For lossy compression, the process of compression throws away high information of the original but approximately matches its features to regular (simple, low information, standardized) family of patterns. This process is related to how a Fourier series can represent any function by a sum of sines and cosines having particular coefficients and frequencies. Essentially the compressed file is only including the coefficients, frequency parameters of the series function along with much smaller bitmaps and tossing out the original image entirely.
The original is later reconstructed from these coefficients and bitmap data alone by reversing the process.
A series of various frequency raised-cosine functions is used in JPEG and MPEG instead of a Fourier sine/cosine because it's positive only and it has a roll-off factor that works better with 2-D image representation. A lot of this relies on the fact that your eye actually sucks at certain kinds of image recognition so it can be fooled, within reason, with a cheap imitation of the image.
http://en.wikipedia.org/wiki/Raised-cosine_filter
http://en.wikipedia.org/wiki/File:Raised-cosine-filter.pngThe difference in low or high fidelity settings in a JPEG have to do with how many frequency terms in the series representation you are going to include in your file. Fewer means smaller file size but worse fidelity because you are using fewer higher order terms (each amplitude coefficent is a number that has to have space in the file) and vice versa.
You see these represented raised-cosine patterns, and the corruption of the original data, when you save to JPEG with low fidelity: you get the blocky artifacts - those blocks are the 2-dimensional, low order raised-cosine terms. Adding higher order, higher frequency (smaller blocks) increases the fidelity of the representation.
Instead of a raised-cosine, you could also use a fractal kernel with multiple frequencies and save the amplitudes and patch bitmaps, which is what fractal compression does.
The very short answer: the reconstructed information content comes from the latent information and redundancy of the original image that allowed simple patterns to used in the compression to fit "well enough for the human eye". Not something for nothing actually.