Pod Insights The Vergecast Marc Levoy on moving from Google to Adobe and the ethics of computational photography About this episode The Verge's Nilay Patel talks with former Google engineer Marc Levoy about his move to Adobe, the state of the smartphone camera, and the future of computational photography.
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1:13 Nilay Patel? Hey everybody, it's Sinead from the Vergecast. Really fun interview episode this week. Adobe VP and fellow Mark Lavoie joins us to talk about smartphone photography, specifically computational photography. You might recognize Mark's name. He was one of the senior scientists and engineers on the Google Pixel camera team that really brought computational photography to the masses, really changed the industry, changed what we think a smartphone can do when taking a photo. Computational photography, if you don't know, is when the phone takes a series of images and merges them together to make a final image. That's how you get those HDR effects. That's how you get tone mapping. That's how you get things like refocusing on something like light show. Mark has worked on all of this stuff. We talked about the jump from Google to Adobe,
1:57 Nilay Patel? why he decided to make the switch. At Adobe, his team is tasked with building a universal camera app that will work across platforms. We talked about what that might look like, what the next frontier of smartphone photography might be, how video factors into it. And of course, if you've ever heard us talk about computational photography on the Vergecast, you know that I asked Mark Lavoie what the nature of reality truly is. Really interesting conversation. We got all the way into it. Check it out, Mark Lavoie, Adobe fellow.
2:24 Nilay Patel? Mark Lavoie, you are a VP and Fellow at Adobe. Welcome to the Vergecast.
2:29 Speaker 03 Thank you for having me.
2:30 Nilay Patel? So, Mark, you and I have talked before at Pixel events. You were at Google. I think for our audience, most notably, you worked on a team that developed the Pixel camera and really ushered in computational photography. But you've had a long and varied career. Now you're at Adobe. Can you just give people a sense of... I'm looking at your website. It's a long list, but give people a sense of all the kinds of things you've worked on.
2:51 Speaker 03 Well, I was a professor at Stanford for 25 years. I've worked on a variety of things. I've worked on cartoon animation, actually, in the 1970s. I worked on medical imaging, a technique called volume rendering, in the 1980s. I worked on three-dimensional scanning in the 1990s, culminating in bringing a team of 30 Stanford students with me to Italy to digitize the statues of Michelangelo, a project called the Digital Michelangelo Project. I worked on light field imaging in the 2000s, which means taking many closely spaced images in order to be able to either move around after the fact or refocus after the fact. And that's led to some commercial companies like Lightro with a refocusable camera.
3:33 Speaker 03 And then in the 20 teens, I've worked on computational photography, a term that I actually coined or recoined, or at least popularized in a course I taught at Stanford in 2004.
3:46 Nilay Patel? That's a lot of things. I want to focus now on photography very specifically. You made the jump to Adobe. What are you doing at Adobe?
3:54 Speaker 03 So Adobe is a unique company. They are world-class in image editing, and they have more imaging experts than any other company on the planet. So it's really a unique opportunity. The initial press announcement reported that I'll be building a universal camera app. So what does that mean?
4:16 Nilay Patel? What does that mean?
4:17 Speaker 03 What does that mean? So I think of universal in three ways. First of all, we do want a camera experience that is based on computational photography at the point of capture and that runs on as many platforms as possible. At Google, the Pixel was just one platform. I'd like it to be universal in the sense that it affects all of Adobe's products. Adobe has a number of products that do have a camera capture experience as part of them. They don't yet use computational photography. And thirdly, I think of Universal as trying to appeal to a spectrum of users, all the way from the everyday consumer who just wants a great picture when they press
5:00 Speaker 03 the button, to the creative communicators, and all the way up to the professional pros. And that's not really a market that I have targeted before, and it's an interesting one. So, Universal in all three of those senses.
5:16 Nilay Patel? So, I want to talk about the Pixel just a little bit. Obviously, the Pixel 2, I think in particular, was a massively disruptive product in that the camera is so much better than everything else. They've been using kind of the same sensor ever since. The, I think, the leaks of the new camera will use the same sensor, the Sony IMX 633, I think. That's one sensor. When you say "Universal," there's a lot of action in camera sensors beyond just one Sony sensor and one camera. Can you actually get the same look out of all these different sensors? Is that one of the goal, or is it to enhance every kind of sensor that you might come across?
5:52 Speaker 03 The mobile sensor industry is fairly mature. It does improve, but the improvements are coming with some diminishing returns over the years. One variable that's of particular interest is the read noise. As the read noise decreases, you can take pictures in lower and lower light. And so, if Sony or someone else comes up with a sensor that has lower read noise, a lot of people will grab onto it. There are other developments that are happening in the sensor world. There's something called quad Bayer, which has a 2x2 pattern of red, and then a 2x2 pattern of green, and then blue. Whether those catch on or not remains to be seen. They have trade-offs. So, there are improvements being made in
6:36 Speaker 03 the sensors, but I'm not sure that they're pivotal. They're incremental.
6:41 Nilay Patel? Yeah, and I wanted to start there with the Pixel and the conversation about sensors, because the thing the Pixel showed everybody is, "Oh, man, we can do a lot with the processors on the phone to improve the quality of the image it's generated." And that seems to have just become kind of a dominant theme across all the major smartphone vendors now.
7:01 Nilay Patel? I have not yet seen that kind of technique applied beyond a smartphone, right? There's some mirrorless cameras that'll do some of that stuff, but the the big computational photography idea of let's take a bunch of frames and then generate a final frame lives on a smartphone. Can that be expanded beyond the phone?
