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Saturation Adjustments

As discussed earlier, image saturation is a measurement of the intensity of its color. Most images contain many different levels of saturation, which you can measure using the vectorscope.

Even though saturation is usually modified whenever you adjust color balance or contrast (so long as you’re using an application that processes these adjustments in RGB space), there are frequently times when you’ll want to adjust saturation all by itself. You may do this to create different looks, correct for broadcast legality, or perform scene-to-scene color correction.

Most color correction applications provide you with several controls for saturation adjustment, depending on whether you want to adjust saturation throughout the entire image or just the saturation within a narrow tonal range.

Analyzing Saturation Using the Waveform Monitor Set to FLAT (FLT)

To help you control saturation at specific tonal ranges, it’s helpful to be able to analyze image saturation more specifically. The vectorscope shows you the overall saturation for the entire image, which is helpful, and shows you how strong the saturation is for specific hues, which is essential.

However, you can also configure most Waveform Monitors to display saturation as an overlay over luma, usually referred to as FLAT (FLT) or something similar. This way, you can see how saturated an image is at different tonal zones. This mode is principally useful for checking to see how much saturation exists in the highlights and shadows of an image. In this mode, the Waveform scope can’t show you information about specific colors (that’s what the vectorscope is for); it shows you only the amplitude of the chroma component corresponding to each level of the luma component.

Let’s take a look at how this works. Figure 4.114 comprises two halves. The bottom half is completely desaturated and shows the luma level that stretches across the entire frame from left (black) to right (white). The top half has saturated color added to this base luma level.

Figure 4.114

Figure 4.114 A split-screen test pattern. The top half is highly saturated, the bottom half has no saturation.

Examining this image in the Waveform Monitor confirms that the luma of the overall image is a simple ramp gradient (Figure 4.115).

Figure 4.115

Figure 4.115 The test pattern’s overall luma is a simple ramp gradient.

However, turning the Waveform Monitor’s saturation option on shows a different picture. The shadow and highlight portions of the top half of the gradient are highly saturated, with excursions in the Waveform graph well below 0 and above 100 percent, which can be seen as the thick parts of the Waveform graph in Figure 4.116. This image makes it easy to examine the many different controls you’re given to adjust saturation levels throughout the picture.

Figure 4.116

Figure 4.116 The thickness of the Waveform graph indicates high saturation from the shadows through the highlights when the Waveform Monitor is set to display FLAT (FLT).

The Saturation Control

Every color correction application and filter has at least one saturation control that simply raises the saturation throughout the image, creating a more vivid look, or lowers it for a muted result. Sometimes this control takes the form of a single slider or parameter, such as in DaVinci Resolve (Figure 4.117).

Figure 4.117

Figure 4.117 The saturation slider in the primary color correction display of DaVinci Resolve.

The saturation controls in FilmLight Baselight let you control overall saturation, but they also allow independent adjustments to the saturation of the red, green, and blue hues within the image (Figure 4.118).

Figure 4.118

Figure 4.118 The saturation controls in the video grading panel of FilmLight Baselight.

However the saturation controls are set up, increasing saturation intensifies the colors of the image, as shown in Figure 4.119.

Figure 4.119

Figure 4.119 A clip with saturation raised throughout the image. Notice how the waveform displaying saturation is thick throughout the graph.

Decreasing saturation mutes the entire image, as shown in Figure 4.120.

Figure 4.120

Figure 4.120 A clip with saturation lowered throughout the image. Notice how the waveform displaying saturation has thinned.

While simple control over saturation is often exactly what you need, there will be situations where you’ll need to exert more selective control over the saturation in an image, boosting it in specific areas while lowering it in others.

