If you visit once a week, you can download the weekly update file if you visit once a month, you can download the monthly update file. In this way, you can visit the formula database as frequently as is convenient for you. The third pack contains all formulas, regardless of how recently they were updated. The second pack contains all the formulas updated within the past month (and thus contains everything that’s in the first pack, too). The first pack contains all the formulas updated within the past seven days. The formulas are sorted into download packs based on how recently they were modified. The end consumer wouldn't see much difference though, because for a decent results these patches would have to be small, and any magnification will create image artifacts, as fractal patches are nothing more but approximations.If you’re just interested in obtaining the latest versions of Ultra Fractal formulas, click any of the three download links on the main page. You will still have different resolutions, that will just measure patches instead of pixels. I don't need to say that it is not a losless conversion. The compression algorithm stops as soon as it finds more or less decent looking approximation of the original picture. It also takes a long time for calculation, because due to the chaotic nature of fractals the brute force search of parameters has to be performed. The approaches that I have encountered first subdivide the input into many small patches, each of them to be converted individually. Inverse transform in the most cases is impossible(not all images are fractals), and even if you have a fractal, it is computationally impossible to do inverse transformation and get the source formula.Īs most pictures are not fractals, you will need to make a trade-off between speed, compression rate, and accuracy. There are several reasons why this will not happen in foreseeable future.Ĭomputers now have just enough computational power to generate pictures of mandelbrot/julia sets at a decent rate(basically transform mathematical formula/parameters to a picture), and even then these algorithms are better run on GPU. Where fractal compression is considered the worst algorithm among five different options for face images used for facial recognition. While fractal compression can achieve higher compression, the very low accuracy it delivers is not really worth the effort. While the results are different for different types of medical image, it's fairly clear that for a given compression ratio the DCT has higher quality (higher PSNR). Unambiguous references on this topic are tricky since image compression is highly application specific but you can look at this paper comparing fractal "quad-tree" compression and DCT compression for medical imaging: The end result is that on average, fractal compression is usually lower quality and much higher CPU usage than DCT. Fractal compression has huge blowouts in CPU encoding time (hundreds of times slower to encode than DCT) and output image size (often worse on average than DCT). Frankly though, this is just not the common case. While DCT is not necessarily the best algorithm in all cases, it has such excellent "common case" compression performance (in terms of image quality and CPU encoding speed) and is so heavily refined (due to 30 years of usage) that theoretical "best case" improvements in fractal compression, wavelet transforms, chirplet transforms and other algorithms have never proven to be worth the effort.įractal compression requires self-similar elements for the algorithm to excel. DCT was first popularised with JPEG and is now the main algorithm that underpins almost all video encoding. Image compression is completely dominated by an algorithm called "DCT" (discrete cosine transform). Second point: lots of algorithms have amazing "best case" performance but if the algorithm can't excel in the "common case" and doesn't have good performance in its "worst case" then it doesn't really matter. Think about a vector image where half the control points have been thrown away – might be okay at low resolution but will look sloppy/messy/inaccurate at high resolution. While the algorithm is resolution independent, you still need to include enough accuracy to look good at a given size. ULTRA FRACTAL CODEC 720PFirst point: it's wrong that there's no need for 720p versus 1080p in fractal compression.
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