7:17 Speaker 03 Yeah, that's a really good question. So when we won DP Reviews, I think it was the smartphone camera beer. No, it was the innovation of the year. The first time the user forum comments were running 6040. Well, this isn't real photography. They're combining a bunch of different moments. Okay, okay. The next year, we also won the Innovative Technology Award from DP Review. The user forum at Night sight had come out and user forum comments had flipped. They were now running 8020. Huh, why don't the SLR makers do this?
7:57 Speaker 03 So the answer is complicated. I wrote an article, which you can look up online about 10 years ago, that surveyed the SLR industry and tried to identify why they have not begun to use computational methods. And it has to do somewhat with conservatism and who they believe their market is.
8:18 Speaker 03 One factor I've learned since I wrote that article is that actually their programmable processors are not very powerful. They have, for example, on Canon, this digic signal processing chip that's basically an ASIC, a special purpose chip that does just their algorithms. And it would take them several years to spin a new one to do a different algorithm. And so the ARM core that is programmable is relatively weak. And so in terms of programmable and therefore agile software development, the mobile phone makers are actually in a better position than the SLR makers. Also, the SLR sensors are large, so they would have to do a lot more processing.
8:58 Nilay Patel? So do you think you kind of hit just the limit of what you could do with the Pixel platform? And that's why you're expanding to a more universal app? Like, that's the the question I'm thinking about is the Pixel was like far and away the winner in that one little slice of photography. To some extent, Apple caught up. But building an app that works across every phone, perhaps more devices, seems like a bigger opportunity. Was that the thing that attracted you and made you jump?
9:23 Speaker 03 Well, if you look at my career, which I outlined at the beginning here, I get intellectually restless every seven to 10 years. And I think it was time to declare victory and move on. There were diminishing returns among these table stakes of high dynamic range imaging and low light imaging. And it was time to look for a new frontier.
9:46 Nilay Patel? When you say universal across three fronts, is that the new frontier? Or is there an imaging new frontier?
9:50 Speaker 03 There is also an imaging new frontier. I of course don't want to tip my hand as to exactly what we might work on at Adobe. But if you look at the research literature in computer graphics and computer vision, you can see threads for techniques beyond the table stakes that people are trying to accomplish. One example of those would be removing window reflections. There's a lot of papers people have published on removing window reflections. Another thread in the literature is removing harsh shadows and relighting. And there've been some products in that space, maybe not fully successful yet. So if you look at the research literature, you can see a lot of these threads, removing distracting objects, the bus in front of the Eiffel Tower,
10:35 Speaker 03 the power lines, the trolley wires. And there are, there again have been papers published in the research literature. No one's really nailed it yet. So I think there's still a lot to do. And I think of those as being beyond table stakes. But features that pass what Larry Page at Google like to call the toothbrush test. You use it twice a day and it improves your life. In other words, it's a feature that people really want or need.
11:02 Nilay Patel? You know, it's interesting. You started with Forearm Nerds, a DP review, saying this isn't a real photo. It's not just a single frame in a moment in time, which is an argument that we have on the verge all of the time, just because it's fun to dive into. But the things you just described are edit features. They're not capture features. Is that how you think of it?
11:21 Speaker 03 Not necessarily.
11:22 Nilay Patel? Okay.
11:23 Speaker 03 This is a holy grail. I don't know if we'll ever accomplish this. But could you imagine that we had a smartphone where you could tap on a button and the window reflections would disappear? And then you could proceed to frame your photograph. So in other words, it happens in real time during composition. That's before capture. And it would really make a different kind of a device. It would give you a superpower that would be useful.
11:47 Nilay Patel? How do you think about, I mean, I was just scanning all of your Stanford lectures that are on YouTube. And you know, they start, you start in the first one with the equation for depth of field. And you end with like, what makes a good photo with like the artistic nature of photography. Looking through a viewfinder or smartphone screen, editing reality and then capturing that is a very different kind of art making. How do you, how do you think about your responsibility to that?
12:12 Speaker 03 I'd like to empower it and let the artists decide what it's good for. But as I said, I think consumers would find these capabilities useful. If they could press on that bus in front of the Eiffel Tower and it would just disappear. They would know, okay, I don't need to wait for the bus to drive away. I can take my picture now. And that is actually affecting their composition and what they do with the point of capture.
12:35 Nilay Patel? I mean, I feel, I feel like I can just go down this rabbit hole for you. I mean, there's a lot to be said for that. How would you accomplish something like that? Would you need to know, would you need a base frame of what the Eiffel Tower looks like to re-add it? How do you, how would you accomplish something like that?
12:48 Speaker 03 Again, if you look at the research literature, there have been a lot of different approaches. One of them is exactly what you just say. The entertainment industry would call that a clean plate, a clean background plate. That's one possible approach. Another approach, depending on which part of the scene the bus obscures, is to just use your prior knowledge, a machine learning algorithm's prior knowledge of what is likely to be there. And to put plausible things there. If the bus is too large, of course, that's not going to work well. But if it's a small object, it might work very well. And there have been a lot of papers about this so-called in-painting problem. And Adobe as well has some products in that area. The content aware fill is an in-painting algorithm essentially.
13:32 Nilay Patel? Adobe makes Photoshop and I feel like Photoshop as a verb lends itself to this kind of creation, right? This kind of after the fact reality creation. When you say universal camera app from Adobe, they already have Photoshop camera. There's a camera in Lightroom. Is that where this stuff goes?
