Fractal compression is a lossy compression method for digital images, based on fractals. The method is more suitable for textures and natural images, based on the fact that parts of an image often look like other parts of the same image. Fractal algorithms convert these parts into mathematical data called "fractal codes" that are used to recreate the encoded image. To make fractal compression, the image is divided into sub-blocks. Then, for each block, the most similar block if it is in a version of the medium-sized image and stored. This is done for each block.
Then, during decompression, the opposite is done iteratively to recover the original image.
With fractal compression coding it is extremely expensive from a computational point of view due to the search used to find the similarities between them. The decoding, however, is quite fast. While this asymmetry has so far made it impractical for real-time applications, when the video is archived for distribution from disk storage or file download, fractal compression becomes more competitive.
With common compression ratios, up to approximately 50: 1, fractal compression provides similar results to DCT-based algorithms such as JPEG. At high compression ratios, fractal compression can offer superior quality. For satellite images, relationships of more than 170: 1 have been achieved with acceptable results. Fractal video compression ratios of 25: 1-244: 1 have been achieved at reasonable compression times (2.4 to 66 sec / frame).