Targeted Saturation Controls

Many professional color correction applications also provide specific control over saturation within specific tonal regions of the image. In particular, you’ll frequently be concerned with controlling saturation within the darkest shadows and brightest highlights of your image. Sometimes, these controls are fixed and defined by the same shadows/midtones/highlights tonal ranges used by an application’s five-way and nine-way color controls, because the intention is to make quick saturation adjustments at the extremes of image tonality. When available, these controls make it really fast to perform the following operations:

  • Desaturating shadows to make them seem more natural and to create an image with deeper blacks, creating the illusion of greater contrast
  • Desaturating highlights that have troublesome color casts to make them instantly white (for example, shots with a bit too much red or blue at 0 percent/IRE have off-color blacks that are easily fixed with this control)
  • Boosting saturation within a specific region of midtones in order to avoid saturating the entire image
  • Eliminating unwanted color artifacts in shadows and highlights that result from extreme color corrections made to the rest of the image (for example, making big corrections in shots with snow often adds color to the brightest highlights, and this control is a fast fix)
  • Legalizing image saturation in the shadows and highlights (see Chapter 10 for more information)

Currently, the most common type of saturation control is a single saturation control that can be set to affect different tonal ranges via buttons that change the tonal range it affects, whether the overall image or just the highlights, midtones, or shadows (Figure 4.121).

Figure 4.121

Figure 4.121 Buttons in SpeedGrade and Smoke that change whether the primary controls affect the overall image or a specific tonal zone.

Let’s take a look at how Highlights and Shadows controls work to limit a saturation control’s effect, using them to affect the test pattern in Figure 4.122.

  • Highlights saturation controls affect the brightest parts of the image. These controls often have the most effect where the luma component is above approximately 75 percent, with a gentle falloff toward the midtones.
  • Shadows saturation controls affect the darkest parts of the image. These controls often have the most effect where the luma component is below approximately 25 percent, again with a gentle falloff toward the midtones.

    Figure 4.122 shows the effect of setting both Highlights and Shadows saturation controls to 0, desaturating the brightest and darkest areas of the top strip of the test pattern.

    Figure 4.122

    Figure 4.122 Highlights and Shadows saturation controls desaturating the opposite extremes of the image. This effect can be seen to the left and right edges of the uppermost strip of the test pattern (the middle blue strip shows the original saturation).

The Waveform Monitor to the right, which is set to FLAT (FLT), shows the desaturation that’s occurring in the highlights and shadows through the tapering off of the thick part of the waveform.

Other applications, such as DaVinci Resolve and SGO Mistika, provide Curve controls that affect luma versus saturation, providing nearly unlimited fine-tuning of saturation throughout an image (Figure 4.123).

Figure 4.123

Figure 4.123 The advantage of Curve controls is that they enable you to quickly make custom adjustments over user-selectable ranges of image tonality. However, Highlight/Midtone/Shadow-style saturation controls can be easier to adjust for common operations owing to how they’re mapped to your control surface, making fixing a shadow as easy as punching a button and twisting a knob.

Enriching Saturation Without Cheapening an Image

If you’re trying to create a super-saturated look, you don’t want to just crank up the Saturation parameter and leave it at that. You’ll get lots of color, but you risk losing detail due to color bleed, reduced color contrast, artifacts, aliasing in video formats with low chroma sampling, edge ringing, and, of course, broadcast illegality.

Saturation works hand-in-hand with contrast in shaping the look of your clips. Controlling saturation in the shadows and highlights of your images is the key to creating a sophisticated look when increasing the saturation of your images, not to mention maintaining broadcast legality with more stringent broadcasters.

You’ll also find that excessive saturation is a bit more successful in darker images, where the distribution of midtones is weighted more toward the lower end of the digital scale, from about 10 to 60 percent. When the difference is described in terms used by the HSB color model, colors with a lower brightness value appear richer than those with a higher brightness value, which can easily appear artificial and excessive when in abundance.

When you increase the saturation of images, it’s even more important than usual to make sure that neutral areas of the image don’t have an incorrect color cast. If you’re deliberately warming or cooling the image, you may need to ease off the correction.

In the following example, you’ll safely boost the color intensity in Figure 4.124 for an even more richly saturated look.

Figure 4.124

Figure 4.124 The original, unaltered image.

  1. Examine the image using a vectorscope. You can see there’s already plenty of saturation in the picture, which extends well out from the center of the center crosshairs (Figure 4.125).

    Figure 4.125

    Figure 4.125 Vectorscope analysis of the original saturation level.

  2. If you turn up the saturation, the image certainly becomes more colorful, but the color is added indiscriminately throughout the image, even in the deepest shadows. The result is a somewhat gaudy, overcolorful look, which is not what you want (Figure 4.126).