13:49 Speaker 03 Unclear. That's, that's sort of product roadmap and that's unclear. At this point, I plan to build technology and we'll see where it lands.
14:00 Speaker 03 Of course, I don't want to focus just on what happens before you take the picture. If you can, for example, store the burst that you've captured plus any metadata, then you can also edit it after the fact and decide to remove the bust later. So I think editing can be thought of as happening pre-capture, during capture, and after capture.
14:19 Nilay Patel? So to edit afterwards, you probably need to store a number of frames, right? I mean, you brought up Lighttro and Lightfield, right? That was, that entire game was a new file format that contained a variety of data.
14:30 Speaker 03 Right, and that's what I mean by capturing and storing a burst.
14:33 Nilay Patel? Yeah, so, but that was, that implicate like a new file format? I mean, one of the things that I think about with all these cameras is, at the end of the day, I'm just exporting JPGs. For better or worse, that's the world we live in. And I often think it would be great if I could go back to any of these JPGs and read and change them in various ways.
14:50 Speaker 03 Well, actually, the world is a little bit larger than that. So on most phones, you can ask for a DNG to be stored as well, a RAW format. And on the Pixel phone, that RAW file, that DNG file, was merged from a burst of frames. So it was a very good DNG file. It was SLR-ish quality. In also on the Pixel, if you took a portrait mode shot, it stored both the original and the background defocused version. And it also stored something about the segmentation mask of the person or the depth map of the background. So we're already moving in that direction where we're storing more.
15:30 Nilay Patel? Do you think as you envision a universal app, you will have the access you need to the sensors across a variety of platforms to do the kind of photographic computation that you want to do?
15:41 Speaker 03 That's an excellent question, to which I don't fully know the answer.
15:45 Nilay Patel? Does iOS give you that capability? So ask it straight up.
15:49 Speaker 03 So the history of these APIs is that at Stanford, we developed a prototype idea called the Franken Camera, published a paper on it. One of my PhD students, Eddie Talvalla, moved to Google, and that was the genesis of the Camera 2 API. I think once that became known, the other vendors began to follow. Apple followed with a more flexible camera API that could capture bursts and opened it to third-party developers. And so I think the world is moving in the right direction there.
16:22 Nilay Patel? When you think about sort of the state of cameras now, smartphone cameras in particular, the race has been to, you know, Samsung will add a gigantic, I think they're up to 48 megapixel sensor, they'll do the pixel bending that you described earlier, Apple's just adding lenses left and right with various sensor sizes. There's definitely a hardware race going on. Is that useful to you? Do you think having four lenses and a LiDAR sensor is going to help you out?
16:48 Speaker 03 Potentially yes, potentially no. The Pixel 4 last year did add a telephoto lens, and that did help. Google shipped a telephoto lens plus the super res zoom technology that came out of my team, and the two of them worked together very well. So there's definitely something to be said for more hardware. A depth sensor could help with a variety of tasks. So I think hardware is important, but I think what I've shown over the last 10 years is that software is very important. So I think the two work hand in hand.
17:18 Nilay Patel? Well, just as we, I'll tell the audience, before we jumped on, you and I were joking that we're both using standalone cameras as webcams right now. I think you said you had a Sony A7, I have an RX 100. Is there some reason that like a laptop camera can't achieve the effect that you and I have both gone after with dedicated hardware and like big lenses and big sensors here?
17:41 Speaker 03 Yeah, that's a good question. So one difference between our use of a fancy camera as a webcam and what a smartphone camera could do is that this is real time. So video is an entirely different ballgame. The computational photography that we did at Google on the Pixel was largely in the still photography area. There were some other teams at Google working on video, but there was less that they could do because they had to do it in real time. And so as a webcam, you can't take a burst as easily for a single frame, and processing would be very difficult. And so I think video is one of the next frontiers.
18:22 Nilay Patel? So that kind of leads me right in. I did want to ask you about video. When I look at sort of the world of creative expression on the internet, what are the kids doing, right? They are using largely realtime AR filters or video editing software in apps like TikTok, Instagram reels. Snapchat is actually very good at this. That's all, you know, when you open a camera on Instagram, they open you to the video camera and they'll take a still off that video camera, which drives me crazy because it looks horrible. But they know that seamless switching between video and photo is more important than one Instagram photo call. I mean, they've said as much to me. You know, the Pixel as a video device just never got there, right? It just, we complain about it all the time. You look at what Apple's doing, they are doing a little bit of realtime tone mapping.
19:07 Nilay Patel? Their video looks very clean. Samsung is obviously racing ahead. Is there a connection between what you're working on with computational photography and what we see consumers wanting to do with their video cameras and what they are demanding to do with their video cameras?
19:19 Speaker 03 Right, so the way to move ahead in video is going to depend more on hardware. And in particular, it's going to depend on hardware accelerators for the computations. So the CPU, the central processing unit, the GPU, the graphics processing unit, DSPs, digital signal processors, neural engines for machine learning networks. I think that's going to be one of the key battlegrounds going forward. And so the vendors do need to put hardware, are already putting hardware in their devices for accelerating machine learning calculations and they're providing APIs, programming interfaces for developers. That's still sort of nascent. They're not yet stable.
20:04 Speaker 03 They're not yet fully performant. I think that is the next battleground. And those will be useful for doing computational videography. I think that is going to be a future battleground.
