I’m sure you are aware that different colors evoke different feelings on people. Like, for instance, orange looks warm and perhaps aggressive, while blue is cold and calm. Green is the color of hope and also envy, whereas red is passion, white means purity, black can represent death… and so on.
In film we are constantly being bombarded with arrangements of carefully composed and colored frames. But have you stopped to think up to what extent the director and cinematographer are purposely sending a message through the use of color? Any given production has a thought behind it and most of the times nothing is casualty. Let’s go on and analyse Black Swan.
Black Swan (2010)
Cinematographer: Matthew Libatique
Nominated for the 2011 Academy Award for Best Cinematography.
What are the main colors in this film? Clearly Black and White, but also Pink and Green. Here are examples of how these colors are being used.
White is clearly meant for purity and virginity. That is why Natalie Portman’s Nina dressed in white for most of the film, right until the climatic point after her false encounter with Lily (Mila Kunis).
Nina’s past looks pink. References to her childhood and her upbringing are pink. Its girly and childlike allure points out her naivety, and how she is a child inside that has grown up but is still totally immersed in the small inner world of her and her mother, unaware of other world problems or issues outside her close surroundings.
But the story is not all about Nina and her psychological derail. It’s also about her mother Erica (Barbara Hershey) as a narcissist force that abuses her daughter. The envy on the part of the mother plays a huge part in the process, especially in view of the fact that Erica, who was also a budding ballerina, never made it to soloist status. We see evidence that the mother uses Nina to meet her own needs both in her attempt to live vicariously through Nina’s dancing but also in her undermining of Nina becoming too successful.
Nina needs Erica to function and to be a ballerina; Erica needs Nina to live. It’s a symbiotic relationship. When Nina masturbates as “homework” to try to access her inner passion, she sees her mother asleep in her bedroom. This was a metaphor for her mother being connected to her sexuality, and Nina’s attempts to awaken her passion would also awaken her mother. In fact, the mother is the focal point in the audience when Nina takes her suicidal plunge as the White Swan at the end of the ballet. Fellow ballerina Lily tries to befriend Nina, which connects Lily to Nina’s mother in Nina’s head. Seduction must be followed by betrayal in Nina’s head, which causes Nina to view Lily as a rival trying to take away her role in the ballet. Nina can only become the Black Swan (again, in her own head) by killing Lily, which by extension is killing her mother.
Black is pure evil, represented by the mother’s clothing, but also in Lily’s, who is, in fact, a representation of Erica in Nina’s mind.
It’s only after the climax, where Nina finally lets her sexuality free, that we see her dress in Black. Also, during that night, when they are still at the club, light purposely changes from pink to green as Nina battles her way through her repressions and mother issues.
In digital imaging, colorimeters are tristimulus devices used for color calibration. Accurate color profiles ensure consistency throughout the imaging workflow, from acquisition to output. By using a colorimeter we can measure the amount of each of the three primary colors in the mix:
where S1, S2 and S3 can be either positive or negative.
An equal energy white will clearly be given by 3 components of the same value, but, because the scale/precision of each color axis is different (due to human color sensitivity curve), an equal energy white would be more likely to be as follows, which, normalised by each color axis, should then produce a white that is of equal energy.
Another issue is representing a 3D system of color by dividing each color component by the luminance, so that we can model color as a 2D triangle.
HSL and HSV
HSL (hue-saturation-lightness) and HSV (hue-saturation-value) are the two most common cylindrical-coordinate representations of points in an RGB color model. The two representations rearrange the geometry of RGB in an attempt to be more intuitive and perceptually relevant than the cartesian (cube) representation, by mapping the values into a cylinder loosely inspired by a traditional color wheel.
The angle around the central vertical axis corresponds to “hue” and the distance from the axis corresponds to “saturation”. These first two values give the two schemes the ‘H’ and ‘S’ in their names. The height corresponds to a third value, the system’s representation of the perceived luminance in relation to the saturation.
Perceived luminance is a notoriously difficult aspect of color to represent in a digital format, and this has given rise to two systems attempting to solve this issue: HSL (L for lightness) and HSV or HSB (V for value / B for brightness). A third model, HSI (I for intensity), common in computer vision applications, attempts to balance the advantages and disadvantages of the other two systems.
