New Google machine learning method reduces your mobile data traffic by enhancing low-res images


Modern cameras are getting better and better. Contemporary DSLR devices and smartphone cameras alike are capable of capturing ultra-high res pictures, which allow us to see our World in the most detailed of ways.

However, a larger resolution usually leads to a bigger image size, and this fact does increase our overall data usage and the time it takes for a picture to load. You may say that this classifies as a First World problem, but it is a valid concern for many people that live in areas where data plans are pricey, or the internet is spotty.

Fortunately, Google appears to have invented a new technology that may be the salvation to our bandwidth troubles. Called RAISR (short for Rapid and Accurate Image Super Resolution), this new technique uses machine learning to produce high-quality versions of low-resolution images. RAISR differs from traditional upscaling approaches, as upscaling usually only adds more pixels to a low-res image, which might produce a less pixelated version of it, but does little in terms of bringing out more vivid and sharp details.


With this new method, Google takes a completely different approach, as it uses machine learning to train RAISR with pairs of images that consist of a low and high quality version of the same image. The software then finds filters that are selectively applied to each pixel of the low-res version, which produce a level of detail that is comparable to the original.

Google has implemented RAISR in its Google+ app for some Android handsets, and the results have been quite impressive. Essentially, the new method can reduce data usage per image viewed by up to 75%, as RAISR requests a version of the original picture that only has a quarter of the pixels, and then enhances and enlarges it to produce the same quality. On average, this slashes overall bandwidth usage by about a third.

Although this approach is currently in use only with Google+, Google product manager John Nack said that his team is planning to "roll this technology out more broadly". RAISR is already being applied to Google's photo library and is transforming 1 billion images per week, so it could possibly be employed to trim data usage for search results, Play Store graphics, YouTube thumbnails and other Google services. 


via cnet

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3 Comments

1. shahrooz

Posts: 792; Member since: Sep 17, 2013

This is impressive. well done elgoog.

2. mafiaprinc3

Posts: 585; Member since: May 07, 2012

Kudus Google

3. Tziggy14

Posts: 624; Member since: Sep 02, 2014

They need to expand this to Google Photos and Google Maps.

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