It is time to interpolate! Interpolation is a way to add pixels that aren't really there to make an image print properly at sizes larger then normal. Let's look at an example to get an idea of what this is all about:

   Lets say you have an image that is 1000x500 pixels in size, and your printer prints at 250dpi, and you print the image at its 'natural' size (not shrunk or expanded). The print size will be 4 inches by 2 inches. But what if you want everything to remain the same as above, and you want to print that image at 8 inches by 4 inches? It doesn't take a math major to see that something has to give. Either the print resolution has to drop to 125dpi or you need more pixels. Well, that's what interpolation does. It fills in the missing pixels (for the above example you would need four times as many pixels- twice as wide and twice as tall; 2x2=4). That's a lot of information to be made up. Why bother? Sure, you can just make the image four times larger and print it, but the results will be less than satisfying. The image will look quite pixelated- blocky.

   There are a number of different interpolation algorithms, and depending on how 'smart' the interpolation method is in your printing application will greatly affect image quality. Interpolation problems show themselves in different ways. Sometimes you will notice a 'blockiness.' Small areas of low resolution that were quite dark in the original image (like a shadow on a rock) will turn to larger black rectangles. Another place to look is fine lines that run at angles such as high contrast edges between a building and the sky, power lines, hand rails, and such. These turn into 'zippers' or 'ziggy-zaggies' when poorly interpolated.

   Take a look at these example images. They will help explain interpolation a bit:

	Let's make believe that this is a line in a digital image (well, it actually is).
	If we view that line in a larger size (say 300% of the original size) the pixelization (zipper edges) become more apparent. We have all seen this in digital images when we try to view or print them using too much magnification or enlargement.
	This is roughly what Interpolation tries to do. It uses mathematical computations to figure out how to make the larger image look like the original and by changing pixels it can help remove the pixelization (the green represents what the interpolation would try to do, but the green would would actually be blue).
That would seem to make interpolation the great digital image savior, but it doesn't always work that way. The quality of the final image depends on a lot of factors including the resolution of the original image, how much larger it is going to be made, and what interpolation method is used.
	Here is an example of an image that was printed at a size quite a bit larger than the original and without interpolation.
	Here is that same image but this time it was printed with interpolation. Although the zaggies are gone, the image became quite soft. Choosing an interpolation method that is not quite so severe as the one used here might have improved this image. The better applications allow the user to choose among several interpolation methods.
So how does interpolation do all this? A complicated series of mathematical computations are done within the application, usually as the image is being spooled to print. The interpolation algorithm looks at one pixel, and then all the pixels in the surrounding area, and then decides what pixels to change. By the time I got through understanding all the details and explained it to you, we would be sharing our oatmeal in the home, so let's just leave the details to the experts and just worry about image quality.

   How do you choose an interpolation method that is best for you? Some applications don't give you a choice. When you do have a choice, experiment. Variables such as what printer, paper, ink, and printing application you use along with the image itself will dictate what method to use.