Google's New Compression Tool Uses 75% Less Bandwidth Without Sacrificing Image Quality (thenextweb.com)

An anonymous reader quotes a report from The Next Web: Google just released an image compression technology called RAISR (Rapid and Accurate Super Image Resolution) designed to save your precious data without sacrificing photo quality. Claiming to use up to 75 percent less bandwidth, RAISR analyzes both low and high-quality versions of the same image. Once analyzed, it learns what makes the larger version superior and simulates the differences on the smaller version. In essence, it's using machine learning to create an Instagram-like filter to trick your eye into believing the lower-quality image is on par with its full-sized variant. Unfortunately for the majority of smartphone users, the tech only works on Google+ where Google claims to be upscaling over a billion images a week. If you don't want to use Google+, you'll just have to wait a little longer. Google plans to expand RAISR to more apps over the coming months. Hopefully that means Google Photos.

Comments

...without sacrificing photo quality

[JasterBobaMereel]

....is a lie, it reduces image quality just in a way you cannot see visually

If all you want to do is look at the image this is fine, but anything else that needs it full quality will be sacrificed

Re: ...without sacrificing photo quality

[Miamicanes]

How well do Google-compressed images deal with enlargement compared to JPEG, JPEG2000, etc? It's nice to say a new algorithm reduces file size without visual consequences, but compression artifacts can manifest themselves in new, unforseen ways. And future upsizing algorithms might end up being able to get better results from one due to "useless" data the other discards.

Case in point: VHS had a nominal resolution of approximately 160x480 or 512 (with color resolution that barely approximated 40x480/512). But with extreme oversampling of a wider tape path (so you also capture unintended sideband artifacts), you can clean up & resample the video in ways that would be frankly *impossible* if your only remaining source copy was literally a 160x480/512 mpeg-1 capture.

This is a big deal for preservation of analog media. It's deteriorating by the week, but for videotape in particular, there's no good way to massively oversample a decaying source in a way that will let us restore it better in the future. What we *need* is a videotape capture device with a dense array of read heads the full width of the tape, in at least two staggered rows (so row 2's sensors are centered between row 1's sensors), so the state of the entire tape can be captured (the dense array is needed because VCRs recorded diagonally via rotating heads to increase the tape speed relative to the read head... it "kind of" worked, but the capture quality with normal VCRs is *profoundly* impaired if the capture VCR's tracking deviates from the recording VCR's tracking... and the recording VCR's tracking ITSELF might have been "wobbly". We now have the ability to make dense read heads, and sufficiently-cheap phase-change magneto-optical storage space (eg, non-LTH BD-R) to do high-density two-dimensional linear capture so the tracking can be handled after the fact via software.

This isn't sci-fi. There are already floppy drive controllers that can use a normal PC quad-density floppy drive to oversample a 5-1/4" c64/apple II/etc floppy (~25 sectors/track, ~35 tracks at 40-track stepping) at 50+ sectors/track and 80 track steppings. They can recover data from old floppy disks that would have been *unreadable* by the original drives & computers **years** ago. And with some floppy mods to give you 160 or 320 track steppings and slow down the rotation speed, even more discs become readable. Not to mention, even the lesser method can trivially overcome disc-based copy protection (most of which depended on storing data in ways that old drives could semi-reliably read, but couldn't reliably/easily write (or wouldn't, if you used the official kernel/OS/BIOS/API).

Anyway, the point is, for capturing decaying analog content from decaying media, compression is BAD if you ever want to be able to restore or enhance it someday.

Re:...without sacrificing photo quality

[BlackPignouf]

+1.

It's really impressive how much a difference sharp eyes make. I like taking close-up portraits with my 85mm f/1.4 on a full frame sensor.
99% of the whole picture is basically completely out of focus. If the other 1% falls on the eyes, the picture looks perfectly sharp.
It's junk otherwise.

Better article

[alexhs]

Summary's links are fact-free ads.
I found this one, that has the merit to link to the arXiv article about the process.

Not compression but restoration

[Anonymous Coward]

The algorithm is not for compression, but for enhancing a low resolution version of the image.

mod me informative if you want

[Anonymous Coward]

WEBM is video, and the momentum didn't die, when you view a "gif" on imgur you're actually viewing a webm, they just decided to use the wrong extension name. WEBM uses the VP9 codec. WEBP on the other hand is just a container format for the VP8 codec, which was derived from how frames were stored in WEBM when it was VP8-based. WEBP could upgrade to VP9 without changing the format, however it would require developers to link against a new library. And, the changes to VP9 were mostly advancements in moving video so they decided not to bother with static images. RAISR isn't a format, from what I understand, it's an algorithm that requires an entire cloud-based platform running a machine learning heuristic designed to scale images in a way that doesn't look quality but saves on bandwidth. My guess is that it could be applied to any image format eventually. Not only are these technologies open source, and in some ways technically superior to competitors, but they're uninhibited by patents. The world owes Google a big favor for the work they've done.