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Honest question: If a pixel is a point sample, why do all the down-sampling algorithms that look good involve some form of averaging the values of neighboring p

Honest question: If a pixel is a point sample, why do all the down-sampling algorithms that look good involve some form of averaging the values of neighboring pixels? Wouldn't it be more accurate to pick one sample and throw away the rest?

jstelly 10y ago
This is a fairly deep topic so your question has a really long & detailed answer. The simple answer is "aliasing." The concept is that picking one sample for each pixel makes an aggregate image that could be confused with other images, whereas a good downsampling algorithm only represents a (lower frequency, bandlimited) version of the original image.
dahart 10y ago
It's a good question. The name for that - pick one sample, toss the rest - is "nearest neighbor sampling". Whether it's accurate depends on what you are trying to achieve, but the short answer is that in general it is not the most accurate, and it can cause a lot of problems. But your intuition isn't wrong. Nearest neighbor can be a bit more "crisp" feeling on certain images, especially natural photos. A lot of photos have enough blurriness in the 1-3 pixels range that nearest neighbor sampling is a good choice if you're just resizing an image by a factor of 2. But to see why nearest neighbor isn't the best in general, imagine down-sampling a checkboard and what happens as individual checkers become smaller than a pixel. You'll get moire patterns if you don't filter. You'd also expect pixels to turn grey as multiple checkers land there, and maybe not stick to only black or white. Interestingly, some "sharpening" and up-sampling algorithms do the opposite - they subtract the value of surrounding pixels, rather than add. Those are assuming that some averaging already took place, and trying to un-average, if you will, to restore the original point sample.
munchbunny 10y ago
The short answer is that our eyes and brains don't work that way. The long answer is... really long.

jstelly 10y ago

This is a fairly deep topic so your question has a really long & detailed answer. The simple answer is "aliasing." The concept is that picking one sample for each pixel makes an aggregate image that could be confused with other images, whereas a good downsampling algorithm only represents a (lower frequency, bandlimited) version of the original image.

dahart 10y ago

It's a good question. The name for that - pick one sample, toss the rest - is "nearest neighbor sampling". Whether it's accurate depends on what you are trying to achieve, but the short answer is that in general it is not the most accurate, and it can cause a lot of problems. But your intuition isn't wrong. Nearest neighbor can be a bit more "crisp" feeling on certain images, especially natural photos. A lot of photos have enough blurriness in the 1-3 pixels range that nearest neighbor sampling is a good choice if you're just resizing an image by a factor of 2. But to see why nearest neighbor isn't the best in general, imagine down-sampling a checkboard and what happens as individual checkers become smaller than a pixel. You'll get moire patterns if you don't filter. You'd also expect pixels to turn grey as multiple checkers land there, and maybe not stick to only black or white. Interestingly, some "sharpening" and up-sampling algorithms do the opposite - they subtract the value of surrounding pixels, rather than add. Those are assuming that some averaging already took place, and trying to un-average, if you will, to restore the original point sample.

munchbunny 10y ago

The short answer is that our eyes and brains don't work that way. The long answer is... really long.