Speaker 1: Get in text with technology with tech Stuff from stuff
Speaker 1: works dot com and welcome to tech Stuff. I am
Speaker 1: your host, Jonathan Strickland. I'm an executive producer at how
Speaker 1: Stuff Works. I like you and I like technology, and
Speaker 1: I don't really know that much about you. I just
Speaker 1: get a good feeling, but I know a pretty good
Speaker 1: amount about technology. Today, I want to talk about, you know,
Speaker 1: tech and as I'm sure most of you guys know
Speaker 1: by now, some of you know because you're doing it
Speaker 1: at this moment. I live stream tech Stuff recordings on
Speaker 1: Wednesdays and Fridays over at twitch dot tv slash tech Stuff.
Speaker 1: There's a chat room over there, and I like to
Speaker 1: spend some time chatting with viewers and listeners before the
Speaker 1: show and during breaks. And during one of those breaks
Speaker 1: in a recent show, one listener put forth a request
Speaker 1: for a future episode of tech Stuff and the request
Speaker 1: was for camera stabilization or image stabilization. So that's what
Speaker 1: I'm gonna talk about the future is now. So I'm
Speaker 1: gonna talk about image stabilization in general. And there are
Speaker 1: two really big, really general ways to go about this.
Speaker 1: One is to use mechanical elements to help compensate for
Speaker 1: the little or sometimes not so little, jittery movements we
Speaker 1: make as we try to capture video or still images
Speaker 1: using handheld cameras. And the other is the software approach,
Speaker 1: in which algorithms and programs attempt to reduce the jitters
Speaker 1: sometimes reverse the jitters using some sort of trickery on
Speaker 1: the back end. We're gonna look at both of those
Speaker 1: different approaches in this episode. Now, before I do that,
Speaker 1: I think it might be a good idea to talk
Speaker 1: about how digital video cameras work in the first place,
Speaker 1: so that you understand what is going on before we
Speaker 1: even get into stabilization. That way, we can better understand
Speaker 1: the actual mechanics of stabilization when we get there. So
Speaker 1: let's crack open a digital camcorder. Let's say, you guys
Speaker 1: remember cam quarters, right, I imagine there's still a thing.
Speaker 1: I don't know. I haven't been in the market for
Speaker 1: one for a really long time, But these days there
Speaker 1: are lots of other devices out there that can capture
Speaker 1: high resolution video, including several smartphones. So cam quarters a
Speaker 1: term I don't really hear very much anymore. Anyway, that
Speaker 1: doesn't matter, as the cam quarters inwards are pretty much
Speaker 1: the same as you would find in any digital camera
Speaker 1: device these days. Only now we've gotten really good at
Speaker 1: maturizing those components, so you don't have to lug a
Speaker 1: big thing on your shoulder, nor do you need something
Speaker 1: that is part camera part VCR. And if you don't
Speaker 1: know what a VCR is, you'll have to listen to
Speaker 1: an older version of tech stuff. I'm not going to
Speaker 1: go into that here. First, cameras record visual images. I
Speaker 1: know that breaks it down to an incredibly simplistic and
Speaker 1: almost silly statement, but it is something to keep in
Speaker 1: mind that we are talking about visual information. Now there's
Speaker 1: also audio information. Cameras mostly incorporate microphones these days. If
Speaker 1: you have a video camera that doesn't have a microphone
Speaker 1: and you aren't using external microphone, you might be an
Speaker 1: avant garde performer, or you may just have a really
Speaker 1: crappy camera. Breaking this down even further, cameras capture light.
Speaker 1: Because everything we see is from light that's bouncing off
Speaker 1: of surfaces or filtering through materials. Cameras attempt to capture
Speaker 1: that combination of lighting effects in an effort to replicate
Speaker 1: it in some way, typically to replicate in a way
Speaker 1: that is as faithful as possible to the to what
Speaker 1: we would see with our naked eyes, although obviously you
Speaker 1: can set cameras so that you're capturing stuff and altering
Speaker 1: it so it doesn't look the way it would naturally.
Speaker 1: Thus you have all the different types of filters out there.
Speaker 1: You Instagram fanatics out there know all about that, taking
Speaker 1: photos with various filters, But generally speaking, cameras are capturing light.
Speaker 1: Old cameras did this by focusing that light on photo
Speaker 1: sensitive film. So you would take light, you would direct
Speaker 1: it to this film, and that would alter the film chemically.
Speaker 1: You would then develop the film, putting it through other
Speaker 1: chemicals that would give you a negative, a reverse of
Speaker 1: the image that you want, at least in color, and
Speaker 1: then you would use that negative to produce prints, and
Speaker 1: that would replicate what your eyes saw based upon what
Speaker 1: the camera was capable of capturing. Assuming you had a
Speaker 1: good camera, it would look pretty much the way you
Speaker 1: saw it in person. Now, cameras typically use one or
Speaker 1: more lenses to focus light in this way. In fact,
Speaker 1: when we say a camera lens, you really mean a
Speaker 1: series of lenses that are encased in some sort of
Speaker 1: form factor. It's very easy to think of a camera
Speaker 1: lens as being a single thing, because you might have
Speaker 1: a camera body and then a selection of different lenses
Speaker 1: where you can attach or detach them. But each of
Speaker 1: those lenses contains several glass lenses inside of it. It's
Speaker 1: not just a lens inside a casing. A digital cameras
Speaker 1: by the way, you do this too. Uh. They do
Speaker 1: use film to capture images. They use image sensors to
Speaker 1: capture them, but they still use lenses to focus the
Speaker 1: light onto the appropriate part of the camera. Uh. This
Speaker 1: being the sensor as opposed to photosensitive film. The image
Speaker 1: sensors are solid state devices, and the two most common
Speaker 1: types of image sensors are cc D also known as
Speaker 1: charge couple devices and c MOSS or CMOS complementary metal
Speaker 1: oxide semiconductors. But what the heck does that even mean? Well,
Speaker 1: CCD sensors are the older of the two, and they
Speaker 1: lead the way for many many years in terms of
Speaker 1: image quality. CCD was considered to be the top of
Speaker 1: the image quality. Was also more expensive and less energy efficient,
Speaker 1: but it produced the best images. A CCD sensor was
Speaker 1: able to produce much sharper, higher quality images, but s
Speaker 1: MOSS sensors are more efficient the more power efficient they're Also,
Speaker 1: there's also some more built in functionality with MOSS sensors
Speaker 1: than there are with C c d s. Plus, eventually
Speaker 1: the C MOSS quality pretty much caught up with and
Speaker 1: in some ways surpass C c ds. So while there
Speaker 1: are differences, and they're still different kinds of of cameras
Speaker 1: out there that have different kinds of sensors, and different
Speaker 1: photographers will favor one versus the other, the differences in
Speaker 1: quality between the two have largely diminished. It's not as
Speaker 1: dramatic as it once was. Uh, some budget cameras have
Speaker 1: C c d s, A lot of the better cameras
Speaker 1: have C mosses, but that's not that's not necessarily across
Speaker 1: the board. Like photo reactive film, those image sensors are
Speaker 1: photo sensitive. So when light in the form of photons
Speaker 1: hits the photo sites, photo sites are the little photo
Speaker 1: sensitive elements on these image sensors. Each photo site essentially
Speaker 1: is a pixel, so you get one photo site per pixel.
