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from the reduce-the-size-of-your-pron-storage dept.
With unlimited data plans becoming increasingly expensive, or subscribers being forced to ditch their unlimited data due to overuse, anything that can reduce the amount of data we download is welcome. This is especially true for media including images or video, and Google just delivered a major gain when it comes to viewing images online.
The clever scientists at Google Research have come up with a new technique for keeping image size to an absolute minimum without sacrificing quality. So good is this new technique that it promises to reduce the size of an image on disk by as much as 75 percent.
The new technique is called RAISR, which stands for "Rapid and Accurate Image Super-Resolution." Typically, reducing the size of an image means lowering its quality or resolution. RAISR works by taking a low-resolution image and upsampling it, which basically means enhancing the detail using filtering. Anyone who's ever tried to do this manually knows that the end result looks a little blurred. RAISR avoids that thanks to machine learning.
[...] RAISR has been trained using low and high quality versions of images. Machine learning allows the system to figure out the best filters to recreate the high quality image using only the low quality version. What you end up with after lots of training is a system that can do the same high quality upsampling on most images without needing the high quality version for reference.
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(Score: 0) by Anonymous Coward on Thursday January 19 2017, @11:15PM
They're not losing any information. They are adding information by trying to be clever in figuring out what detail is supposed to be there. The point is correct, that you can't add detail that isn't there to begin with. Their deep learning algorithms have been trained on lots of real-life pictures so it makes the best guess what filtering algorithms to use to put information into the image. But keep in mind that the information, the detail, they are putting in isn't in the picture to begin with.
For instance, you can look at a low-resolution image of a circle, and if you recognize it to be a circle, you can redraw it in very sharp and fine detail. However, if what was in that low-resolution image was really not a circle, but a very round ellipse, but you can't tell because of the resolution, you might redraw it as a very fine and sharp circle, but you've added that information yourself.