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Moreover, FLIF supports a form of progressive interlacing (essentially a generalization/improvement of PNG's Adam7 interlacing) which means that any prefix (e.g.partial download) of a compressed file can be used as a reasonable lossy encoding of the entire image.As a result, the overhead of interlacing is small, and in some cases (e.g.photographs) interlaced FLIF files are even smaller than non-interlaced ones.Read more about FLIF and Responsive Web Design Try the Poly-FLIF interactive demo by hrj! BPG and JPEG 2000), FLIF is completely royalty-free and it is not known to be encumbered by software patents. FLIF uses arithmetic coding, just like FFV1 (which inspired FLIF), but as far as we know, all patents related to arithmetic coding are expired.Other than that, we do not think FLIF uses any techniques on which patents are claimed. There are a stunning number of software patents, some of which are very broad and vague; it is impossible to read them all, let alone guarantee that nobody will ever claim part of FLIF to be covered by some patent.In each of these three examples, FLIF performs well — even better than any of the others.

PNG with Adam7 interlacing), but FLIF is better at it.You are supposed to know that PNG works well for line art, but not for photographs. More recent formats like Web P and BPG do not solve this problem, since they still have their strengths and weaknesses. On photographs, PNG performs poorly while Web P, BPG and JPEG 2000 compress well (see plot on the left).For regular photographs where some quality loss is acceptable, JPEG can be used, but for medical images you may want to use lossless JPEG 2000. FLIF works well on any kind of image, so the end-user does not need to try different algorithms and parameters. On medical images, PNG and Web P perform relatively poorly (note: it looks like the most recent development version of Web P performs a lot better!It is a variant of CABAC (context-adaptive binary arithmetic coding), where instead of using a multi-dimensional array of quantized local image information, the contexts are nodes of decision trees which are dynamically learned at encode time.This means a much more image-specific context model can be used, resulting in better compression.

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