Speaker 1: When when a photon hits a photos ight, this produces
Speaker 1: an electrical charge. Now a bright light will end up
Speaker 1: generating a stronger charge than a dem light will. The
Speaker 1: photo sites must register the relative brightness of the light
Speaker 1: that's hitting them, as well as the relative brightness of
Speaker 1: the color of light that is red, green, or blue light,
Speaker 1: in order to reproduce color images. Otherwise you're just gonna
Speaker 1: get black and white images. Some sensors do this by
Speaker 1: having a color filter above each pixel photo site, uh,
Speaker 1: and they will have a different color filter over the
Speaker 1: different photo sites. This is called a Bear filter array
Speaker 1: is named after Bryce Beyer, who invented the array. It's
Speaker 1: a filter consisting of a mosaic or array of red, green,
Speaker 1: and blue filters above the photo sites on the sensor chip.
Speaker 1: So it looks kind of like a checkerboard, except you've
Speaker 1: got three colors, not two, and uh. One fourth of
Speaker 1: those filters are red, one fourth of those filters are blue,
Speaker 1: and the other half are green. So you have twice
Speaker 1: as many green as you have either blue or red.
Speaker 1: So why is that Why would you have twice as
Speaker 1: many green filters? Well, that's to mimic the color sensitivity
Speaker 1: of the human eye, because our color sensitivity is not
Speaker 1: even across the board from a biological standpoint. Some filters,
Speaker 1: some Bayer filters, will include different shades of green, so
Speaker 1: you won't just have one single shade of green filter
Speaker 1: you might have to you might have one that's like
Speaker 1: a dark forest green and one that's kind of more
Speaker 1: of a lighter green or something along those lines. Uh.
Speaker 1: Some filters will use clear filters in place of some
Speaker 1: of the green, and that's in order to improve light sensitivity,
Speaker 1: but in general, that's how most of those filters work.
Speaker 1: When the camera records an image, initially, it's a picture
Speaker 1: that's just red, green, and blue in the raw file.
Speaker 1: So if you were to look at a raw photo
Speaker 1: image before any processing has happened, it would look really weird.
Speaker 1: It wouldn't look color realistic at all. It could look
Speaker 1: pretty awful. It's what photogra is often called false color.
Speaker 1: But then you have this image processing unit that's part
Speaker 1: of digital cameras, and it can take this raw file
Speaker 1: and convert it into more natural imaging using what is
Speaker 1: called a demosaic stage, So it takes all that jumblee
Speaker 1: information and makes some meaning out of it. One cool
Speaker 1: thing that happens with this software is how it determines
Speaker 1: the color of an individual pixels. So remember images are
Speaker 1: made up of millions of these pixels. A pixels essentially
Speaker 1: a point of light, or if you prefer a single
Speaker 1: point of color in an image, and the more pixels
Speaker 1: you have, the smaller they Let's say that you have
Speaker 1: the same dimensions of a photo. Let's start with that.
Speaker 1: So let's say you're using an eight by ten photo.
Speaker 1: This is just for the purposes of explaining pixels and resolution.
Speaker 1: If you increase the resolution of an eight by ten photos,
Speaker 1: so you're not changing the size of the image, You're
Speaker 1: just changing the resolution. If you increase the resolution, that
Speaker 1: means you're packing or pixels into that same physical space.
Speaker 1: That means the pixels themselves have to be smaller. If
Speaker 1: you are reducing the resolution, you're decreasing the number of pixels.
Speaker 1: That means the size of each individual pixel gets larger.
Speaker 1: So if you've ever seen an image where someone has
Speaker 1: taken a digital photo that was for a very small
Speaker 1: set of dimensions and then they've blown it up, it
Speaker 1: looks very blocky or pixelated, that's because it was a
Speaker 1: certain resolution and a certain size, and now you've stretched it,
Speaker 1: so all of those pixels have individually been stretched as well.
Speaker 1: If you want to increase resolution, then you have to
Speaker 1: reduce pixel size to add more pixels to that image.
Speaker 1: This only works to make an image sharper up to
Speaker 1: a point. Higher resolution does not automatically mean a better
Speaker 1: quality of picture. There are other elements that are also
Speaker 1: very important, things like color representation, so in contrast ratio,
Speaker 1: So it's not just resolute shan that you need to
Speaker 1: think about. This is why we've done episodes of tech
Speaker 1: stuff in the past where we've talked about the megapixel myth,
Speaker 1: the idea that if you go out and buy a
Speaker 1: camera that has more megapixels that automatically takes better photos
Speaker 1: than a camera that has a lower megapixel count. That's
Speaker 1: just not necessarily the case. It might be true, but
Speaker 1: it's not because of the megapixels. It's because of other
Speaker 1: elements as well. The only time you really have to
Speaker 1: worry about really high megapixels counts as if you wanted
Speaker 1: to take a photo and then blow it up to
Speaker 1: a really large size and you didn't want to have
Speaker 1: too much distortion when you were doing that. You want
Speaker 1: a really high megapixel count so that you can do
Speaker 1: that without losing too much on the resolution side. Well,
Speaker 1: the way this image processing software tends to work is
Speaker 1: it looks at each individual pixel and then it looks
Speaker 1: at all that pixels neighbors and it starts to say, well,
Speaker 1: based upon all the neighbors of this pixel, what color
Speaker 1: should this specific pixel be. And it starts to extrapolate
Speaker 1: information based on this and it makes some decisions about
Speaker 1: what color each pixel should be based upon the nature
Speaker 1: of its neighbors. And as it turns out, this software
Speaker 1: is pretty good. It can reproduce colors fairly faithfully. And
Speaker 1: that seems pretty interesting to me when you think that.
Speaker 1: You know, when you how do you start with that?
Speaker 1: Right when you first start, you don't have any necessarily
Speaker 1: any natural colors. It's all the red, green, blue. But
Speaker 1: by using this kind of process of deduction, the image
Speaker 1: processing software can create a natural looking image. And it's
Speaker 1: all about again looking at all the neighbors of this
Speaker 1: one pixel to determine what color it should be. This
Speaker 1: happens super fast. By the way, it sounds like something
Speaker 1: that would take a really long time, but the software
Speaker 1: is incredibly fast. Now. Bear filters are found in many,
Speaker 1: but not all cameras. Some use other types of filter systems. Uh.
Speaker 1: The fob on sensor has red, green, and blue filters
Speaker 1: over every single photo site. So instead of having either
Speaker 1: a red, or green, or blue filter over each photo site.
Speaker 1: Every single photo site has all three, and some photographers
Speaker 1: say that this produces more natural looking images. Others say
Speaker 1: they are completely bonkers crazy wah wah, who uh. I
Speaker 1: guess it really depends on your perspective and what you
Speaker 1: have experience with um, because as far as I can tell,
Speaker 1: it's not that different. But then my visual acuity is
Speaker 1: nowhere near the level of say a professional photographers. The
Speaker 1: important thing to remember here is that the image sensor
Speaker 1: generates an electrical charge based off these photons, whether it's
Speaker 1: measuring the red, green, or blue, or just the amount
Speaker 1: of light in general, and the camera has to measure
Speaker 1: that that electrical charge and convert that value into a
Speaker 1: digital value, or convert that electrical charge into a digital
Speaker 1: value with an analog to digital converter, and then you're
Speaker 1: left with digital data for all the processing. So the
Speaker 1: software does all the work on the back end, so
Speaker 1: you've got the hard work on the front end where
Speaker 1: the image has to her image sensor has to take
Speaker 1: all these this light information converted into electrical charges, measure
Speaker 1: that and convert that into digital information, and then the
Speaker 1: software takes that digital information and interprets it to create
Speaker 1: the image that you're looking at. There are other things
Speaker 1: that take into consideration here, such as the shutter speed
Speaker 1: of the camera. Shutter is a device that cuts off
Speaker 1: light exposure to the sensor in the camera. The shutter
Speaker 1: speed determines the exposure time of a camera's UH the
Speaker 1: image that you're taking, So a short exposure is good
Speaker 1: if you're trying to take an image of something that
Speaker 1: is moving very quickly, but you need a lot of
Speaker 1: light to do that. You don't you're not leaving the
Speaker 1: shutter open very long for light to get to the
Speaker 1: image sensor. The same was true of film cameras. You
Speaker 1: need a very fast shutter speed, but you need a
Speaker 1: lot of light in order for the light to be
Speaker 1: able to register against the the photo sensitive material, whether
Speaker 1: it was film or an image sensor. If you're using
Speaker 1: a very slow shutter speed where the shutter is open
Speaker 1: for a longer amount of time, and this is all
Speaker 1: relative by the way, we're talking about fractions of a second.
