3 ms·
>RAISR produces results that are comparable to or better than the currently available super-resolution methods, and does so roughly 10 to 100 times faster, allo
by gwern 10y ago
>RAISR produces results that are comparable to or better than the currently available super-resolution methods, and does so roughly 10 to 100 times faster, allowing it to be run on a typical mobile device in real-time....For example, in addition to improving digital “pinch to zoom” on your phone, one could capture, save, or transmit images at lower resolution and super-resolve on demand without any visible degradation in quality, all while utilizing less of mobile data and storage plans.
> In our proposed approach, the run-time is more than one to two orders of magnitude faster than the best competing methods currently available, while producing results comparable or better than state-of-the-art.
From the blog post & article, I think the idea is that you ship a small CNN (a few MB model size, which will accordingly run in realtime on mobile GPUs) to the Android device, and then when browsing, it downloads a small thumbnail (cheap on bandwidth, quick to download), and upscale it as necessary locally on the device.
- jacobolus 10y agoIn general they’d get better bandwidth savings for the same quality and decode effort by shipping the higher-resolution image with more aggressive image compression (using http://bellard.org/bpg/ http://bellard.org/bpg/ or something, not JPEG, and ideally running the decode in hardware). Seems like a dubious method overall. The main impression I get is “woo! neural nets can do everything! let’s use them everywhere!” I don’t believe their claim that this is “comparable or better than the state of the art” for image upscaling without much better evidence than they provide in their paper.