    Figure 4.126

    Figure 4.126 After increasing image saturation, the FLAT (FLT) analysis in the Waveform Monitor has grown thicker.

  3. Examine the bottom of the Waveform Monitor with FLAT (FLT) turned on. You can also see that the thickness of the waveform that indicates increased saturation is extending down below 0 percent/IRE (Figure 4.127). This can also be apparent in gamut scopes that show a composite transformation of the image data with special markers for the upper and lower limits of acceptable saturation, as discussed in Chapter 10.

    Figure 4.127

    Figure 4.127 In the top image, the Waveform Monitor is set to luma. At the bottom, the Waveform Monitor is set to FLAT (FLT). Notice how the fuzzy part of the right waveform stretches below the bottom line corresponding to 0 percent/IRE.

    Excessive saturation in the shadows is what makes the last adjustment look unflattering. You don’t expect to see increased saturation in shadows; you expect saturation to diminish with the light level.

  4. To correct this, you can use whichever controls your application provides to reduce shadow saturation, either lowering the shadows using the Shadows controls, or using a luma versus saturation curve to roll off the saturation in the darkest part of the picture (Figure 4.128).

    Figure 4.128

    Figure 4.128 Adding two control points to a luma versus saturation curve to reduce image saturation in the deepest shadows, with a smooth transition to the more highly saturated midtones.

    You don’t want to desaturate the shadows all the way to 0, or the image might pick up a gritty, unpleasant look (unless, of course, that’s what you’re going for). You can see the result in Figure 4.129.

    Figure 4.129

    Figure 4.129 The high-saturation version of the image with both Highlights and Shadows saturation controls reduced to 20 percent.

    A close-up of the bald man’s head and the plant’s shadow on the wall makes it easier to see the difference (Figure 4.130).

    Figure 4.130

    Figure 4.130 Close-up comparison of shadow saturation. At top, increased shadow saturation looks unnatural. At bottom, reduced shadow saturation looks closer to what we’ve come to expect from professionally shot programs.

When boosting saturation, it’s easy to play with fire. Reducing shadow saturation in the face of increased midtone saturation is a good way to keep the perceived contrast of your images higher and to keep the shadows of your image looking deep and clean. Alternately, you could use your controls to instead be more selective about how you boost saturation, raising it only in the midtones while leaving the highlights and shadows alone.

By keeping an eye on the saturation in the highlights and shadows of your images, you can more easily create more vivid looks without making your images look like a bad TV signal.

Controlling “Colorfulness”

In Edward Giorgianni and Thomas Madden’s Digital Color Management: Encoding Solutions (Wiley, 2009), colorfulness is defined as the “attribute of a visual sensation according to which an area appears to exhibit more or less of its hue.” This is the definition I’m using for purposes of discussing the concept that a subject may appear more or less colorful despite its actual level of saturation.

This is an important concept, because it describes various perceptual foibles by which clients and audience members may perceive what’s onscreen differently from what the video scopes show. In short, you may have highly saturated images that don’t appear to be very colorful, and you may have relatively desaturated images that appear to be more colorful than you would think based on their saturation.

So, if saturation isn’t the absolute determinant of colorfulness, then what other qualities affect one’s perception of how much color is in an image?

Brightness and Colorfulness During Acquisition

In human vision and image recording, the more brightly illuminated a subject is, the more colorful it is perceived to be, even though the color of the subject is identical no matter what the lighting conditions. This is known as the Hunt effect: Reduced illumination results in reduced colorfulness (Figure 4.131).

Figure 4.131

Figure 4.131 The same set of colorful objects with both dim and bright practical lighting. While the dim version certainly has rich color, the brighter items such as the flowers seem more colorful still.

For the colorist, the Hunt effect relates directly to the perceived colorfulness of displays set to different peak white settings; given the same display surround, a higher peak white output results in a more colorful-looking image, whereas a lower peak white output appears less colorful. This is one of many reasons to maintain careful control over the calibration of your display.