Speaker 1: If you're leaving the shutter speed open for a longer
Speaker 1: amount of time, that's great for low light UH activities
Speaker 1: like if you want to take an image of something
Speaker 1: that's in really low light. Using a longer shutter speed
Speaker 1: where it's open longer is a good idea, But any
Speaker 1: motion is going to insert a lot of blur into
Speaker 1: that image, So you want the you wanted to be
Speaker 1: very still. If you're going to use a slower shutter speed.
Speaker 1: If you wanted to do something like high speed camera
Speaker 1: work where you're going to show something back in super
Speaker 1: slow motion, that shutter is moving incredibly fast. You're talking
Speaker 1: about taking thousands of images every single second. In order
Speaker 1: to do that, you have to have it very very
Speaker 1: well lit. So if you've ever been on a set
Speaker 1: where they're shooting super high speed UH footage in order
Speaker 1: to show back at at slow speed, then you know
Speaker 1: what I'm talking about. The lights in those kind of
Speaker 1: situations tend to be out of control. They're really actually
Speaker 1: very much in control. They're just extremely well lit. So
Speaker 1: the whole reason I wanted to talk about this bit
Speaker 1: is that if you move a camera around while you're
Speaker 1: taking images, whether it's snapshot or video, you can end
Speaker 1: up with a jittery mess and it's really difficult to
Speaker 1: hold completely still. If you've ever tried to just hold still, uh,
Speaker 1: you know even if you've got the hands of a surgeon,
Speaker 1: you're gonna notice a little bit of a jitter in there.
Speaker 1: Most of us have some of that whenever we operate
Speaker 1: a camera. Some of it's more obvious than others, and
Speaker 1: a little bit people can tend to look past, but
Speaker 1: more than a little bit, it gets very distracting. So
Speaker 1: if you ever wanted to do something like move around
Speaker 1: while you're shoeing video, you might have so much jetd
Speaker 1: that's distracting, and you want to figure out a way
Speaker 1: to reduce that, and we found numerous ways to cut
Speaker 1: that down. Some ways just involved locking down the camera,
Speaker 1: so you might put it in a tripod, and then
Speaker 1: the camera's motion is strictly limited and you can operate
Speaker 1: it without it having too much jetter. But then you
Speaker 1: have a pretty static shot. You could use cranes and dollies,
Speaker 1: which tend to have very smooth movement along certain directions,
Speaker 1: but you are limited in the ways you can move
Speaker 1: the camera. In both of those, you can't go anywhere
Speaker 1: an actor can go. For example, if you wanted to
Speaker 1: shoot a film, you could put the camera on a
Speaker 1: dolly that isn't on a track. So there's some track systems.
Speaker 1: Those are great. If you have uneven ground or rough
Speaker 1: terrain and you put tracks down, you can have a
Speaker 1: nice smooth camera operation, but again you're limited to just
Speaker 1: moving on the tracks, or you could do a wheeled
Speaker 1: dolly that can move around uh an area, but then
Speaker 1: you need some that's an area that's pretty smooth and flat.
Speaker 1: That really still limits what you can do with a camera.
Speaker 1: So one of the ways that we have come up
Speaker 1: with two improve camera operation and to be able to
Speaker 1: go in the same places where it say performers can
Speaker 1: go is with steadicams. Now. The steadicam was invented in
Speaker 1: the mid nineteen seventies as a way for camera operators
Speaker 1: to move around while running a camera and get smooth footage.
Speaker 1: Whether it's with a documentary or with a film where
Speaker 1: you're following actual characters. The steadicam can remove the jitter
Speaker 1: created by walking or running. And when we move around
Speaker 1: in our own bodies, this is happening all the time.
Speaker 1: There's always this jitter as we're walking or running, but
Speaker 1: we don't really notice it because our brains are really
Speaker 1: good at smoothing all of that out to create a
Speaker 1: more steady experience in our consciousness. So it's only when
Speaker 1: you're really concentrating on it that you become aware of it.
Speaker 1: It's sort of similar to how the world does not
Speaker 1: spontaneously go away and then come back every time you
Speaker 1: blink your eyes. It's only when you really think about
Speaker 1: blinking your eyes that you start to notice it. And
Speaker 1: now you're noticing it, aren't you sorry about that? My bad?
Speaker 1: Don't worry. You'll you'll stop thinking about blinking your eyes
Speaker 1: in just a minute and then everything will be fine.
Speaker 1: So how does a steady cam stabilize the image so
Speaker 1: that you can get the really cool effects you see
Speaker 1: in movies like the Amazing copa shot in Good Fellas.
Speaker 1: So if you're not familiar with the sequence I'm talking about,
Speaker 1: it's it's in the movie. It's well in the film.
Speaker 1: There's a sequence that's a little bit more than two
Speaker 1: and a half minutes long, and it's a an uncut shot.
Speaker 1: It tracks two characters as they cross a New York street.
Speaker 1: They walk down some stairs into the kitchen of a
Speaker 1: busy restaurant. They walk through the kitchen, passing by dozens
Speaker 1: of extras as they're moving around in the kitchen as
Speaker 1: cooks or or waiters, they emerge onto a dining floor
Speaker 1: and they're seated right up front at a stage. And
Speaker 1: this is all in a single uncut shot. And it
Speaker 1: was using a film camera, not a digital camera. This
Speaker 1: was in the early nineties, and there were other challenges there.