Other, more computationally intensive models, such as CIELAB or CIECAM02 are said to better achieve the goal of accurate and uniform color display, but their adoption has been slow. This 4-color system is the base of color processing on digital photographic development. Programs like Lightroom or Photoshop Camera Raw show a and b controls under the titles Temperature and Tint
RGB to HSL
Back to HSL… to calculate an HSL value from a RGB value we need to know which are the maximum and minimum components from the sample.
Say, for example, Raspberry: RGB(214, 39, 134). The maximum would be R and the minimum G, being B the medium value. This value would lie in the sixth section of the following diagram where the 255 possible values have been divided into six equal sections.
So the values would be as follows:
A similar approach for calculating the hue is as follows. As hue is perceived to be circular it is very intuitive to use degrees instead of values, so for this case we would just have to divide 360º of the spectrum into 6 sections.
Chroma and Saturation
Because these definitions of saturation – in which very dark (in both models) or very light (in HSL) near-neutral colors, for instance, are considered fully saturated – conflict with the intuitive notion of color purity, often a conic or bi-conic solid is drawn instead, with what this article calls chroma as its radial dimension, instead of saturation.
Some useful definitions to avoid misunderstandings are:
Intensity radiance: The total amount of light passing through a particular area.
Chroma: The colorfulness (amount of color) relative to the brightness of a similarly illuminated white.
Saturation: The colorfulness (amount of color) of a stimulus relative to its own brightness.
Hue and Chroma
Both hue and chroma are defined based on the projection of the RGB cube onto a hexagon in the “chromaticity plane”. The projection takes the shape of a hexagon, with red, yellow, green, cyan, blue, and magenta at its corners. Chroma is the relative size of the hexagon passing through a point (modulus of the point from the origin), and hue is how far around that hexagon’s edge the point lies (angle of the vector to a point in the projection,with red at 0°).
More precisely, both hue and chroma in this model are defined with respect to the hexagonal shape of the projection. The chroma is the proportion of the distance from the origin to the edge of the hexagon. In the lower part of the diagram to the right, this is the ratio of lengths OP/OP′, or alternately the ratio of the radii of the two hexagons. This ratio is the difference between the largest and smallest values among R, G, or B in a color. To make our definitions easier to write, we’ll define these maximum and minimum component values as M and m, respectively.
To understand why chroma can be written as M − m, notice that any neutral color, with R = G = B, projects onto the origin and so has 0 chroma. Thus if we add or subtract the same amount from all three of R, G, and B, we move vertically within our tilted cube, and do not change the projection. Therefore, the two colors (R, G, B) and (R − m, G − m, B − m) project on the same point, and have the same chroma. The chroma of a color with one of its components equal to zero (m = 0) is simply the maximum of the other two components. This chroma is M in the particular case of a color with a zero component, and M − m in general.
The hue is the proportion of the distance around the edge of the hexagon which passes through the projected point, originally measured on the range[0, 1] or [0,255] but now typically measured in degrees [0°, 360°]. For points which project onto the origin in the chromaticity plane (i.e., grays), hue is undefined.
Sometimes for image analysis applications, this hexagon-to-circle transformation is skipped, and hue and chroma (we’ll denote these H2 and C2) are defined by the usual cartesian-to-polar coordinate transformations (right). The easiest way to derive those is via a pair of cartesian chromaticity coordinates which we’ll call α and β:
(The atan2 function, a “two-argument arctangent”, computes the angle from a cartesian coordinate pair. The first argument is the vertical or y-axis value, and the second argument is the horizontal or x-axis value. In some computer programs, like Excel, the order is reversed.)
Notice that these two definitions of hue (H and H2) nearly coincide, with a maximum difference between them for any color of about 1.12° – which occurs at twelve particular hues, for instance H = 13.38°, H2 = 12.26° – and with H = H2 for every multiple of 30°. The two definitions of chroma (C andC2) differ more substantially: they are equal at the corners of our hexagon, but at points halfway between two corners, such as H = H2 = 30°, we have C = 1, but C2 = √¾ ≈ 0.866, a difference of about 13.4%.
In color reproduction, including computer graphics and photography, the gamut, or color gamut, is a certain complete subset of colors. The most common usage refers to the subset of colors which can be accurately represented in a given circumstance, such as within a given color space or by a certain output device.
