Gainy image compression

I understand that the use of fractal techniques to compress images is largely abandoned, i assume because other algorithms are faster, make smaller files and so forth. However, it seems to me that fractal image compression would still be useful if one wanted to add information to an image rather than just subtracting it, and that this information might even sometimes have a tendency to be close to being accurate.

So this is my idea:

Scan an image for patterns which show at least two levels of self-similarity, for instance a star field, a tree, a network of blood vessels or a pebbly beach. Have the algorithm determine which areas exhibit these features and also whether they do so in more than one way - for instance, a root system in sandy soil might have a definite distribution of particle sizes between roots which have a definite pattern of branches. Separate all areas of the image which cannot be processed in this way, such as a clear blue sky, recording any colour gradients in these areas and store them as functions. Anything else, which is unpredictable in this manner, separate out and store separately in a more conventional form.

When the image is to be displayed, the actual pixels of the image often need not be used. Instead, the fractal and gradient patterns are reproduced. With no zooming, the image looks the same as the original and doesn't make any attempt to make anything up. If zooming does take place, the self-similar tendency of the fractals and the predictability of the gradients is exploited to introduce new details to the image. A simple example would be a photograph of a fern. Zooming in on the frond would provide an infinite series of metafronds no matter how far zoomed in the image is. Similarly, a tree might have leaves of a particular shape which are predictable from the shape of the whole tree. This could get more interesting when two features of an area interact, because new structures not closely resembling either might emerge, but which really exist. Zooming into a night sky could look like the Hubble Deep Field.

Faces are said to be easily compressible and storable in databases using very few bytes - i seem to recall about forty. A blurred face could be similar. The lower part of a face might be predictable from the upper and vice versa. Similarly, a self-similar area which disappears behind something in the foreground could continue with the same pattern, and a sky, for example, which had the same shade of blue on either side of a foreground object would probably be the same behind it, though it might have a cloud.

The result is unreliable but interesting and possibly helpful gainy image compression which might reveal details of images behind objects, deblur faces and show smaller details of the whites of one's eyes or the vein pattern in a pot plant. Often wrong but infinitely zoomable.