Speaker 1: For example, the way the film camera worked. It was
Speaker 1: actually pulling film from one side of the camera and
Speaker 1: it would go in through the camera where you would
Speaker 1: expose the film to light coming through the camera. The
Speaker 1: exposed film would then go into a canister on the
Speaker 1: other side of the camera. Now, what that meant was
Speaker 1: that as you shot film, the weight of the camera
Speaker 1: started to shift because the side worthy the the exposed
Speaker 1: film was coming out that started getting heavier and heavier,
Speaker 1: so you had to compensate for that. It's actually pretty
Speaker 1: remarkable if you watched that sequence that it doesn't just
Speaker 1: slowly start tilting towards the right because the camera was
Speaker 1: getting progressively more heavy on the right side. By the way,
Speaker 1: if you're curious, there's some great behind the scenes documentaries
Speaker 1: and interviews about the copa shot. It's one of those
Speaker 1: things that's talked about in film school. It took them
Speaker 1: eight takes and that was it, and they actually finished
Speaker 1: it in half a day of shooting, which if you've
Speaker 1: ever been on a film shoot for a two and
Speaker 1: a half minute sequence uncut, to get completed in eight
Speaker 1: takes is pretty phenomenal. Also, it tells you the difference
Speaker 1: between someone, say like Martin Scorsese and Stanley Kubrick, because
Speaker 1: if Kubrick were shooting that, he would have still been
Speaker 1: doing it years later. Uh. Anyway, the steadicam was a
Speaker 1: big part of why this shot was even possible because
Speaker 1: it meant following these actors through these different environments, including
Speaker 1: downstairs and through a crowded kitchen, and it was something
Speaker 1: that you just could not do on a track or
Speaker 1: on a wheeled dolly. Uh. And it was all because
Speaker 1: of the steadicam. Now, let's say he came itself was
Speaker 1: invented again in the seventies, not in the nineties. It
Speaker 1: was invented by a guy named Garrett Brown who was
Speaker 1: a commercial director and a producer. He was not an engineer,
Speaker 1: and he was just trying to come up with a
Speaker 1: way to remove all this jetter that was coming around
Speaker 1: whenever you wanted to do a handheld shot and you
Speaker 1: wanted to follow an actor along a place where you
Speaker 1: couldn't have these other traditional camera setups, and he got
Speaker 1: a rough idea of it and called it the Brown's Stabilizer.
Speaker 1: In nine three, fortunately, a more refined version would become
Speaker 1: the first steadicam, and it consisted of three components. You
Speaker 1: had an articulated iso elastic arm that would attach to
Speaker 1: a sled. The sled was a kind of a it's
Speaker 1: it's a housing for the camera and for all the
Speaker 1: camera components. It looks like a big pole, and then
Speaker 1: the various components attached to either the top or the
Speaker 1: bottom of the pole, the camera at the top and
Speaker 1: then everything else closer towards the bottom. And then a
Speaker 1: vest that helped distribute the weight of the system and
Speaker 1: provide more stability. So you had an iso elastic arm
Speaker 1: that on one end would attach to the vest and
Speaker 1: thus have that, you know, just weight distribution. On the
Speaker 1: other end, it attached to the sled which held the
Speaker 1: camera and its various components like a battery pack, a monitor,
Speaker 1: that kind of thing. Now, the arm of the original
Speaker 1: steadicam was a lot like a swing arm lamp with
Speaker 1: a spring loaded arm. Uh if you ever look at
Speaker 1: one of those. In fact, I have some right in
Speaker 1: front of me right now, because the microphones I use
Speaker 1: are on that this style of arm, you have two
Speaker 1: bars that make up each segment of the arm, and
Speaker 1: they're in parallel with one another. Uh. These two bars
Speaker 1: would then end at metal blocks and they would also
Speaker 1: join together with a pivot point, a pivot joint that
Speaker 1: will allow them to kind of bend like a human
Speaker 1: arm would, and the camera sled would fit onto the arm.
Speaker 1: The sled would consist of that assembly that holds the camera,
Speaker 1: the battery, the motor, counterweights, and some systems. All of
Speaker 1: that's very important, and it's on was called the sled
Speaker 1: pole central piece of the sled, and that fits into
Speaker 1: the arm of the stead decam. Now, the way of
Speaker 1: the sled and the camera is constantly pulling the arm
Speaker 1: that's attached to the vest downward. Right. So imagine that
Speaker 1: you're holding a weight at arms length. Let's say it's
Speaker 1: a a fifteen pound weight. You're constantly feeling gravity pulling
Speaker 1: down on that weight that's being held out arms length.
Speaker 1: The steadicam arm is the same sort of thing. It's
Speaker 1: holding up the sled and the camera. Well, the way
Speaker 1: it holds it up and keeps it in the same
Speaker 1: relative position, so that the camera doesn't just continuously sink
Speaker 1: towards the floor. Is through this spring loaded system, it's
Speaker 1: counteracting that downward force. The parallel metal bars in each
Speaker 1: arm have these spring systems that are creating a force
Speaker 1: that's directly opposite the downward force of the weight from
Speaker 1: the sled and the camera. In fact, it's precisely calibrated
Speaker 1: so it will maintain that that way. The only time
Speaker 1: you change the elevation of the camera is if the
Speaker 1: camera operator wants to. The camera operator can raise or
Speaker 1: lower the camera, but it will then stay in that
Speaker 1: relative position relative to the camera operator because the arms
Speaker 1: spring loaded system is counteracting that downward pull from gravity.
Speaker 1: It's really clever. It's also very difficult, obviously to explain
Speaker 1: this in an audio format. Fortunately, there are numerous articles,
Speaker 1: including one on How Stuff Works, about how steadicams work,
Speaker 1: as well as videos on various platforms that show how
Speaker 1: steadicam systems work to kind of help you get a
Speaker 1: visual reference to what I'm talking about. So if you
Speaker 1: still are having trouble imagining this, I highly recommend checking
Speaker 1: out an article or watching a video to get a
Speaker 1: little deeper understanding. To get that spring system UH into
Speaker 1: further detail, it would require a very deep discussion, so
Speaker 1: and again it's very difficult to explain it in an
Speaker 1: audio format. So let's just say that there's a system
Speaker 1: of pulleys and springs inside these arms that provide that
Speaker 1: counter force, and will move on to talk about the
Speaker 1: sled and the sled pole. So the sled includes an
Speaker 1: element called a gimbal and a gimbals, a mechanism that
Speaker 1: keeps something in a position of relative stability despite a
Speaker 1: moving environment, typically horizontally, and in fact, gimbles have been
Speaker 1: around for centuries and the good old days ships used
Speaker 1: compasses mounted on gimbals, and that allowed the compass to
Speaker 1: appear horizontal even as the ship was pitching or rolling
Speaker 1: on rough seas. Typically gimbals consists of a couple of
Speaker 1: rings pivoted at right angles to achieve this effect, and
Speaker 1: so the UH, the the system around the compass moves,
Speaker 1: but the compass itself appears to be steady relative to you.
Speaker 1: The camera sled also changes a camera's center of gravity.
Speaker 1: So the center of gravity, obviously that's the point at
Speaker 1: which you can balance an object, and cameras typically there
Speaker 1: their center of gravity, it doesn't make it very easy
Speaker 1: to keep them nice and stable. But adding them to
Speaker 1: the sled, if you add mass to something, you change
Speaker 1: at center of gravity, and typically the the handle the
Speaker 1: control system for a steadicam is located very close to
Speaker 1: the actual center of gravity of the whole system. That
Speaker 1: allows you to have much more control over the camera
Speaker 1: and stabilize it. As such, the sled also changes the
Speaker 1: the moment of inertia for the camera system. Uh. That
Speaker 1: means that it becomes more resistant to rotation, and that's
Speaker 1: really important for image stabilization. To the effect of a
Speaker 1: steadicam is that you get this gliding shot that isn't
Speaker 1: bothered by all the shakes of walking or running. And
Speaker 1: there are tons of examples of steadicam shots in cinema.
Speaker 1: Uh the Good Fellows shot being a really famous one.
Speaker 1: Or there's also one in Kubricks The Shining there's a
Speaker 1: part where Danny is running away from his dad in
Speaker 1: a hedge maze in the snow, and we're following right
Speaker 1: behind him as he's making turns left and right, and uh,
Speaker 1: it's a steadicamp SHOT's pretty impressive. So that's another famous one.
Speaker 1: But there are tons of them in movies now. As
Speaker 1: we'll see in a bit, some cameras today incorporate equally
Speaker 1: ingenious ways to reduce unwanted motion in footage that do
Speaker 1: not require you to strap it to some sort of
Speaker 1: complicated apparatus that you have to then wear so that
Speaker 1: you can take some of the weight onto your own body.
