Online image decompressor2/3/2024 ![]() ![]() On medical images, PNG and WebP perform relatively poorly (note: it looks like the most recent development version of WebP performs a lot better!) while BPG and JPEG 2000 work well (see middle plot). On photographs, PNG performs poorly while WebP, BPG and JPEG 2000 compress well (see plot on the left). Here is an example to illustrate the point. The conclusion? FLIF beats anything else in all categories. Here is a selection of different kinds of images and how each image format performs with them. More recent formats like WebP and BPG do not solve this problem, since they still have their strengths and weaknesses.įLIF works well on any kind of image, so the end-user does not need to try different algorithms and parameters. It can be tricky for non-technical end-users. 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. You are supposed to know that PNG works well for line art, but not for photographs. It has slightly improved since then.) Works on any kind of imageįLIF does away with knowing what image format performs the best at any given task. ![]() (Note: the graph below is for an early version of FLIF. FLIF clearly beats other image compression algorithms. The results of a compression test similar to the WebP study are shown below. ![]() Here are some of the key advantages of FLIF: Best compression The file format has been standardised and versioned. (or 19% on average, including 16-bit images which are not supported by WebP and BPG). Then FLIF still beats that by 12% on a median corpus
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