20:16 Nilay Patel? Is that connected to what you and your team, your forthcoming team will be doing at Adobe?
20:22 Speaker 03 We'll have to see.
20:23 Nilay Patel? How big is this? I noticed there's a couple of job listings up on LinkedIn. I saw that you tweeted them. How big is this team going to be?
20:30 Speaker 03 We'll have to see.
20:31 Nilay Patel? How big is it now? Is it just one person?
20:33 Speaker 03 Yeah, it is just me. My team at Google was about 30 people. That's a nice size for a close-knit engineering team. If not on the larger size for a close-knit engineering team.
20:48 Nilay Patel? Did you work on the hardware of at Google too? Because now you're fully going to be in the software world, right? You won't have that connection to the hardware side. How connected were you to the hardware side of Google?
20:57 Speaker 03 Um, I gave them advice.
21:01 Speaker 03 Whether they listen to it or not would be another question.
21:05 Nilay Patel? Yeah, that sounds about right. When you look across the sweep of smartphone hardware, is there a particular device or style of device that you're most interested in expanding these techniques to? Is it, you know, the 96-megapixel sensors we see on some Chinese phones? Is it whatever Apple has the next iPhone? Is there, is there a place where you think there's yet more to be gotten?
21:27 Speaker 03 Because of the diminishing returns due to the laws of physics, I don't know that the basic sensors are that much of a draw. I don't know that going to 96-megapixels is a good idea. I mean, the signal-to-noise ratio will depend on the size of the sensor. And so it is more or less a question of how big a sensor can you stuff into the form factor of a mobile camera? Before, the iPhone, smartphones were thicker. If we could go back to that, if that would be acceptable, then we could put larger sensors in there. Nokia experimented with that, wasn't commercially successful. Other than that, I think it's going to be hard to innovate a lot in that space.
22:10 Speaker 03 I think it will depend more on the accelerators, how much computation you can do during video or right after photographic capture. And I think that's going to be a battleground.
22:22 Nilay Patel? When you say 96 is a bad idea, right? I mean, much like we had megahertz wars for a while, we did have a megapixel war for a minute. Then there was, I think, much more excitingly, an ISO war, where low-light photography and DSLRs got way better. And then soon that came to smartphones. But we appear to be in some sort of megapixel count war again, especially in the Android side. When you say it's not a good idea, what makes it specifically not a good idea?
22:48 Speaker 03 The, as I said, the signal to noise ratio is basically a matter of the total sensor size. If you want to put 96 megapixels and you can't squeeze a larger sensor physically into the form factor of the phone, then you have to make the pixels smaller. And you end up close to the fraction limit and those pixels end up worse. They are noisier. So it's just not clear how much advantage you get. There might be a little bit more headroom there. Maybe you can do a better job of demosaicing if you have, meaning computing the red, green, blue at each pixel, if you have more pixels, but there isn't going to be that much headroom there. Maybe the spec on the box attracts some consumers, but I think eventually, like the megapixel
23:32 Speaker 03 war on SLRs, it will tone down and people will realize that's not really an advantage.
23:37 Nilay Patel? Do you think any of the kind of pixel binning or quad bearer techniques? Because, I mean, those, the 48 megapixel cameras, they still spit out a 12 megapixel photo by default, right? Do you think those help?
23:49 Speaker 03 That remains to be seen. If you have four reds, four greens and four blues, that makes demosaicing, interpolating the reds, greens and blues that you don't see harder. And so those quad bearer sensors have been subject to spatial aliasing, artifacts of one kind or another, zippering along, along rows or columns. And whether that can really be adequately solved remains to be seen.
24:13 Nilay Patel? One of the things, as we review the phones, kind of on our, our very consumer side, and it's always really interesting to connect sort of how we review things that consumers use with how you make them, how you build them, how you think about them, is we have noticed a particular HDR look has emerged on each of the phones. So Samsung has a very particular, very saturated look. I can spot a Samsung photo a mile away. Apple started in one place, they went to another place and they're, they're going to yet a third place, I think. The pixel has been relatively constant, but it's moved a little closer to where the other, other folks are, in my opinion. That is a big artistic decision, right? That's connected to a lot of engineering, but at some point you have to make a qualitative determination.
24:58 Nilay Patel? How are these photos going to look? You obviously had a huge hand in that. How did you make that determination?
25:03 Speaker 03 You're right that it's an artistic decision. And my team was instrumental in that. I looked at a lot of paintings. And looked at how painters over the centuries have handled dynamic range. One of my favorite painters was Caravaggio. Caravaggio had dark shadows. I liked that.
25:26 Nilay Patel? That really explains a lot about the pixel, too.
25:28 Speaker 03 Right. Last year, we moved a little bit more toward Titian. Titian has lighter shadows. It's a constant debate. It's a constant emerging taste. And you're right that the phones are different. It's also true that there is probably some ultimate limit on high dynamic range imaging. Not necessarily on how high a dynamic range you could capture, but on how high a dynamic range you can effectively render without the image looking cartoony. One of my favorite photographic artists is Trey Ratcliffe. And his look is deliberately pushed and cartoony. I think that's his style.
26:09 Speaker 03 But I'm not sure I would want the Trey Ratcliffe look with every picture that I took every day with a smartphone. And so I think that's that's an important limit. It's not clear how we get beyond that limit or whether we ever can.