Speaker 1: And we'll talk about him in just a second, but
Speaker 1: first let's take a quick break to thank our sponsor.
Speaker 1: All right, So let's say you want to get smooth video,
Speaker 1: or you plan on capturing still images, but you want
Speaker 1: to use a lower shutter speed to capture in low light,
Speaker 1: and you're gonna be holding the camera by hand, you're
Speaker 1: gonna want to reduce that jitter, or else what you'll
Speaker 1: get back won't look nearly as good as what you want,
Speaker 1: what you saw in person, unless you're really going for
Speaker 1: that shaky cam look, in which case image stabilization isn't
Speaker 1: really your bag. Baby. For a camera to correct for
Speaker 1: that kind of motion, you need a few elements. The first,
Speaker 1: which you'll find in both of the major solutions to
Speaker 1: this problem would be sensors that can detect camera motion.
Speaker 1: For a camera to correct for a shaking motion. It
Speaker 1: first has to detect that shaking is happening in the
Speaker 1: first place, or else nothing can happen. So let's consider
Speaker 1: the type of sensors found in these solutions that can
Speaker 1: detect camera motions. So, for example, we'll talk about optical
Speaker 1: image stabilization or o I S solutions. That's one of
Speaker 1: the two that we're going to chat about in this section.
Speaker 1: Optical image stabilization and in camera systems. Those are the
Speaker 1: two big ones. Both of those rely on sensors to
Speaker 1: detect when motion is happening. Otherwise there's no way to
Speaker 1: compensate for it, right, You have to know something's happening
Speaker 1: before you can fix it. Well, the optical image stabilization
Speaker 1: uses the optical pathway, in other words, the lenses to
Speaker 1: correct for camera shaking. And from a very basic standpoint,
Speaker 1: the secret is that these lenses have a specific movable
Speaker 1: lens of floating lens inside of them, so a an
Speaker 1: actual lens singular inside this lens. Casing will be movable
Speaker 1: with respect to the rest of the system inside of it.
Speaker 1: It can shift left, right, up and down. Movable lenses
Speaker 1: are pretty cool and I can't wait to talk more
Speaker 1: about in a second, but again, in order to move
Speaker 1: the lens to where it needs to be, you have
Speaker 1: to detect the shaking in the first place. It's a
Speaker 1: little complicated. So you've got these sensors, and with these solutions,
Speaker 1: that's typically two piezo electric angular velocity sensors, which do
Speaker 1: what gyroscopes do. And the reason you need two of
Speaker 1: them is because each sensor really only detects motion along
Speaker 1: one element of movement, like horizontal or vertical, So you
Speaker 1: need two of them at sort of ninety degrees from
Speaker 1: each other in order to detect vertical motion versus horizontal motion,
Speaker 1: and together they can detect the motion you would find
Speaker 1: in your typical camera shaking movements. They're not going to
Speaker 1: be able to fix anything that's dramatic, so if you
Speaker 1: whip the camera left or right or up or down,
Speaker 1: it's not gonna be able to compensate for that. But
Speaker 1: for just the small motions that our hands make while
Speaker 1: we're holding a camera and trying to capture an image,
Speaker 1: they can often correct for that. H So you gotta
Speaker 1: keep that in mind. It's really the jitters that these
Speaker 1: things fix, not the big stuff. Um. Also for the
Speaker 1: optical image stabilizers, they cannot correct for rotation along the
Speaker 1: optical axis. In other words, if you were to take
Speaker 1: your camera. Let's say you're holding up a camera and
Speaker 1: it's got a lock in on the way the image
Speaker 1: is showing up like landscape, and you start rotating the
Speaker 1: camera h so that you're doing either a clockwise or
Speaker 1: counterclockwise motion, tilting the image. These sorts of sensors don't
Speaker 1: detect that kind of momentum, that rotational momentum in that respect,
Speaker 1: so you could get tilt, but no jitter in this case.
Speaker 1: So how did those sensors work? Well, we gotta break
Speaker 1: it down, and it's a little a little scary when
Speaker 1: you see something saying piezo electric angular velocity sensors, What
Speaker 1: the heck does that mean? Well, if you break it down,
Speaker 1: it's not that tough. First, you've got piezo electric. You've
Speaker 1: probably heard of the piezo electric effect. This refers to
Speaker 1: the ability of certain materials to generate an alternating current voltage.
Speaker 1: When those materials are subjected to mechanical stress or vibration.
Speaker 1: They also will do the opposite. They will vibrate if
Speaker 1: you subject them to an alternating current voltage, So it
Speaker 1: goes either way. Quartz crystal do this, and that's why
Speaker 1: they were used in watches and still are in some watches.
Speaker 1: Uh So it's a very predictable behavior. If you know
Speaker 1: the the the basics of that material, you can replicate
Speaker 1: it over and over and over again. It's always going
Speaker 1: to be the same. Next, we have the term angular velocity,
Speaker 1: so that refers to the change in rotational angle along
Speaker 1: an access per unit of time, and we express this
Speaker 1: in degrees per second. That's how you measure angular velocity
Speaker 1: in degrees per second. So a piece of electric angular
Speaker 1: velocity sensor is one that detects changes in rotational angles
Speaker 1: along an access by generating an alternating current voltage in
Speaker 1: response to mechanical stress. And that mechanical stress is brought
Speaker 1: to us courtesy of the Coriolis effect or the Coreolis force.
Speaker 1: I really should say not the Coriolis effect, which is
Speaker 1: a very specific thing that refers to an enormous system
Speaker 1: called planet Earth. But the Coriolis force. This is an
Speaker 1: inertial force that was first described by stove Gasparred Coriolis,
Speaker 1: a French engineer of some renown, and as Encyclopedia Britannica
Speaker 1: puts it, quote Coriolis showed that if the ordinary Newtonian
Speaker 1: laws of motion of bodies are to be used in
Speaker 1: a rotating frame of reference, and inertial force acting to
Speaker 1: the right of the direction of body motion for counterclockwise
Speaker 1: rotation of the reference frame or to the left for
Speaker 1: a clockwise rotation, must be included in the equations of motion.
Speaker 1: So essentially it's talking about detecting specific types of velocity
Speaker 1: of changes in will not even changes, but just in
Speaker 1: the direction and speed of motion. The important thing to
Speaker 1: remember is that any motion would cause these piece of
Speaker 1: electric sensors to deform slightly, so they can take different shapes.
Speaker 1: And these are very tiny, tiny sensors, but they can
Speaker 1: take different physical shapes like a tuning fork shape is
Speaker 1: not uncommon. And as you move these things, it deforms them.
Speaker 1: They change their shape slightly because of that inertia, and
Speaker 1: that mechanical stress thus generates an alternating current voltage. Because
Speaker 1: they are piezo electric materials, the sensor will detect that
Speaker 1: change in voltage or that generation of voltage. And it
Speaker 1: gets a little more technical from here, but it also
Speaker 1: gets really complicated. So kind of like the steadicam, We're
Speaker 1: gonna take a bird's eye view of this. Essentially, the
Speaker 1: sensors consist of sensing arms and drive arms of this
Speaker 1: piezo electric material, and the emotion sensed produces a potential difference,
Speaker 1: an electrical potential difference. It's this potential difference that indicates
Speaker 1: to the sensor the change in angular velocity, and the
Speaker 1: sensor's output is an electrical signal which can then be
Speaker 1: processed by a microcomputer. The other big element to this
Speaker 1: optical image stabilization system is the movable lens, that floating
Speaker 1: lens inside the overall camera lens. So this is completely
Speaker 1: contained within a camera lens itself. If you've ever seen
Speaker 1: a camera where you've got a body that can then
Speaker 1: you can attach different types of lenses to it. You
Speaker 1: attach the lens, and if you need a different lens,
Speaker 1: you pop the first one off, you put a new
Speaker 1: one on. This system is completely contained within those individual
Speaker 1: lens cases. It's not in the actual camera body itself.
