eli5: Why do computer screens appear distorted in pictures, unless you zoom in?

Question

Whenever you take a picture of a computer screen, the screen looks distorted, but when you zoom in closer on the picture, it focuses a lot more

In: Technology

Answer 1

The reason is [Nyquist–Shannon sampling theorem](https://en.wikipedia.org/wiki/Nyquist–Shannon_sampling_theorem).

For an ELI5 explanation, this phenomenon has the same cause (mathematically) as the “videos of spinning wheel” phenomenon. You know when you record the image of a car with their wheel? If the wheel rotate at the right speed in real life, the video will show the wheel rotating *backward*.

This is because we are subsampling a continuous series of information, ie. capturing image at a discrete point in time. Then when we watch the video, we are reconstructing this continuous information based on the subsamples, which require us to interpolate between the samples. Unfortunately, this interpolation can be wrong: if 2 consecutive pictures showed that the wheel turned 3/4 of a round forward, the interpolation would be that the wheel turn 1/4 of a round backward.

This problem can be avoided by increasing the frequency of the subsample. For example, if you take video at 3x frame rate, a 3/4 round forward turn into 3 consecutive frames of 1/4 round forward, and we will reconstruct the motion correctly.

Same thing about camera and screen, but now we have a 2-dimensional subsampling problem instead. The pixel on the screen never 1-1 match the tiny sensors inside the camera, so we end up where we are picking up the pixels in the wrong distribution: some pixels have more influence than other, or a straight row of pixels could influence multiple different rows of sensors. This doesn’t affect just video screen, but can affect any patterns that are small and changing rapidly, such as a brick wall from a distant.

Then there is also the second layer of subsampling problem, when you view a camera picture on your screen. In this case though, it’s technically possible to match the pixels of the picture to the pixels on the screen. But if it doesn’t you also run into the same problem.

The answer is still the same, make more sampling, then you can faithfully reconstruct the original signal.