26:22 Nilay Patel? You know, our friend, Marques Branley, who I'm sure you know, does these challenges every so often. We ask people to vote, like blind Pepsi challenges of smartphone photos. And I think every time he's done it, it doesn't matter how good the photo is. The brightest photo always wins, right? The crappiest photo, but it's the brightest one. And that's the easiest cheat that any camera maker has is just to overexpose it a little bit. And then you'll win on Twitter. How do you fight that? I mean, like literally the Pepsi challenge, Pepsi won. This is like ancient history, but Pepsi would win those wine taste tests because Pepsi had more sugar. But then over time, people would prefer Coke. Like, that seems like the same category of the brighter photo wins, even if the overall quality over time is lower. How do you solve for that in a moment like this?
27:07 Speaker 03 That was a debate that at Google we had all the time. At Adobe, I'm hoping to put it more in the hands of the consumer or the creative professional. Let them decide what the look will be. But of course, that was a constant debate because you're right. Brighter would often win in a one-to-one comparison. One factor that you haven't mentioned that I should add in here is the tuning of the displays on these smartphones. Most smartphones are a little bit cranked relative to a calibrated, so-called sRGB display. They're more saturated, they're more contrasting. You could argue that that's probably the right thing to do on the small screen. It would be a terrible thing to do on a large screen. It would look very cartoony.
27:51 Speaker 03 But that kind of contributes to what people want to see and to taste, especially since most photographs are looked at only on the small screen. Yeah, it's a constant debate, a constant emerging trend. It will probably change again. We can look at photographs from the 50s and 60s. And partially because of Kodak's choices, but also because of their technology, we could identify a Kodachrome or an Ektachrome picture. And we'll be able to recognize pictures from various decades of digital photography as well.
28:23 Nilay Patel? Is kind of your vision for a universal app? And I recognize you're a team of one, building on team, many steps to come. But that anybody will download it on any phone and the image that comes out will look the same no matter the phone?
28:37 Speaker 03 That remains to be seen. One of the interesting questions, sort of hidden under your question, is personalization, but also regionalization. And some phone vendors do regionalize their phones and some do not. At Adobe, I think the preference is to leave the creative decisions in the hands of the photographers more so than the phone vendors have been doing in their software. Unknown how that will shake out.
29:03 Nilay Patel? Well, the reason I ask, I mean, depending, again, I said there's many layers underneath it. But assuming you get a sort of standardized access to sensor data, would your instinct be to, here's a relatively neutral thing for the creative to work from that looks the same across every phone?
29:19 Speaker 03 That's one possible path. So to dive down a little bit into the way raw images are processed, you can take an approach of, just among SLRs, let's say, of putting out the kind of image that that SLR would have put out. And so Nick Imaging, when the company still existed, did that. Adobe tries to do its own white balancing and processing and give a fairly uniform look across any SLR. That's a different decision. And maybe that would be continued for the different smartphone vendors. Maybe not. Remains to be seen.
29:57 Nilay Patel? No one on the Lightroom team will tell me this. I just don't think they ever will. But I swear, the auto button in Lightroom has been tweaked to look more like the iPhone. So when you hit it, it generates a more, more even dynamic range in the highlights and shadows. I swear they made this change like two years ago. They will not admit it to me. But that's like a, the reason I bring that up is that is, for better or worse, the iPhone and the Pixel, and now Samsung, that is what photos look like. They look like high dynamic range. The vast majority of photos people capture and see on, on online platforms, that's what they look like. Is, and that, to a large extent, that is an expectation that you helped create. Do you think, if we go look back and look at those
30:39 Nilay Patel? Kodachrome photos or Ecochrome photos, they did not look like that. They could not look like that. Do you think that is going to have an effect on the shape of photos to come beyond just, well, now the sky is exposed and so is your face?
30:50 Speaker 03 Well, it's useful, just from a practical point of view, to not have highlights blown out and shadows crushed to black. If you can institute controls at the time of capture, you could let the photographer make some artistic decisions. It's okay if this blows out. It's okay if this gets crushed to black. I actually want it to look like a silhouette. And in the Pixel 4, we did that. We had these brightness and shadow controls. But to the extent a consumer just wants to press the button and get a photo, they probably want to avoid blown out highlights and crushed shadows. And so you're kind of forced into this regime where you do do high dynamic range tone mapping. And Then the question just becomes within that range. Do you make it high tone? Do you make it low tone?
31:34 Speaker 03 Do you make it more like Caravaggio or more like Titian? And those tastes could change over time. They could be regionalized. They could be personalized. Who knows where that will go? It's almost like you're asking me, what will artistic tastes look like in 10 or 20 years?
31:49 Nilay Patel? Sure.
31:50 Speaker 03 I wouldn't dare. Yeah, but I wouldn't dare try to answer.
31:52 Nilay Patel? I mean, but I mean, to be very blunt about it, the techniques you pioneered, developed, shipped have radically altered the way that people think of what a photo should look like. You're a good person to ask for what might happen the next time around.
32:06 Speaker 03 Right, but that's like trying to ask the people in the, I believe it was the Netherlands, who first developed oil painting in the Renaissance. And it eventually got down to Italy where they were doing Tempora instead. And asking one of those inventors of oil paints, so what are paintings going to look like in 200 years because of your invention? No idea.
32:28 Nilay Patel? Yeah. How do you think, I mean, that the, one of the, kind of the themes of The Verge, and I know this is in your work too, is the tools have become so much easier to use. Computational photography is, is hard. Like, I'm sure you will agree. It was not accessible to a lot of people for a long time. The first HDR photo I took, I had to set up my DSLR on a tripod and take 15 frames and merge them using some plugin that hardly worked in Photoshop. Now that's just all happening on my smartphone. Is that a trend that you can see the next thing that's hard or difficult or complicated will get democratized? Maybe that's a better way to ask this question, what will change?