Speaker 1: So we're talking about a piece of glass essentially that
Speaker 1: shaped a very specific way, that's in a movable frame
Speaker 1: inside this lens, and movable so that it can go up, down,
Speaker 1: left or right, but it still remains aligned front and
Speaker 1: back with the rest of the lens assembly. So it's
Speaker 1: all meant to direct light back to the image sensor
Speaker 1: in a proper way. And you typically have a lens
Speaker 1: in the focusing group. That's the one that's closest to
Speaker 1: the end, the part that you're you know, is facing
Speaker 1: out to the outward world. That's the focusing group lens.
Speaker 1: And then towards the back of the lens array you
Speaker 1: have some other lenses that are meant to direct the
Speaker 1: light properly. Between those two sets is where you put
Speaker 1: the image stabilization lens. Really, you don't do it, the
Speaker 1: lens manufacturer does. Don't open your lenses, that's crazy talk.
Speaker 1: But the stabilization lens is in between these two other sets.
Speaker 1: So you got the focusing lens at the at one
Speaker 1: end of the the lens array. You've got the other
Speaker 1: groups in the very back that are directing it towards
Speaker 1: the image sensor. In between those, you have this floating
Speaker 1: image stabilizer lens, and it's not fixed in relation to
Speaker 1: the rest of the lens array the way the other
Speaker 1: lenses are. So it's in this frame that uses electro
Speaker 1: magnets to move the frame with respect to the rest
Speaker 1: of the lens array, and it's this frame that reacts
Speaker 1: to the information from the motion sensors. So the motion
Speaker 1: sensors are picking up jet and based upon those electrical
Speaker 1: potential differences, it's able to identify how much jeer is
Speaker 1: coming up and down versus left and right. It sends
Speaker 1: that information to the micro computer or micro controller rather
Speaker 1: that controls the movement of the frame holding this image
Speaker 1: stabilizer lens. The image stabilizer lens then is moved into
Speaker 1: place so that the light coming in through the focusing
Speaker 1: lens can be redirected towards the lenses in the back
Speaker 1: of this lens array to thus go to the image
Speaker 1: sensor in the very back of the camera. And it's
Speaker 1: the end effect is supposed to be as if there
Speaker 1: was not any jitter at all, as if there was
Speaker 1: no broken line between the light coming in through the
Speaker 1: focusing lens to the image sensor in the back. So,
Speaker 1: if you want to think about in another way, imagine
Speaker 1: that you've got a character, let's call him a Z's
Speaker 1: and the ceas has a mirror, and the seas is
Speaker 1: standing at the entrance of a cave and light is
Speaker 1: coming in, and you need to have some light directed
Speaker 1: from the outside of the cave into the cave. So
Speaker 1: you can read some really funky hieroglyphics that someone wrote
Speaker 1: years ago that suggested maybe aliens came down ages ago
Speaker 1: and you yell a seas light and the seas has
Speaker 1: to manipulate the mirror in such a way so that
Speaker 1: light coming from outside is then reflected to go further
Speaker 1: into the cave and illuminate the cave wall. That's what
Speaker 1: this image stabilizer lens is doing, except instead of reflecting
Speaker 1: light obviously, it's it's directing the light by making it
Speaker 1: go through a specific part of the lens. And yes,
Speaker 1: that was a fifth element reference for those of you
Speaker 1: who are paying attention, And if you don't know what
Speaker 1: that is, you should watch The Fifth Element because it's amazing.
Speaker 1: It doesn't have a lot of steadicam shots in it,
Speaker 1: but it's a great movie anyway. The cool thing about
Speaker 1: this particular approach, this optical image stabilization approach, is that
Speaker 1: it can turn any compatible camera into an image stabilized
Speaker 1: camera because all the technology is inside the lens itself.
Speaker 1: So if you have a lens that's got this image
Speaker 1: stabilization system in it, then you can attach that to
Speaker 1: any compatible camera and you get that image stabilization. The
Speaker 1: downside is it really makes those lenses more expensive, and
Speaker 1: lenses are not cheap to start off with, so if
Speaker 1: you're building up a selection of lenses and you want
Speaker 1: them all to have image stabilization capabilities, you start really
Speaker 1: racking up the cost pretty quickly. But there is another
Speaker 1: route to go, and that is called the in camera stabilizer.
Speaker 1: This takes a different approach to stabilization, but it uses
Speaker 1: a very similar philosophy. So instead of having a floating
Speaker 1: lens internally in that array that can move around and
Speaker 1: redirect light, UH, it actually has the image sensor itself
Speaker 1: on a movable frame. So this would be as if
Speaker 1: you could move that cave wall so that it was
Speaker 1: in line with the light. As opposed to moving the
Speaker 1: mirror to redirect the light to the cave wall, you're
Speaker 1: actually moving the sensor itself. Otherwise it's behaving pretty much
Speaker 1: the same way as the optical image stabilizer. These are
Speaker 1: also sometimes called mechanical image stabilizers UH to differentiate the two,
Speaker 1: or in camera image stabilizers. Because it's inside the camera,
Speaker 1: the body of the camera itself, not inside the lenses.
