To help us understand dynamic range, let's first define the term luminance.
Luminance is basically a measure of the intensity of light per unit area.
And this is again, kind of accounting for
also the light traveling in a given direction.
So any time it, light is hitting any surface area, the direction it's coming
in from, and the luminous intensity per unit square area of that
region is what we're looking for, and that's what the measure of luminance is.
And it's ca, measured in candela per meter squared, cd over m squared.
To help us quantify this, let's look at the whole range of luminance.
Now I know we've, in the past, always looked at a black and white image or
a range of zero to one, or zero to 50, to 255 in a different set of numbers.
But now, we're going to look at it in terms of luminance, on a surface, or
an object, or a scene.
And in this case, let's look at these values.
And again basically, I'm showing you that this is a log plot,
going from eight, six, four, two, zero, minus ten, minus four, and minus six.
So, of course, that's why it kind of has a linear thing.
But these are again, numbers that are much more in detail,
kind of showing you things about what's happening in an image.
And I've just now marked in this range basically in indoor,
sunlight, sunlight is much brighter, and
in fact, there's even something much even brighter than that.
Indoor images would have these type of ranges here.
So this point is basically showing sunlight.
This would be indoor.
These are the kind of images, we looked at it.
If it, this scene was lit by just moonlight, directly captured,
that's what we would have.
And, of course, this is starlight.
Now, one thing to note,
the human vision system, can measure static contrast ratio.
That is, being able to kind of see the range at any moment, when I'm looking at
a scene, are from hundred to one, so ten raised two to one.
And again, the human eye, in a static case,
can also adjust itself by about 6.5 f-stops.
Remember the term of f-stops, when we looked at from the camera where,
when we actually looked at what were the f-stops.
And we looked at a variety of ways of what of happens.
And that basically means is, how wide the aperture is opening up that allows
us to capture more light, or less light, depending on the size of the aperture.
Now, I do categorize this in two different ways.
One, I'm talking about static contrast ratio.
Now, another one is dynamic contrast ratio.
That is meant for if the scene is dynamically changing.
And in this case, our human vision system can actually do much better.
And in fact, we see a range from 1 million, ten raised to six, to one.
And basically, this can be captured with about 20 different f-stops.
So you notice,
the range goes from 6.5 to 20, when we go from static to dynamic.
Now the big difference that I want to point out between static contrast and
the static scene basically is, nothing is changing.
With dynamic is when the, the illumination in the scene is changing.
Now this could be because the scene is dynamically changing, or
somebody is changing the lighting conditions, and this could be again,
the sun is moving around, there's shadows and stuff like that.
So that's the difference.
Of course, human eye system, as we've talked about before,
is a pretty impressive bit of technology, and
it can adapt quite quickly to a variety of shades.
And again, something to think about when you start thinking about the human
saccade system,
and I encourage you to look at that kind of stuff on the web, because it
allows you to kind of adapt very quickly from one brightness to the other.
Now partly what we are trying to do with this representation of dynamic range,
is to create images that will allow us to actually capture dynamic range
in one static image.
That actually is somewhat similar to what our human vision can,
system can do.
By moving our eyes around in saccades and stuff like that, we can see a lot of
dynamic range, and our eyes adapt very much to bright and low light situations.
And we can see much more detail.
Of course, cameras at present cannot do this, and
that's one of the questions for computational photography is,
how do we kind of, bring in the computation to make it do the adaptation
to what it's seeing in the light, and being able to capture images?
And of course, at the end of it, we want to create sometimes a,
just a static image that captures it.
You'll see more examples of this as we continue talking more.