An alias, in signal processing which is what we're dealing with when we are talking about images, is when a signal is sampled at a resolution that makes it impossible to recreate the original signal exactly.

Take this 1-dimensional case:



The original signal is the purple sine wave, and the blue dots are where it has been sampled. The blue line is the recreation of that signal based purely on those four samples - it is an alias of the original signal.

In 2D image processing, this aliasing tends to appear as "jaggies":



The line in image on the left is meant to be a perfectly straight line, but scaling it up has made it look less like a line. One way of improving the look of the line would just be to sample it at a higher resolution, the ideal resolution is called the Nyquist frequency, which is half of the highest frequency in the original signal. (In the case above there is no way of reproducing the signal perfectly because of its infinite frequency components).

Anti-aliasing (AA) is a general term for improving the recreation of a signal, without sampling it at a higher frequency. So linear interpolation (or a box filter) is a simple AA technique, but there are myriad other filters that have different properties and have to be chosen depending on the type of signal you're trying to recreate, the amount of memory/processing power you have to hand, and the sort of results you'd be happy with.

What exactly is 'anti-aliased image generation'?
Pretty simple question, but something I haven't been able to find out. Who was the first person to describe the idea of a pointer? The abstract concept itself?