Speaker 1: So the big pro here is that if you've got
Speaker 1: that in the camera, it doesn't matter what kind of
Speaker 1: lens you use, because it's already got the image stabilization
Speaker 1: in there. The downside is, if you're using lenses for
Speaker 1: something that's further away, you're using like zoom lenses that
Speaker 1: kind of thing, the reduction in jender is less effective
Speaker 1: than it would be if you were using the optical
Speaker 1: image stabilizer. So depending upon your use, one may be
Speaker 1: better than the other. Depending on your budget, the in
Speaker 1: camera version may be better. And in fact, you can
Speaker 1: find this specific type of image stabilization, and lots of
Speaker 1: smartphones out there actually have this capability of moving the
Speaker 1: image sensor tiny tiny amounts to reduce jitter. Not all
Speaker 1: of them do this, by the way. Some of them
Speaker 1: use post process image stabilization, which we'll talk about in
Speaker 1: a minute. So it's kind of a neat an elegant solution. Um,
Speaker 1: if you have stabilized your camera, let's say that you've
Speaker 1: got one of these two systems in place in your
Speaker 1: camera system. Uh, and let's say that you've decided to
Speaker 1: to really lock down your camera. Let's say that you
Speaker 1: put it on a tripod, you actually probably want to
Speaker 1: turn off image stabilization at that point, and if you
Speaker 1: don't turn it off, the motions of the stabilizer itself
Speaker 1: can end up being picked up by the system, which
Speaker 1: then tries to compensate for that movement. Which puts me
Speaker 1: in mind of the time I was in college and
Speaker 1: tried tight rope walking for the first and only time
Speaker 1: in my life. So let me explain, because this analogy
Speaker 1: I think is very apt. When I tried tight rope walking,
Speaker 1: I stepped on the tight rope and it was really
Speaker 1: something between a tight rope and a slide rope, and
Speaker 1: as I put weight on my leg, my leg began
Speaker 1: to shake. Now that caused the rope to move around,
Speaker 1: and I tried to catch my balance but found myself overcompensating,
Speaker 1: so I would shift and then the rope would move
Speaker 1: the other way, and I try and shift again, and
Speaker 1: it was just getting worse and worse, and it caused
Speaker 1: my leg to shake more, and instead of studying myself,
Speaker 1: I just found the lower half of my body was
Speaker 1: going crazy and not in a fun way. So, after
Speaker 1: approximately fifteen seconds of trying this, I realized that tight
Speaker 1: ropes were probably just one of those things I wasn't
Speaker 1: meant to experience, and I stopped. Now, the image stabilizer
Speaker 1: system kind of behaves the way my legs did on
Speaker 1: that tight rope in these situations. So you've got your
Speaker 1: image stabilized camera on a tripod and your image stabilization
Speaker 1: is turned on. A little motion within the system itself
Speaker 1: could be picked up as a camera shake, so then
Speaker 1: it tries to compensate, but because the camera is not
Speaker 1: actually shaking, it's on a steady tripod, uh, it then
Speaker 1: starts to detect the solution for that shake as its
Speaker 1: own problem, and it becomes this feedback loop. And this
Speaker 1: can actually introduce motion, blur and jitter in your photos
Speaker 1: even and your video even though you've got your camera
Speaker 1: mounted to a stable tripod. So you might want to
Speaker 1: turn off image stabilization in that process unless you plan
Speaker 1: on doing a panning shot. So panning shots can be
Speaker 1: tricky to you want a nice even pan You're either
Speaker 1: going left to right or right to left, and you
Speaker 1: want to avoid introducing any jitter in the image. Vertically. Well,
Speaker 1: some image stabilizers have a panning mode so that way
Speaker 1: they compensate for vertical movements, but not horizontal movements, and
Speaker 1: those systems will remove some of that jitter while still
Speaker 1: allowing for a nice smooth panning motion. With both optical
Speaker 1: image stabilizers and mechanical image stabilizers, it took a lot
Speaker 1: of engineering to figure out precisely how to make the
Speaker 1: internal mechanisms respond in such a way to preserve the
Speaker 1: integrity of an image while simultaneously removing jitter. And while
Speaker 1: the basics could be found in much older technologies and
Speaker 1: use for centuries, like the gimbal, getting that precision and
Speaker 1: response time to a level that is useful in a
Speaker 1: dynamic use case such as taking video or photos required
Speaker 1: a whole lot of engineering. So I'm really impressed at
Speaker 1: this technology. But we still have one more variation to
Speaker 1: talk about because some systems don't use any moving parts
Speaker 1: at all to create image stabilization. Instead, they try to
Speaker 1: beat the problem with software in a post process solution.
Speaker 1: So how does that work? Well, we'll find out, but
Speaker 1: first let's take another quick break to thank our sponsor.
Speaker 1: Some digital cameras, particularly some smartphones, have what is called
Speaker 1: virtual image stabilization or electronic image stabilization, or sometimes just
Speaker 1: post process image stabilization, and these systems don't use moving
Speaker 1: parts to adjust sensors or lenses in order to keep
Speaker 1: the image nice and steady. Instead, they use software. So
Speaker 1: what's going on here, Well, from a high level perspective,
Speaker 1: a program attempts to reverse any shake found in an
Speaker 1: image algorithmically, and some do this in a pretty basic way.
Speaker 1: Imagine you have a video open on YouTube. So let's
Speaker 1: just say you've got a regular video. It's not full frame,
Speaker 1: it's just the video in YouTube's desktop application. Now, imagine
Speaker 1: that the actual video footage extends beyond the borders of
Speaker 1: that video frame. I mean, even if you were looking
Speaker 1: at it in full screen mode. Imagine that the video
Speaker 1: itself would extend a little bit beyond every single border,
Speaker 1: just a touch. So what you're seeing is not the
Speaker 1: full frame of video. It's a section, a cropped section
Speaker 1: of that video. The edges are cut off because those
Speaker 1: edges provide some cheat room, a little bit of buffer
Speaker 1: for the purposes of image stabilization. Now, this approach relies
Speaker 1: on some assumptions. The program does not necessarily know which
Speaker 1: elements of a video you're really interested in, so it
Speaker 1: has to make some guesses. Let's say that you're taking
Speaker 1: a video of a kid running uh and the kids
Speaker 1: running out though through the snow in a hedge maze,
Speaker 1: and you're running after the kid. Maybe you're shouting out
Speaker 1: encouraging words about this nifty hotel you've been hired to
Speaker 1: look after, and your video footage would be really shaky
Speaker 1: because you're holding the camera as you're running. You don't
Speaker 1: have a steady camp, so every single run step you're
Speaker 1: taking it's it's jittering the camera. But feeding the footage
Speaker 1: through an algorithm can smooth things out a bit, and
Speaker 1: the algorithm recognizes that the kid you're chasing is the
Speaker 1: interesting thing in the frame. It's doing this through some
Speaker 1: processing and figuring out which pixels are changing the most
Speaker 1: and which ones are staying more or less the same,
Speaker 1: and kind of drawing some conclusions based on that. So
Speaker 1: while the camera shakes around, the algorithm repositions the frame
Speaker 1: of view for the audience, so the kid remains more
Speaker 1: or less in the same general area relative to the screen.
Speaker 1: So you can think of it as like a a
Speaker 1: picture in picture sort of thing, and the picture in
Speaker 1: picture that frame is moving around with relation to the
Speaker 1: rest of the the the the big picture view. But
Speaker 1: if you just stare at whatever is happening inside that
Speaker 1: picture within picture, it looks nice and steady, or at
Speaker 1: least compared to the overall image. Uh. This is not
Speaker 1: a perfect system, obviously, it can sometimes be very off putting,
Speaker 1: but it is a common one that's in use in
Speaker 1: post process image stabilization. The way this is done practically
Speaker 1: has changed over the years, like the way that people
Speaker 1: have actually designed the algorithms so that they could make
Speaker 1: this happen. That has changed. So a very early version
Speaker 1: of this would have a camera essentially the image processing
Speaker 1: software identify a point in the background that was clearly
Speaker 1: defined and lit. So let's say that you've got uh
Speaker 1: an image of a person standing in a field, and
Speaker 1: there's a some wooded forest as opposed to the unwitted
Speaker 1: forest in the background, and there's a particular tree that's
Speaker 1: a nice sharp relief. Well, the image processing software might
Speaker 1: focus on that tree and say, all right, we're going
Speaker 1: to lock onto this section of the tree, and we
Speaker 1: want that section of the tree to be in this
Speaker 1: relative position in our frame of view for the duration
Speaker 1: of this video. So you're hand holding the camera pointing
Speaker 1: it at somebody who standing in the field chatting, and
Speaker 1: because of the image processing, trying to keep that one
Speaker 1: part of the tree in that one part of the frame.
Speaker 1: It stabilizes the image even if there's a little bit
Speaker 1: of jitter as you're holding it pointing it at the
Speaker 1: person who's talking in the video. Uh, that works, okay,
Speaker 1: it's it's a little primitive. You could actually do a
Speaker 1: fairly nice static shot that way, but you can't move
Speaker 1: the camera at all because if you start moving the
Speaker 1: camera purposefully, i mean, beyond just the little jitters, then
Speaker 1: that frame of reference is going to move dramatically and
Speaker 1: the software can and handle that. It has to, you know,
Speaker 1: keep an eye on a relatively stable shot. So this
Speaker 1: is for static images static video. A slightly more advanced
Speaker 1: version of that same approach would pick to reference points,
Speaker 1: so one on either side of the frame, and then
Speaker 1: it was essentially draw an imaginary line between those two points.