Another sense, less frequently used but not less correct, refers to the complete set of colors found within an image at a given time. In this context, digitalizing a photograph, converting a digital image to a different color space, or outputting it to a given medium using a certain output device generally alters its gamut, in the sense that some of the colors in the original are lost in the process.
If we represent a gamut as a simplistic triangle, white would be in its center. The sum of any two colors is represented by a vector addition of each of their paths from the center. Hue is given by the direction of the vector and saturation by its modulus.
It is clear from the comparison that ProPhoto’s gamut is the widest and that’s why it is the gamut used in professional photography. When rescaling to sRGB for instance, like mosts screens are, many colors have to be resampled changing the brightness and hue probably. This effect is called gamut clipping.
.The first color photograph was made in 1861 by James Clark Maxwell (the handsome dude you see to the right). Maxwell studied the human eye to find that our eyes were sensitive only to red, green, and blue light.
Before long, Maxwell had developed a method (now called the Harris Shutter effect) to mimic our eyesight and make color photographs by making three black & white pictures: One with a red filter over his lens, one with a green filter, and one with a blue filter.
When he combined them together, photo magic happened and the color photograph was born!
Let’s play with this!
So now T_Paul at RetouchPRO is proposing a fun challenge: to re-construct a color image from a film roll with 3 different black & white shots that clearly belong to each one of the three RGB channels.
Here’s the process I followed to obtain the following result:
Aligning the three layers was a bit tricky, because as they are shot in turn they’re not exactly the same. Specially the guy in the middle couldn’t hold it and moved significantly. So I first attempted an automatic alignment (in Photoshop Edit>>Auto-Align Layers) cutting each one of them from the strip and placing them as 3 different layers in a new image.
To adjust minor alignment issues your can play around with opacity to visualise a layer and the one right below. At this point it is sufficient just to be sure the logs, that obviously didn’t move, are pretty straight. We’ll deal with the guys later on.
Rough color correction
I later saved each layer as a different image that would become my red, blue and green filters and loaded them as channels on a new RGB multichannel image (remember: in PS Mode>>RGB).
How to know which is which is merely intuitive. The higher the amount of white, the more of that color you will obtain in the final mix. Therefore faces should be pretty dark in the blue filter, and skies darker for the red filter… and so on. According to this theory, you can instantly recognise what’s going to happen. The red filter is so light that there is going to be far too much red component in the composition.
To roughly compensate the filters let’s apply a level correction to each one of them first:
Further color correct
Several adjustment tone and color calibration layers later, the image looks like this:
[one_half]For a more detailed, zone specific color correction, you can treat each channel separately. By following simple color rules you can manage to change a wrong color only altering 33% of the information in the area. This is a simple RGB color wheel where you can see the three primary colors, together with their secondary colors. If you want to remove a red blemish, you’ll need to go darker on the red, but also perhaps it is a good idea to go lighter on the green layer as it is magenta’s complementary color.[/one_half][one_half_last]
In this particular photograph I dealt with yellow spots due to small aberrations, which I solved by simply painting on the blue filter with white on “lighten” multiplicity mode.
In optics, chromatic aberration is a type of distortion in which there is a failure of a lens to focus all colors to the same convergence point. It manifests itself as “fringes” of color along boundaries that separate dark and bright parts of the image. In our example it is due to movement. Not all three filters overlap exactly the same so you can get fringes of yellow, magenta or cian here and there. In this occasion I corrected it locally by manually adapting the alignment of only the channel that is off. See the results:
A mixture of both techniques, color correction by channel and chromatic aberration adjustment, were used in this guy’s face:
As for image restoration, I had to get rid of all artifacts on the image. Most of them would be due to weathering on either one of the filters, so instead of flattening out and working on a composite image, I preferred to go on and heal each one of them per filter.
As seen above:
CYAN imperfections are corrected in the RED filter
YELLOW imperfections are corrected in the BLUE filter, and
MAGENTA imperfections are corrected in the GREEN filter
And finally, general retouching. And by this I mean to flatten the image and apply levels, toning and, like I did in this case, frequency separation technique to increase definition on the main subject and obscure disturbing details.