Making fine prints in your digital darkroom
Light and color: an introduction
by Norman Koren

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Table of contents

for the Making Fine
Prints series

Getting started | Scanners | Pixels, images, & files
Light & color
Light & color | Color models
Digital cameras | Printers | Papers and inks
Monitor calibration and gamma
Printer calibration | Scanning | Basic image editing
Black & White | Matting and framing
Tonal quality and dynamic range in digital cameras

Color Management: Introduction | Implementation
Profiles with MonacoEZcolor | Evaluating profiles

for Image editing with
Picture Window Pro
Introduction | Making masks
Contrast masking
Tinting and hand coloring B&W images
Example: Sunset, Providence, Rhode Island

This page introduces the basic concepts of light and color. Color theory is dealt with in more depth in the series on Color management.

Light and Color

We begin with a review of light and color. The concepts presented here-- additive and subtractive color and their respective primaries-- are critically important for image editing. You may skip to the next section if you are familiar with them.

The human eye is sensitive to electromagnetic radiation with wavelengths between about 380 and 700 nanometers. This radiation is known as light. The visible spectrum is illustrated on the right. The eye has three classes of color-sensitive light receptors called cones, which respond roughly to red, blue and green light (around 650, 530 and 460 nm, respectively). A range of colors can be reproduced by one of two complimentary approaches:

Unfortunately, ideal C, Y and M inks don't exist; the subtractive primaries don't entirely remove their compliments (R, B and G). This isn't a problem for film, where light is transmitted through three separate dye layers, but it has important consequences for prints made with ink on reflective media (i.e., paper). Combining C, Y and M usually produces a muddy brown. Black ink (K) must added to the mix to obtain deep black tones. CMYK color is highly device dependent-- there are many algorithms for converting RGB to CMYK. Photographic editing should be done in RGB (or related) color spaces. Conversion to CMYK (usually with colors added to extend the printer color gamut) should be left to the printer driver software.
Additive color Subtractive color
Light sources: beams of light or dots of light on monitor screens Objects that transmit or reflect light: film or prints. Typically illuminated by white light.
Primaries: Red (R), Green (G), Blue (B) Primaries: Cyan (C), Magenta (M), Yellow (Y)
Light from independent sources is added. Portions of the visible light spectrum are absorbed by inks, which contain dyes or pigments, or by dye layers in photographic film or paper.
Adding red and green makes yellow (R + G = Y); Similarly, G + B = C and R + B = M. Adding all three additive primaries in roughly equal amounts creates gray or white light. Each subtractive primary removes one of the additive primary colors from the reflected or transmitted image. Cyan (C) removes red; hence it is known as minus red (-R). Similarly, M is -G and Y is -B. Objects are typically illuminated by white light. Combining two subtractive primaries makes an additive primary (see illustration). Combining all three subtractive primaries in roughly equal amounts creates gray or black.

You can obtain a wide range of colors, but not all the colors the eye can see, by combining RGB light. The gamut of colors a device can reproduce depends on the spectrum of the primaries, which can be far from ideal. To complicate matters, the eye's response doesn't correspond exactly to R, G and B, as commonly defined (the description above is oversimplified). Device color gamut and the eye's response are discussed in detail in the page on Color Management.

Color models

If you lighten or darken color images you need to understand how color is represented. Unfortunately there are several models for representing color. The first two should be familiar; the latter two may be new. It is not practical to use RGB or CMY(K) to adjust brightness or color saturation because each of the three color channels would have to be changed, and changing them by the same amount to adjust brightness would usually shift the color (hue). HSV and HSL are practical for editing because the software only needs to change V, L, or S. Image editing software typically transforms RGB data into one of these representations, performs the adjustment, then transforms the data back to RGB. You need to know which color model is used because the effects on saturation are very different.
HSV color is shown here in an illustration from Jonathan Sachs' tutorial, "The Basics of Digital Images" (right click on the link to save it in Adobe PDF format). V = max(R,G,B). Maximum Value (V = 1 or 100%) corresponds to pure white (R=G=B=1) and to any fully saturated color (at least one RGB value at 1 and one at 0; no gray component (W = min(R,G,B)). V = 0 is pure black, regardless of H and S. The HSV color model can be depicted as a cone, widest at the top (V = 1), coming to a point at the bottom (V = 0; pure black). (I use the "V"-like appearance of the cone as a mnemonic to remember "HSV." The names of the color models are pretty arbitrary.) efg has a technically detailed explanation of the HSV color model, complete with a Java applet.
HSL color. Maximum color saturation takes place at L = 0.5 (50%). L = 0 is pure black and L = 1 (100%) is pure white, regardless of H or S. The HSL color model can be depicted as a double cone, widest at the middle (L = 0.5), coming to points at the top (L = 1; pure white) and bottom (L = 0; pure black).