Contrast and Colorfulness While Grading

Interestingly, the relationship between saturation and image contrast when adjusted by master RGB controls as described in Chapter 3 is analogous to the Hunt effect. Expanding contrast increases saturation, which is usually a desirable result (Figure 4.132).

Figure 4.132

Figure 4.132 Our test image before and after contrast expansion.

As has been discussed previously, things get more complicated when you exercise independent control over contrast of the luma component, separate from the chroma of the signal. In this case, given the mathematics of digital image processing, stretching the contrast of images to make them brighter offers less perceived colorfulness than would a darker image (Figure 4.133).

Figure 4.133

Figure 4.133 Stretching contrast of just the Y’ component diminishes colorfulness.

In both examples, image saturation is clearly intensified, but the quality of the graded images is quite different. This is the reason why clients often substitute the words bright and saturated with one another, because often the quality they’re trying to describe is not so easy to isolate.

Size and Colorfulness

The size of a feature has a direct relationship to its perceived colorfulness. In an example cited by Mahdi Nezamabadi (“The Effect of Image Size on the Color Appearance of Image Reproductions,” Ph.D. dissertation, Rochester Institute of Technology, 2008), viewer observations of small paint patches were compared to their observations of the finally painted four walls of a room. Even though the color of the patch and the walls were demonstrably identical through spectroradiometric measurement, viewers reported an increase in lightness and chroma in the painted walls.

In other words, larger objects that have the same color as smaller objects are perceived as being more colorful. This is shown in the following two images, where the same vase, red box, gold frame stained glass lamp, and bottom of the vase appear in both images. However, when filling more of the frame in the image at the right, the objects being zoomed in on will appear to most observers to be more colorful, even though there’s no actual change in saturation (Figure 4.134).

Figure 4.134

Figure 4.134 When pushing into a colorful object, the increased size can give an impression of greater colorfulness, even though saturation is the same.

This is a valuable phenomenon to be aware of when dealing with situations where you’re trying to match one shot to another and you’re wondering why the client keeps saying that one seems to be brighter or more colorful than the other, even though your video scopes show that the colors match exactly. In such a situation, you have a choice. You can “fudge it” by slightly lowering the saturation of the red box where larger or boosting the saturation of the red box where smaller in order to make the cut between the two less jarring. Or, you can explain the phenomenon to the client and point out how this can have a useful impact on the viewer for the right program, giving close-ups and push-ins more pop for the right cut.

This correlation between size and colorfulness also applies to the overall size of the image, which relates directly to how big of a display you’re working with. In Figure 4.135, the same image is shown larger and smaller. Again, to most observers, the larger image appears to be more colorful, even though they’re identical.

Figure 4.135

Figure 4.135 When viewing an image on a larger display, it’s easy to get an impression of greater colorfulness than the same image on a smaller display, even though both images have identical saturation.

Now, one of the reasons for the careful monitoring practices described in Chapter 2 is to attempt to compensate for this effect via careful surround lighting and appropriate seating distance. For projection, this is also compensated for by the fact that images are projected with less illumination than from self-illuminated displays such as LCD, OLED, and Plasma.

However, it’s inarguable that grading on a 20’ projection screen is a far different experience than grading on even a 40” display which, perceptually speaking, is significantly different from grading on a 15” display. Having graded on projection screens and video displays both large and small, I can comfortably say that I make different decisions about certain adjustments based on the overall size of the image, which is a good bias to be aware of.

However, another approach to this issue is to grade while keeping in mind the size of the display viewers will watch your program on. Ideally, matching your grading suite’s primary display, whether a monitor or projector, to the audience’s experience (living room or theatrical) will enable the best decision making on your part as you make color adjustments.

Color Contrast and Colorfulness

Finally, another aspect of the image that affects perceived colorfulness is color contrast, or how much of a difference there is between individual hues within an image. Described in much more detail in the following section, contrast of hue is one aspect of color contrast that makes a big difference between the perceived colorfulness of an image as your client sees it and the level of measured saturation.

Despite whatever measurable similarity in saturation there is, any client will tell you that an image with a greater range of hues in it will appear to be more colorful than one with fewer. On that note, let’s take a closer look at color contrast, the different ways it can be expressed, and how you can control it.

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