33:06 Speaker 03 Yeah. Video and real time, what happens in the viewfinder while you're composing your shot?
33:12 Nilay Patel? What specifically with video? I think that's such a rich area now where we're just seeing so much experimentation because the platforms are doing it at the platform level. And then we are seeing, because there's a closed loop of distribution, inspiration, creation with users on a platform, with the app in the same space that they distribute. It just seems to be happening way faster than anywhere else. What do you see as the next kind of trend there that will, not creatively, but in terms of actual capture and edit?
33:40 Speaker 03 Well, so the same table stakes that my team and the other players in the industry brought to still photography still has not been applied to video. So things blow out too often on video. Shadows are crushed to black. The white balance is not stable. All of those things need to be fixed. You don't need to look any further than video conferencing to see how bad video is. So that all needs to be to be fixed. And then the question becomes beyond these table stakes, what are the creative effects you can do with either short videos or longer videos that will be interesting or useful to consumers?
34:20 Speaker 03 And I think there's a lot of games that can be played in that in that space of synthetic motion blur or fast multiples, the kinds of effects that you saw being done at the Olympics, the last Olympics. There's just a lot of playing that can be done in there. One question, of course, is how easy can you make that for consumers? Video editing has always been a harder hill to climb than photo editing. And so consumers generally don't edit their videos. So can you come up with user experiences, user interface paradigms that make that easier? I think that's that's really going to be a challenge going forward.
34:58 Nilay Patel? We, Scott Belsky was on the podcast last year now, I think. Scott Belsky is the Chief Product Officer at Adobe for the audience. He and I had a conversation about whether Adobe ever feels like Instagram is just ripping Adobe off, right? Like, inside of Instagram is a tiny version of Photoshop. Inside of TikTok is a tiny version of Premiere. And that seems like, I mean, from my perspective, is great, right? The tools are democratized, they're being used, they are teaching an entire generation of kids maybe to use other software. Scott, you know, Scott's answer was very much like, this is great, it inspires us, we're all just going to keep competing, which is always, that's what every executive says. It seems like what you're going to say, too. What is it that you think Adobe can do in this moment that the consumer platforms cannot?
35:42 Speaker 03 I am going to give exactly the answer you didn't expect. Well, when, let me nuance it a little bit. When I was at Google, we published as we went along. Clearly, we created fast followers by doing that publishing. But it had the advantage that it would allow me as an executive to hire PhD superstars who wanted the international reputation and interaction with the research community that publishing brought and the larger impact as well. And that enabled us to move even faster because we had such smart and creative people. And that's no different at Adobe.
36:23 Speaker 03 Adobe Research has a stable of the best imaging experts in the world. They do publish. That does mean that others will be able to follow what they do. But it also means that they can innovate fast and come up with lots of great innovations. And I think that's just the way Silicon Valley is developing. Not universally, Apple does not publish as much. But in many of the companies, that is true. And so how does a company respond that is being fast followed because its own researchers or engineers are publishing? They have to run faster and breathe deeper and come up with better innovations. And I think that's what Adobe is going to have to do as well.
37:06 Nilay Patel? This doesn't seem, I mean, publishing, right, is one way to communicate. But I'm assuming the ecosystem of people who can build these products is relatively small. There must be some amount of conversation. Obviously, there's conferences like Siggraph, although probably all virtual right now. Inside of the community, where are the tension points of photography? Where are the, of developing computational photography and video? Where are the debates?
37:30 Speaker 03 So companies will have a natural advantage over academia, for example, because they can capture more training data. And so a lot of the discussion these days is about the move from classical algorithms to machine learning for many of these tasks. And then the question is, what does your training data look like? And the larger companies can do a better job of capturing training data. So a lot of the discussion is around training data. Another point of discussion right now is that computer graphics is getting so good, you can't tell a real photograph from a computer graphics rendering anymore in most instances. Could we use computer graphics to train computational photography algorithms? There are just a whole lot of really, really juicy intellectual questions in there.
38:14 Speaker 03 Those are the kinds of things that we debate endlessly at conferences about.
38:19 Nilay Patel? What would the, I mean, I think I can maybe guess, but what would the challenges or the ethical considerations of using computer graphics to train a computational photography algorithm look like?
38:29 Speaker 03 Mostly the question of whether you're rendering a set of scenes that are representative of what you'll photograph. That's the question for most machine learning models is, is it a sufficiently diverse and representative training data?
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40:03 Nilay Patel? I feel like underlying this entire conversation, ever since you said you can take the bus out of the Eiffel Tower, that's great in one instance, it could be applied in many unethical instances. Do you think that as you create the tools, you have a responsibility to kind of build an ethical framework into them? I realize that's a, we have like 10 minutes left, so I really, that's like a thorny conversation, but I'm very curious.
40:27 Speaker 03 I think that has to get layered on top of the technology. And there are a number of aspects to it. Bias is one aspect. Authenticity of the photograph is another. In some cases, it doesn't matter whether it's authentic. You're making something creative. In other cases, like photojournalism, it's very important whether it's authentic. And so, those things are important. They'll, they'll get layered on top of the technologies. The technologies will be developed anyway.