Now the important part. What you must remember about the HSV and HSL color models is,

  Darkening in HSV reduces saturation.   Darkening in HSL increases saturation when L > 0.5.  
  Lightening in HSV increases saturation.   Lightening in HSL reduces saturation when L > 0.5.  
Best representation of saturation
Best representation of lightness

HSV and HSL were developed to represent colors in systems with limited dynamic range (pixel levels 0-255 for 24-bit color). The limitation forces a compromise. HSV represents saturation much better than brightness: V = 1 can be a pure primary color or pure white; hence "Value" is a poor representaton of brightness. HSL represents brightness much better than saturation: L = 1 is always pure white, but when L > 0.5, colors with S = 1 contain white, hence aren't completely saturated. In both models, hue H is unchanged when L, V, or S are adjusted.

HSV and HSL are illustrated above for red (H=0). S varies from 0 to 1 along the horizontal axis; V and L vary from 0 to 1 along the vertical axis. The right side of the HSV illustration (S=1) always has maximum saturation (G=B=0) but the top (V=1) varies from pure white at S=0 to pure red at S=1. The top of the HSL illustration (L=1) is pure white for all values of S. It would be nice to be able to represent brightness and saturation properly in one system, but you can't have it both ways.

Transformations for adjusting brightness and saturation in Picture Window Pro let you choose between HSV and HSL. HSV is the default. I usually prefer HSL when the primary intent of the transformation is to adjust brightness; I sometimes prefer HSV when the primary intent is to adjust saturation (I work with brightness more often). But I'm not rigid about these preferences; I often try out both to see how they appear in the Preview window. I don't recommend HSL when you want to darken white or nearly white tones. They can take on an unnatural color cast.

 SHSV = 1
Relationship between RGB, HSV, and HSL color representation for math geeks
H is the same for HSV and HSL. We won't give the equations here; you can find them in efg's HSV lab report. Expressed in degrees (0-360) for any nonzero x, H = 0 for Red (x,0,0); 60 for Yellow (x,x,0), 120 for Green (0,x,0) (illustrated below), 180 for Cyan (0,x,x), 240 for Blue (0,0,x), and 300 for Magenta (x,0,x). H can also be represented on a scale of 0 to 1.

Assume R, G, and B can have values between 0 and 1. Let W = min(R,G,B) = the gray component.

V = max(R,G,B)
L = (V+W)/2
SHSV = (V-W) / V
SHSL = (V-W) / (V+W) = (V-W) / (2L) ;      L <= 0.5
SHSL = (V-W) / (2-V-W) = (V-W) / (2-2L) ;  L > 0.5
Any color with R, G, or B = 1 has V = 1.
Maximum saturation occurs when W = 0.
A bright, fully saturated color (max(R,G,B) = V = 1; min(R,G,B) = W = 0; SHSV = SHSL = 1) must have L = 0.5. L = 1 corresponds to pure white.
V, L, and S illustrated for H = 0.333 (120; Green)

HSV: Best representation of saturation

HSL: Best representation of lightness
V, L, and H illustrated for S = 1 (maximum saturation)
The Y, C, and M bands are much narrower than the R, G, and B bands. I'm not sure why; I suspect it results from specific properties of HSL and HSV (where V = max(R,G,B) rather than, say, mean(R,G,B).). Y, C, and M appear brighter than R, G, and B at similar V and L levels because both V and L are related to max(R,G,B).
Some interesting relationships
(1) For W = min(R,G,B) = 0 (no gray; maximum saturation),  SHSL = SHSV = 1 ;  L = V / 2 ;  L <= 0.5.
(2) For V = max(R,G,B) = 1 and SHSL = 1,  L = 1-(SHSV / 2) ;  SHSV = (1-L) / 2;  L >= 0.5
These relationships imply that (1) the bottom half of the HSL L-H plot for S=1 (above, right) is identical to the HSV V-H plot for S=1 (above, left), and (2) the top half of the HSL S-H plot is identical to an HSV S-H plot for V=1 (not shown), i.e., the HSL L-H plot (above, right) combines two HSV plots.

All saturation equations have V-W in the numerator; they differ in denominator scaling. In both representations, S is a measure of relative saturation. S is 0 when W = V (R = G = B; neutral gray); S = 1 when W is at its minimum allowable value for a given value of V or L. For HSV, W = 0 when S = 1.

For HSL with L <= 0.5, maximum saturation takes place when W = 0; SHSL = (V-W) / (V+W) = 1. When L > 0.5, W must be greater than 0. Maximum saturation takes place when W = 2L-V takes its minimum value, Wmin. In this case  V = 1, so Wmin = 2L-1. SHSL = (V-W) / (2-V-W) = (1-2L+1) / (2-1-2L+1) = 1.

V and L don't correspond with perceived luminance; for example, blue and gray or white with the same V or L values would have very different luminance. The PAL luminance signal, Y = 0.30R + 0.59G + 0.11B, corresponds more closely to perceived luminance.

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Images and text copyright (C) 2000-2013 by Norman Koren. Norman Koren lives in Boulder, Colorado, where he worked in developing magnetic recording technology for high capacity data storage systems until 2001. Since 2003 most of his time has been devoted to the development of Imatest. He has been involved with photography since 1964.