10.4 Visual Qualities

Before you begin applying specific postprocessing techniques to improve an image, itll be useful for you to be able to quickly assess the image and correctly diagnose its defects.  Simply observing that it doesn’t look quite right doesn’t provide much guidance in your search for the appropriate corrective measures.  In this section we’ll briefly outline the main dimensions along which an image can vary: saturation, contrast, sharpness, brightness, and color balance.  Once you have a firm grasp of these concepts and begin to accumulate some experience in assessing where each image lies along these various dimensions, you’ll start to get more efficient at figuring out what postprocessing effects need to be applied to each image to improve its overall aesthetics. 

10.4.1 Saturation

The term saturation refers to the overall color richness of an image.  There are a number of different technical definitions of saturation, but they’re not important here.  What is important is that saturation is a highly subective quality that can nevertheless strongly impact whether and how much the average viewer enjoys your photos.  Indeed, assessing—and therefore adjusting—saturation is one of the most difficult tasks in digital nature photography.  Most beginners tend to oversaturate.  After some time they realize that they’ve been oversaturating their images and will sometimes overcompensate by undersaturating everything.  Meanwhile, other people viewing their images via the internet will be seeing the same images but with slightly different saturation levels, due to differences in the rendering hardware (and software) between their own computers and that used by the photographer.  Then there is the overall cultural drift in terms of what is currently considered stylish (with regards to color richness), versus
retro or simply outdated.  It is, in a word, frustrating.
    Let’s consider an example.  In the figure below, the middle image (labeled
+0) is the original, which hasn’t been subjected to any saturation adjustments.  The bottom row in the figure shows the image after increasing the saturation by various amounts (+25, +50, and +100) in Photoshop.  Increasing the saturation obviously brings out more of the latent colors in the photo, resulting in a more striking image.  Viewers’ eyes are often drawn to the most intensely colorful images on a page, though this doesn’t mean that they’ll necessarily find that image more aesthetically pleasing than less saturated versions of the same photo.  In this example the +100 image is obviously useless for aesthetic purposes.  At this resolution the +50 image may appear over-saturated as well, depending on tastes (I, for example, think it looks hideous).  For the +25 image the verdict is less clear.  Moving to the top row of the figure, the -100 cell is again clearly useless as a color photograph (all of the color has been removed, resulting in a grayscale image), and the -50 image clearly seems flat.  The -25 is closer to the original; the beak is a bit less yellow and the background is also slightly less green.

Fig. 10.4.1: Saturation.  Adjusting the saturation slider in Photoshop’s Hue/
Saturation tool results in changes in color richness.  The original image is shown
in the middle (+0 saturation).  Decreasing the saturation (top row) moves the image
progressively toward grayscale.  Increasing saturation (bottom row) improves color
richness but makes the image look increasingly artificial and unpleasant.

    It’s important to note that this example is limited in its illustrative power by the low resolution of the images shown here.  At normal resolutions, saturation adjustments of -25 or +25 would typically be far too extreme (though there are exceptions to every rule).  Personally, I rarely increase saturation at all (directly, via the Saturation slider in Photoshop), and when I do it’s never by more than +5.  The problem with increasing saturation by more than this amount is that experienced viewers of digital art quickly notice the unnaturally elevated saturation and conclude that the photo has been
    Ideal saturation levels are highly subjective.  Keep in mind also that different computer monitors render images with different amounts of saturation, so what looks good on your screen might not look as good on your neighbor’s.  This is why I recommend leaving the saturation neutral—i.e., as it looks straight out-of-the-camera—when processing images for display on the internet.  For for making photographic prints (section 14.1), especially canvases, I instead recommend trying different saturation levels on smaller trial prints to find the best setting for your particular printing device.

Fig. 10.4.2: Over-saturation can cause clipping in some channels and not others.
In this example, the red of the bird’s facial shield and beak has been over-
saturated, resulting in an obvious lack of detail.  The red component of the
histogram reveals the clipping that has occurred at the right.