Speaker 1: So let's say it's identified a tree on one side
Speaker 1: and a bush on the other side, and said, all right,
Speaker 1: based upon this setup, we want these two points to
Speaker 1: remain the same general spots in your frame of view,
Speaker 1: and the line that's between them will always be at
Speaker 1: the same alignment no matter what. This would allow you
Speaker 1: to actually have some rotation of the camera as well,
Speaker 1: and the processor could could account for that and correct
Speaker 1: for it. So, let's say you're holding a smartphone and
Speaker 1: you're trying to take video of someone, and you're using
Speaker 1: this particular version of image stabilization. If the outside of
Speaker 1: your smartphone where to dip down a little bit and
Speaker 1: the left side lifts up a little bit, thus you
Speaker 1: would have a little bit of a tilt to the
Speaker 1: video before you corrected it. Because of this particular form
Speaker 1: of post process image stabilization, it would detect that change
Speaker 1: relative to that imaginary line and correct for it. Uh,
Speaker 1: depending on how dramatic your turn was. You might actually
Speaker 1: notice this while looking at the playback, because unless you
Speaker 1: crop further into the image, you might start seeing the
Speaker 1: edges of where the cut off is for the video
Speaker 1: popping up and it can be a little weird. You
Speaker 1: may have even seen this in some videos online where
Speaker 1: you start seeing an edge kind of creep into the
Speaker 1: video a little bit. That's due to this post process
Speaker 1: image stabilization, whether it was for jitter or for rotation,
Speaker 1: and it can be very off putting. That's why most
Speaker 1: people who use this, who are actual video editors, will
Speaker 1: digitally punch in a little bit. They'll crop the image
Speaker 1: so that they cut off those edges so that you
Speaker 1: can't see that when it happens. Of course, the problem
Speaker 1: with that is that you have to be at a
Speaker 1: high enough resolution where when you digitally punch in, it's
Speaker 1: not it's not a noticeable decrease in quality of the
Speaker 1: actual image itself. For moving shots, visual image stabilization might
Speaker 1: look at individual pixels and track how they change over time,
Speaker 1: and they interpret that as motion. So if these pixels
Speaker 1: are changing in ways where you see one pixel changing
Speaker 1: and the pixel next to it has become the same
Speaker 1: as the pixel that was to its left, and then
Speaker 1: two pixels down it becomes what was two pixels to
Speaker 1: the left, etcetera, etcetera. It can then start to interpret
Speaker 1: this as motion and starts to work out where things
Speaker 1: are moving, and then and editing, you can again crop
Speaker 1: your video to remove those jolting edges and punch in
Speaker 1: a bit. Some folks from Google and Georgia Tech actually
Speaker 1: developed a new method of virtual image stabilization a few
Speaker 1: years ago. So I guess it's not new now, but
Speaker 1: it was a couple of years ago, and they published
Speaker 1: their work in a paper titled Auto directed Video Stabilization
Speaker 1: with Robust L one Optimal Camera Paths. The paper also
Speaker 1: gets super technical. It is out there free for you
Speaker 1: to read, so feel free to seek it out if
Speaker 1: you are technically minded. Here's the bit that I think
Speaker 1: is really important. They lay out that this post process
Speaker 1: video stabilization requires three steps, and the first is to
Speaker 1: estimate the original camera path, which is the one that's
Speaker 1: all shaky and stuff. It's the one you want to fix.
Speaker 1: The second step is to estimate a new smooth camera path,
Speaker 1: and the third step is to synthesize a stabilized path
Speaker 1: by following the estimated smooth path. This is not easy
Speaker 1: to do, and there are lots of different ways to
Speaker 1: try and do it, and we're always seeing developments in
Speaker 1: this space. However, that being said, even as this post
Speaker 1: process stabilization technique advances and it evolves, I think most
Speaker 1: filmmakers would argue that the optical image stabilization and the
Speaker 1: in camera stabilization systems are far superior that you can
Speaker 1: get some image stabilization that's all right for basic use
Speaker 1: in this post process approach, especially if you're going to
Speaker 1: just do something like share something online, like it's a
Speaker 1: online video or you know something on Facebook or Twitter,
Speaker 1: it's not that big a deal to have this post
Speaker 1: process image stabilization then. But if you want something that
Speaker 1: is more of a professional level, hands down, the argument
Speaker 1: I have seen is that you should go optical image
Speaker 1: stabilization if you can, in camera stabilization if you if
Speaker 1: optical is too cost prohibitive, But either of them are
Speaker 1: far more reliable and effective than post process image stabilization,
Speaker 1: and they are not going to create the weird artifacts
Speaker 1: that you might see using a software based solution. So
Speaker 1: it turns out that in this case, the pchanical one,
Speaker 1: the electronic mechanical one, might be better than the software one.
Speaker 1: That may not always be the case. We may eventually
Speaker 1: get to a point where they are advanced enough algorithms
Speaker 1: and advanced enough cameras where it becomes a non factor,
Speaker 1: but we're not there yet. So generally speaking, image stabilization
Speaker 1: is all about trying to take out the frailties of
Speaker 1: being human, trying to remove that little element of humanity
Speaker 1: where we get that imperfection that creeps into our art.
Speaker 1: And for some of us. That means that we get
Speaker 1: an image that we actually really wanted to convey to
Speaker 1: our audience. For other people, they may say, well, that
Speaker 1: kind of removes some of the humanity from the art itself,
Speaker 1: and I'm not here to make either argument. I think
Speaker 1: that there are some amazing uses of image stabilization that
Speaker 1: create really compelling effects and are great for storytelling, and
Speaker 1: I think there are elements where you don't want that,
Speaker 1: where you want something more natural, a stick and shaky,
Speaker 1: to convey a specific kind of mood or tone, And
Speaker 1: really it comes down to the intent of what you
Speaker 1: are doing and the theme that you're going for. I
Speaker 1: don't think that either is necessarily inferior or superior to
Speaker 1: the other, and I enjoy plenty of work that incorporates
Speaker 1: both types of elements, hopefully purposefully. Sometimes you get these
Speaker 1: these effects just by happenstance because people just didn't know
Speaker 1: any better, and that can still be effective, but it's
Speaker 1: a little less special to me than when people go
Speaker 1: into it knowing what they're doing. Thank you so much
Speaker 1: for joining me on this episode. It was a lot
Speaker 1: of fun to look into a more technical aspect. I've
Speaker 1: got another one coming up soon. That's also extremely technical,
Speaker 1: and also was a suggestion that was left in the
Speaker 1: twitch dot tv slash text stuff chat room. Remember, on
Speaker 1: Wednesdays and Fridays, I do live stream my recordings of
Speaker 1: text so just go to twitch dot tv slash tech Stuff.
Speaker 1: You can see the schedule there. Also, if you have
Speaker 1: any suggestions for future episodes of tech Stuff, you can
Speaker 1: send me a message. My email address is tech Stuff
Speaker 1: at how stuff works dot com. People ask me for
Speaker 1: it all the time. I say it in every single
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Speaker 1: you can drop me a line on Facebook or Twitter.
Speaker 1: The handle at both of those is text stuff hs W.
Speaker 1: That's it for me. I'll tell it to you again.
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Speaker 1: because at how stuff works dot com.