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One of the biggest advantages of fractal image compression is its potential to achieve extremely high compression ratios.

Traditional image compression methods generally reduce file size by:

•removing repeated data,

•simplifying frequencies,

•or eliminating visually insignificant information.

Fractal compression can go much further because it often stores only mathematical transformation parameters instead of large quantities of pixel information.

In many natural images:

•textures repeat,

•patterns recur,

•and structures resemble one another across different scales.

The algorithm exploits this redundancy very aggressively.

For example:

•one leaf may resemble another,

•one cloud pattern may resemble another,

•or one section of a mountain texture may resemble another after scaling and rotation.

Instead of storing each region separately, the encoder stores:

•references,

•geometric transformations,

•brightness adjustments,

•and scaling coefficients.

This can dramatically reduce the amount of required storage.

In theory, highly self-similar images can be compressed far more efficiently than with classical transform-based codecs.