40:51 Nilay Patel? Well, let me challenge you on that, actually. Right? I mean, that is how we have heard technologists for decades now describe the nature of technology. I would suggest that maybe that approach has had its ups and downs. And in particularly, we are very clearly seeing many of the downsides of saying that technology is neutral now. Your project is capturing reality in some way. Have you reconsidered that approach of I'm going to build the neutral platform and other people will make ethical decisions?
41:18 Speaker 03 No. Science and engineering will move forward anyway. And the ethical use of it has to be layered on top of that. There are very, very few instances in which you should actually stop the science and engineering. I could think of a couple, like genetic engineering, but I have not seen that in my area.
41:37 Nilay Patel? Do you think as more of our communication becomes visual, which is undeniably happening, that things like deep fakes, or here's a video of a protest where I've deleted, it captures some of the people, so you can't even check my work, like, do those things occur to you?
41:53 Speaker 03 Uh, yeah. So deep fakes is a, is a good example. It's important to remember that a lot of the technology that is now useful in deep fakes has been developed and continues to be developed for the special effects industry for movies, where people go to the movies and very much enjoy the things they see there that they know aren't real. Would you prefer not to have a special effects industry and in any of the creative movies that they, that they've made? I mean, I think Star Wars has been a great addition to our culture. Those things didn't happen. There is a lot of fakery going on there. It's a dual use technology.
42:29 Nilay Patel? There's a, a little bit of a gatekeeping effect with the special effects industry, right? Those things are hard to use. They're hard to access. They require skill. They have to be commercially viable. As you make all of those tools more democratic, does that change the nature of them?
42:43 Speaker 03 Right. But at that point, the, uh, the horses, uh, you can't close the door after the horse has already left the barn. You can't say, we're not going to allow those techniques to be used on the mobile platform or for everyday photography. The techniques were developed, they will be used there. What society needs to do is to layer on top of that, the proper controls and expectations so that they're not misused.
43:04 Nilay Patel? Let me give this a more specific example. You mentioned regionalization on phones. When I think of phone cameras and the, the single bit of regionalization that is most prominent to me, it is face smoothing, right? Samsung phones will do it. They do it more aggressively in Korea and other Asian countries. Here in the United States, there's just a cultural norm that that is bad. And then you would talk to folks like Instagram, they will police the filters that are available on their app because they know that teenagers will enter bad cycles if the filters do communicate the wrong things. That does seem like a big discussion, right? Should we just automatically smooth faces? Where do you think the, not say it too in the weeds, but Again, we started with, I want the photos to look like paintings.
43:48 Nilay Patel? And now the question is, what should the painters make? Right? How do you think about those techniques and those tools at a company like Adobe where anything is possible? Right? Where like the goal is to empower the creative as much as possible.
43:59 Speaker 03 Right, that's a legitimate question. And so, as you said, if you look across all the phone vendors, they've taken different decisions on how much of that to do. And that's a complex decision that includes ethics and taste and so on. And they may change their minds over the years. That is a question that gets layered on top of the technology and it may change. And maybe some kind of trace of authenticity is important, but for most of the uses where photos get shared on social media, no one cares about the authenticity, they just care, is that really what this teenager looks like or not? It's questions that will have to be answered sort of at the societal level and in the
44:40 Speaker 03 product decisions of each company at each moment in time. There's no pat answer to that question.
44:47 Nilay Patel? Yeah, it's interesting because again, the last time Scott was on, he was on to talk about Adobe's Content Authenticity Initiative, which provides a level of being able to check if a photo on Twitter is really from the New York Times. Right? And I think that that makes sense for photo journalists. It seems like it requires a lot of checking, like it will require a lot of checking over time. But, you know, we just ran a giant package on citizen journalism effectively around the actions of the police. And it feels like it's just going to come to everyone as more of the tools get democratized.
45:20 Speaker 03 Right, right. And it will be interesting to see whether there becomes pressure either from the phone vendors or from other software companies to put that kind of content authenticity into all photographs. I'll be interested to follow that.
45:35 Nilay Patel? I want to just shift gears for one second. This, I mean, I could do this, this conversation in particular for a long time, but we have only talked about phones. We have, someone talked about DSLRs, but Adobe also makes a suite of powerful desktop software. They make Photoshop. Do you see these techniques coming to other types of devices, laptops, tablets? Do you see those cameras accelerating in the same way? There are things you can definitely do on a desktop computer that you can never do on a phone and vice versa. How do you see those two things converging?
46:06 Speaker 03 It may. There are a lot of hard intellectual questions to be answered. If, as I said earlier, we're capturing bursts of frames, we're capturing depth maps, we're capturing metadata, what do those look like as creative, manipulable objects? How are they represented in programs like Photoshop and Lightroom? How much storage are they taking in your cloud? There are a lot of technical challenges there and a lot of just design questions. What does the user experience or the creative professional's experience look like manipulating those things? Is it too much complexity or is there a way we can make it straightforward to do? And I look forward to getting into exactly those, those design questions.
46:50 Nilay Patel? I mean, I'm asked earlier, but you just said, what does the manipulable data look like? That really does feel like a new file format or a new container format that is just going to have to be, okay, here's a re-editable photo.
47:04 Speaker 03 Yes, I think that's the direction that the, that the entire industry is headed. And who will want to re-edit them and what kinds of re-edits will be supported and what can, what that software will look like is going to be very interesting going forward.
47:16 Nilay Patel? Do you think that those edits should travel with the file format?
