Google roasts Apple on computational images: ‘It’s not mad science’


Even although I’m a long-term iPhone consumer, I listen yearly when Google holds its annual Pixel telephone occasion, as that’s one among two alternatives Android telephone makers must persuade me to make the massive change — Samsung’s Galaxy Unpacked occasion for S-series telephones is the opposite. While Samsung usually pitches cutting-edge hardware — lovely screens, quick wi-fi options, and new cameras — Google takes a unique tack. “The hardware isn’t what makes our camera so much better,” Sabrina Ellis from the Pixel workforce stated at this time in introducing Pixel four. “The special sauce that makes our Pixel camera unique is our computational photography.”

Computational images has actually develop into a significant promoting level for Pixel telephones. The one Pixel three characteristic that made me jealous final 12 months was Night Sight, a Google-developed machine studying trick that immediately restores brightness and colour to dimly lit images. There was one other, much less broadly appreciated Pixel three characteristic known as Super Res Zoom that makes use of a number of exposures to interchange mediocre “digital zoom” efficiency. In quick, high-speed cellular processors and cameras are essentially redefining every day images, and yearly, it looks as if Google is main the way in which.

When Apple advertising chief Phil Schiller received up on stage final month to debate Deep Fusion, a brand new iPhone neural engine trick to extract extra element from 9 exposures, he referred to it as “computational photography mad science,” eliciting laughter and applause from the viewers. But throughout at this time’s Made by Google ’19 occasion, Google researcher and Stanford professor emeritus Marc Levoy fired an attention-grabbing shot again on the marketer: “This isn’t ‘mad science’ — it’s just simple physics.”

Above: Apple advertising chief Phil Schiller makes an attempt to elucidate Deep Fusion, a computational images characteristic present in new iPhones.

Image Credit: Apple

On one hand, I can perceive the place Professor Levoy is coming from: When you’ve spent years growing sensible Google computational images methods comparable to single-lens portrait mode, artificial fill-flash, Night Sight, and Super Res Zoom, being described as a mad scientist — even jokingly — by a fast-following competitor may really feel considerably disrespectful. I can even perceive the need to reply, significantly with a pithy reference to the supposedly primary science underlying the improvements. It was an ideal quote, received my consideration, and shaded Apple, so … mission completed?

But on the opposite hand, the best particulars of those improvements are shifting effectively past the comprehension of common folks, arguably to the purpose the place smartphone launch occasions comparable to Google’s and Apple’s is perhaps finest served subsequent 12 months with separate post-keynote digital camera spotlights. Adding a second lens is quite a bit simpler to elucidate than an AI approach that extracts extra element from a single lens. I believe Schiller’s glib reference to “mad science” was shorthand for “innovative in ways that are as hard to explain as they are to dream up,” and actually, most observers left Apple’s occasion with little to no concept of how Deep Fusion labored or what it did, exterior of “adding more detail.”

Interestingly, that’s the very crux of computational images at this level. The “simple physics” of mixing a number of exposures to extract extra element has develop into not solely a viable technique to generate higher pictures, but in addition a powerful sufficient promoting level to depend upon for annual smartphone updates, and all however kill gross sales of primary standalone cameras. As I famous in an earlier article, Apple’s Deep Fusion does for element what multi-exposure excessive dynamic vary (HDR) pictures did for brightness and colour, utilizing math and machine studying to find out and retain solely one of the best pixels from a number of pictures. Google’s newest additions, comparable to a long-exposure astrophotography mode, Live HDR+ previewing, and dual-exposure captures are all utilizing software program to rival if not exceed options in even the newest and most costly DSLRs.

Machine studying, on-device neural engines, and total enhancements in element efficiency have actually come collectively to revolutionize pocket images. The Pixel workforce may downplay the comparative significance of hardware in its digital camera options, and it’s true that Pixels don’t have as many lenses as iPhone 11 Pro fashions, or as many megapixels as many different Android rivals. But it’s the whole bundle of millisecond-level digital camera sensor and picture processor responsiveness, high-performance neural evaluation, and inventive, well-trained photograph software program that allow common folks to simply “point and shoot” their technique to stunningly clear, detailed pictures. With their telephones.

So right here’s to the mad scientists who’ve made these kinds of improvements potential. It could be easy physics to you as builders, however for these of us who more and more depend upon your computational images strategies in our cameras, the outcomes are certainly more and more seeming nearer to magic than science. And I can’t wait to see what new methods you’ll have up your sleeves on the subsequent present.


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