seeing isn't believing: the context of color
Seeing is believing, or so we think. One of the problems we face in photography is the difference between the way machines -including our digital cameras- and humans 'see' color. While understanding the differences has always been important in photography, it is even more important in digital photography since we rely more heavily on software and electronics throughout the workflow. The illustrations in this article point out one of the key differences between machine and human 'vision'.
The Context of Color
A fundamental difference between machines and humans is that humans see color in 'context'; the appearance of a particular color changes according to its surroundings. Machines, on the other hand, could care less about surroundings; a particular color looks exactly the same regardless of surroundings. This poses a problem when we rely on equipment and software to make judgments about color, because they don't anticipate how a human might see color within various contexts.
There is no question that squares "A" and "B" in this example are different from each other: square "A" is darker than square "B". The real question is why do they look different? Both squares are identical in value (135 on a scale of 0-255), that is, they are the exact same shade of gray. Move your pointer over the illustration to compare.
Move pointer over illustration to compare.
This visual difference has consequences beyond what may seem obvious. If square "A" represents a color we are attempting to match, say a gray shirt, and both "A" and "B" are identical... then which one is correct? If we consider "A" to be visually correct, then "B" is incorrect - even though it's an exact match of "A". "B" is incorrect because it's the wrong shade of gray when viewed within the context of the darker squares surrounding it.
Squares "A" and "B" are identical shades of gray, but appear to be different because of the difference in their surroundings.
If "A" and "B" represent two identical gray shirts in an actual photograph, where one shirt is surrounded by a light background and the other by a dark one, they will not appear to match even though they are identical.
In order to create a visual match, shirt "B" needs to be darkened. In other words, for the two shirts to match... they must be different colors.
In illustration 2. "B" has been altered to provide a visual match to "A". Move pointer over illustration to see the actual difference between these two "matching" colors.
Move pointer over illustration to compare.
These previous illustrations point to the somewhat disconcerting fact that for some colors to appear "correct" within a given context, they must actually be incorrect. Machines cannot make these decisions for us because unlike us, they do not see color in context.
There are 11 solid blocks in the following image ranging in tone from solid black to solid white. The light-to-dark gradient that appears within each box is an illusion, caused by the proximity to blocks of lighter & darker tones.
As the blocks are separated, the illusion of a gradient disappears.
The phenomenon illustrated in the previous examples is referred to as Simultaneous Contrast. Understanding that the presence of one color can impact our perception of another is quite useful. Aside from using this knowledge in image editing and color matching, it is routinely used to choose mattes, and to design borders, that have a positive effect on a photograph's presentation.
A large white border surrounding an image tends to soften, or de-saturate color, while a black border has the opposite effect of deepening color, or adding saturation. Different colors of mattes all have a perceptual effect on the image they surround. It pays to experiment with different mattes, rather than always selecting a color that matches, or pulls from something within the image. Trying opposite colors, as well as light and dark mattes, may lead to discovering a unique combination that may not have been obvious.
Our perception of color is influenced by a color's surroundings or context. The same identical color can be perceived as two or more different colors. If a color 'appears' different (as in the two gray shirts mentioned in illustrations 1 & 2), we treat it as though it is different, making corrections that force it to match - even if doing so creates a numerical mismatch resulting in a new color... perception trumps math, even if our perception is wrong.
Machines are unfailingly accurate but don't view color the way humans do. Always keep in mind that we don't produce photographs for a machine's enjoyment, but for ours.
Optical 'illusions' work (and entertain) because they illustrate curious things about our perception. In many cases they help to improve our photography by teaching us lessons about perception versus reality that we can apply to our work.
Other Resources and Illustrations
A Google search on Visual Illusions will return a large number of links to interesting examples of all kinds. As you read, keep in mind that many of these provide subtle lessons we can use to improve our photography and to solve problems that might otherwise leave us stumped.