![]() It would be unjustifiably and indefensibly cynical of us to suggest that because the format is to be removed from Chrome 110, that is why Google is willing to open source the tech, so we won't. Because this feature uses the new JPEG-XL image format – the one that Google said it would remove from future versions of Chrome back in October. The algorithm is described in a post titled "Open sourcing the attention center model" on Google's open source blog… and there lies the irony, and that is the reason that the preceding paragraph used the conditional mode. Sick of AI engines scraping your pics for facial recognition? Here's a way to Fawkes them right up.People outperform computer programs for 'compressing' pix.Third time's a harm? Microsoft tries to get twice-rejected encoding patent past skeptical examiners.Google kills forthcoming JPEG XL image format in Chromium.We recommend playing with this demonstration, so long as you have a Chrome-based browser and you enable its experimental JPEG-XL image renderer: go to chrome://flags, search for jxl and enable it. The illusion would be that a perfectly sharp version appeared right at the start. If it worked well enough, you probably wouldn't even notice it happened. Coder: encoder codes the symbols using the model while decoder. Once those parts are fairly sharp, then the rest is filled in, the relatively boring bits last of all. Modeler: Its purpose is to condition the image data for compression using the knowledge of data. Then as your attention roams around the picture, the algorithm has guessed where your eyes will go next and fills in more detail in those bits next. The idea is that a low-res version of the whole image appears right at the start, and by the time that your visual cortex has decided where to point your pupils, that area of the image is already getting sharpened up. But now it's more about mobile and wireless connections, whose speed not only varies wildly but unpredictably. If you're old enough to remember watching GIF images gradually appear, line by line, as they downloaded over a dial-up modem, you will immediately grasp the appeal. The new attention center model does something different: It uses machine learning to attempt to identify which parts of an image will attract a human's attention first, so that it can selectively decompress those regions first.
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