
  Yes. The compression works by analyzing changes in the screen. A simple example of this would be if you are displaying data. Say you are taking real time data and it is scrolling across the screen. Your display moves the data to the left, drops off what moved out of the frame, and adds what is new. Pictures are more complex, but the idea is the same. Only take what is new.
natosaichek 9y ago
Well, It assumes that the amount of delta you can create in the transmitted image is sufficiently large that you can discern it from other sources of random variation in the image. So if the drone is moving around dramatically and there are huge deltas in the image, making your windows opaque / translucent repeatedly may not be sufficient signal. But if the drone is observing a dark compound and you flash lights everywhere repeatedly, it might not matter that the drone is moving around a lot - there might still be a dramatic change in the data transmitted by the drone. Ultimately you're putting some signal into a potentially noisy channel and trying to reconstruct whether or not your observations contain that signal. The example is simple enough that normal human eyeballs looking at the output can detect it. Using more sophisticated techniques might be able to tease out smaller signals from noisier channels.

I'm assuming this is predicated on the drone being absolutely still and that nothing in the scene (leaves on trees) is moving? Is that even realistic?

by Sargos 9y ago

I'm assuming this is predicated on the drone being absolutely still and that nothing in the scene (leaves on trees) is moving? Is that even realistic?

godelski 9y ago

If you do a regular sequence of major changes, you could pick that signal out. This would create a larger and more regular signal in the data than the random noise from leaves. I don't think you could accurately predict observation with just one set of environmental changes.

Kalium 9y ago

Generally speaking, inter-frame video compression uses an amount of data directly related to the amount that things change from one frame to the next. So while noise like leaves moving shows up as small changes moment to moment, larger changes are still realistically visible. My younger years spent screwing around with video for a hobby are coming back to me now.

kmm 9y ago

Shouldn't simple linear movement of a scene still compress really well?

godelski 9y ago
Yes. The compression works by analyzing changes in the screen. A simple example of this would be if you are displaying data. Say you are taking real time data and it is scrolling across the screen. Your display moves the data to the left, drops off what moved out of the frame, and adds what is new. Pictures are more complex, but the idea is the same. Only take what is new.

natosaichek 9y ago

Well, It assumes that the amount of delta you can create in the transmitted image is sufficiently large that you can discern it from other sources of random variation in the image. So if the drone is moving around dramatically and there are huge deltas in the image, making your windows opaque / translucent repeatedly may not be sufficient signal. But if the drone is observing a dark compound and you flash lights everywhere repeatedly, it might not matter that the drone is moving around a lot - there might still be a dramatic change in the data transmitted by the drone. Ultimately you're putting some signal into a potentially noisy channel and trying to reconstruct whether or not your observations contain that signal. The example is simple enough that normal human eyeballs looking at the output can detect it. Using more sophisticated techniques might be able to tease out smaller signals from noisier channels.