Much of my writing on the benefits of working linear is tied to the
eLin documentation.
It occurred to me that it might be helpful to describe these advantages
in more general terms, and to provide equivalences of the eLin color
pipeline for two popular floating point compositing apps.
First, let's get some terms straight. A lot of people use the term
linear
to describe images that look correct on their displays without any
color correction. In visual effects circles we sometimes hear about
converting Cineon images from log space to linear so they look
right.
When I use the term linear, I am talking about something
else. I am talking about a linear measure of light values. I freely
intermingle terms like
photometrically linear, radiometrically linear, scene-referred values, gamma 1.0, and just plain old
linear when describing the color space in which pixel values equate to light intensities.
If
you were to display such an image on a standard computer monitor
without any correction, it would look very dark. The best way to
visualize this is to think about the middle gray card you bought when
you took your first photography class. It appears to be a value midway
between black and white, both to our eyes and in our correctly-exposed
shots, and yet it is described as being 18% gray.
Wed want
images of this card to appear at or near 50% on our display. But in
scene-referred values, an object that is 18% reflective should have
pixel values of 18%, or 0.180 on a scale of 01.
Virtual Graycard Comparotron2000:
Linear image with no LUT (card = 0.18, or 18%)
Image with a 2.2 LUT applied (card = 0.46, or 46%)
If your digital camera didn't introduce a gamma 2.2 (or thereabouts)
characteristic into the JPEGs it shoots, they'd look like the linear
example above. The images where the card looks right are variously
described as
perceptually encoded, gamma encoded, or they may even be identified as
gamma 2.2 encoded, or having a
gamma 2.2 characteristic curve. A specific variant of gamma 2.2 goes by the name
sRGB. In an attempt to create a catchy (and catch-all) term, the eLin documentation refers to these color space collectively as
vid, since NTSC video has a gamma of 2.2 (kindasorta), and since these images look right on a video monitor.
Many
of the image processing tools we use behave differently when performed
at different gammas. If you gamma an image dark, blur it, and gamma it
back up (inverse of gamma = 1/gamma), you get a different result than
if you simply blur the image.
When you convert an image to
linear space, your subsequent image processing operations better match
real-world physical properties of light. If you are accustomed to
processing perceptually encoded images, you will probably find that
switching to g1.0 processing will make your familiar effects look more
organic (with a few notable exceptions to be covered in a later
article).
.