47:19 Speaker 03 I think the options should certainly be there. Not everybody will want it. If I share a picture on social media, I don't know that I need to share the whole re-editable blob.
47:29 Nilay Patel? Yeah.
47:29 Speaker 03 But as the creator, I'd like as much flexibility as possible.
47:33 Nilay Patel? You have probably seen image formats come and go and get developed and have the various controversies. What is the state of the sort of image format debate right now as everyone's moving towards this? The big companies do not seem great at standardizing anything lately. So I'm wondering if, if any company is pushing one direction or we're going to end up with 15 proprietary smartphone formats.
47:55 Speaker 03 If I tried to make a prediction there, I would just get it wrong. So I, and I try not to make any prediction. One thing I will say is that it's darn nice that JPEG has lasted as long as it has. It means that I can go back and look at pictures that I took in the 1980s even. I can still view them. Unfortunately, the same thing is not true in the video world because of the way the business models and the patent wars and so on developed in the video world. I cannot look at a video file that I created in the 1990s or even in the early 2000s unless I have some very specialized software. I'd like to see that settle down and we'll have to see what happens with these file formats that enable richer metadata.
48:38 Speaker 03 Hopefully we'll come up with file formats that will stand the test of time.
48:43 Nilay Patel? When you think about what in your new project, what that format could look like, would it just be the burst? Or would it be a base frame and some additional information from the rest of the frames you capture?
48:54 Speaker 03 You are asking the $64,000 question. I don't know the answer to that yet. That's exactly the right question to ask, though.
49:01 Nilay Patel? Yeah, I mean, it just seems it seems like incredible because the next question, I feel like fundamentally, I've just been asking you, what is the nature of reality over and over again? The fundamental question is, which is the base frame? How do you pick that base frame if you're only going to store the metadata from all the other frames? Because that decision becomes much higher stakes then.
49:18 Speaker 03 Wouldn't you like the ability to change the base frame after capture? If someone someone's eyes was were closed or they weren't smiling at that particular moment?
49:27 Nilay Patel? Well, yeah, but now you're storing all the frames, right?
49:29 Speaker 03 Well, that's one possibility.
49:33 Nilay Patel? I feel like you will know the answer better than me is that that's like, if you have a base frame and someone's eyes are closed and then you only store the part of the image where the eyes are open, I've come right back to what is the nature of reality, right?
49:45 Speaker 03 Yeah, or a lot of people have proposed these digital photo montage methods where you either open the eyes synthetically or if it's a group shot and someone's eyes were open here and the other person smiled in this other frame, do you combine parts of different frames? And then you are creating a moment that never actually happened, but maybe it's still a better photograph. And I think a lot of that stuff will happen.
50:09 Nilay Patel? Yeah. How do you, again, I just, we have circled this. What do you think the nature of reality is?
50:20 Speaker 03 The nature of reality matters more perhaps for photojournalism than it does for you and I taking a picture?
50:25 Nilay Patel? Well, so I mean, that's like my bias, right? Like, that's what we make. So just an example, we get requests when we publish photos of protests to blur the faces. And we say no, right? I mean, I think other people, for good reasons, accept that request and they do it. And I think people on social media do it. We just would never, right? We have to, that's our responsibility. So that is definitely my initial frame of thinking. But you're saying beyond that, maybe it doesn't matter so much?
50:51 Speaker 03 When I take a picture of a landscape, I don't want it to look like something when I wasn't there, but some people will. Some people would love to make the sky look clear or starry or, or, or something like that. It is a complete spectrum of what people want that to look like. Is it clearly an artistic interpretation? Is it just a little tweet to remove the bus in front of the Eiffel Tower? Is it exactly what I saw with all the grunge? Everyone has a different opinion there. And everyone has a different purpose for their photography.
51:23 Nilay Patel? That's great. So just to wrap this up, you're, obviously at the beginning, you're obviously thinking about a wide sweep of things from sensor hardware to the ability to re-edit to file formats. What does the next year look like for you? What does success look like at a year?
51:37 Speaker 03 The next year for me is just hiring a team of PhD superstars who would like to publish what they do and are willing to roll up their sleeves and build the, the next generation of amazing computational photography.
51:55 Nilay Patel? You don't think you'll have product shifts in a year?
51:57 Speaker 03 Probably not. Probably not. It just takes time to build these things. When I took a leave of absence from Stanford to move to Google to work on Google Glass in 2011, Google Glass came out with our photography on it experimentally. It was at least two years after that. I don't remember exactly how long it was. These things do take time.
52:19 Nilay Patel? Yeah. And, and that's because you want to go beyond what you have described as table stakes.
52:24 Speaker 03 That's right.
52:25 Nilay Patel? You're not just trying to build GCAM for the Samsung phone from Adobe?
52:28 Speaker 03 That's right.
52:29 Nilay Patel? All right. Well, Mark, this was an incredible conversation. Like I said, I think I could probably, I didn't even talk about smartphone displays with you. I feel like I could just do that for an hour. You're going to have to come back, especially as this project continues, because I want to hear everything about it. Thank you so much for taking the time.
52:42 Speaker 03 And thank you for inviting me.
52:46 Nilay Patel? All right. My thanks to Mark Lavoy for joining us. That was just a wild conversation. I want to talk to that guy again and again and again, as he begins to build this app. I think it's gonna be really fun. We'll be back on Friday with a chat show, lots of news to talk about. It's fall hardware season. Dieter's got those big reviews coming up. We'll see you then.
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