    Increasing the saturation of an image artificially also poses the risk of clipping individual color channels.  As described previously (sections 6.2, 10.2), clipping the image histogram—at either end—is generally bad because it can result in loss of detail as fine differences in pixel intensity are obliterated by mapping those pixels to the same hue.  The same thing can occur in individual color channels (i.e., red, green, blue), resulting in loss of detail in regions of an image having nearly a solid color.  In the gallinule image above, notice that the red portion of the beak and facial shield is relatively devoid of fine-scale details.  In the accompanying panel of histograms, notice that the red channel shows clipping at the right end—i.e., an interrupted peak at the edge of the graph—likely accounting for the lack of detail in the intense red of the bird’s beak.  This was a direct result of globally increasing the saturation of the entire image: because global changes affect all colors equally, any color already over-represented in the image (even locally) can be pushed to the point of clipping and therefore loss of detail.
    Note that the Hue/Saturation tool isn’t the only way to alter the saturation of an image in Photoshop.  I’ve found that the Levels and Curves tools also increase apparent saturation in some circumstances, and I sometimes end up needing to decrease saturation a bit using the Hue/Saturation tool after using Levels or Curves.  Also be aware that in Photoshop’s RAW conversion window (called Adobe Camera Raw, or ACR), which opens automatically when you open a RAW file in Photoshop, there is a slider called Vibrance which allows more subtle adjustments to saturation than does the actual Saturation slider.  Whereas the Saturation slider increases the intensity of all colors in the image equally, Vibrance preferentially boosts those pixels that are least saturated, and can be used to rectify undersaturation problems without as much risk of clipping.  As we’ll see in section 10.6, another option is to selectively increase saturation in just those regions of an image that obviously need it, using Photoshop’s selection tools.

10.4.2 Color Balance

One concept you’ll encounter a lot more in other books on photography than in this one is color balance, or white balance.  These terms refer to the color cast that results from a light source having a non-neutral color temperature (section 7.1).  For indoor photography, artificial white-balance correction is typically essential, as artificial lights often produce a color bias that looks unpleasant in digital stills.  For outdoor nature photography, color temperature is still a relevant issue, since natural light can indeed assume a wide range of temperatures.  However, to the extent that natural lighting effects are deemed integral to the mood of the scene being captured,
correction of white balance in these cases will generally be less desirable. 

Fig. 10.4.3: The effect of color temperature.
Middle image: original photo.  Top: after
adjusting the white balance to make the
image appear
warmer.  Bottom: after
adjusting the white balance to make the
image appear

    Personally, I almost never adjust color balance in my images.  I simply shoot in RAW with auto-white balance (AWB), and leave the default settings in place during RAW conversion.  If an image has a reddish cast because it was taken with the setting sun behind me, I rely on my viewers to correctly interpret that cast as indicating the time of day at which the scene was captured.  My attempts in the past to correct color casts have generally been unsuccessful, so when I encounter an image during filtering (chapter 12) that has a noticeable, unpleasant cast, I generally skip it and move on to another image.
    Note that the perception of color cast can be significantly affected by viewing conditions—i.e., the monitor used to view the images, and even the light bulbs used in the room where you do your image processing.  Many home lamps produce light with a yellow bias (
3000-4000°K), either due to the light bulb alone, or to the bulb in combination with a lamp shade.  By imposing a color cast on everything else in the room, home lighting can strongly affect your perception of the color balance in images rendered on an electronic display, sometimes resulting in perception of a color cast opposite that imposed by the lighting (i.e., seemingly bluish).  You can now buy light bulbs with a more neutral color temperature (~5500°K), though the effect of any lamp shade and even wall paint may confound any neutrality in the bulb’s output.  Monitor calibration is another important consideration in this context (section 14.1.2).

10.4.3 Contrast

After saturation, the next most striking aspect of an image—in terms of sheer ability to attract attention—may well be its contrast.  As with images that have been over-saturated in postprocess, photos with excessive contrast need not be aesthetically pleasing to attract attention.  The trick is to find a tasteful degree of contrast that will both attract the eye and avoid the appearance of artificial manipulation.
    Contrast is a very general term.  In Photoshop and similar programs, adjustments to contrast generally result in increasing the differences between the extremes—e.g., whiter whites and blacker blacks, as well as greener greens and redder reds, etc.  In the figure below, we show the original image in the middle (labeled
+0) along with an image artificially depleted for contrast (-50) and one artificially enriched in contrast (+100). 

Fig. 10.4.4: Contrast.  Increasing the contrast results in brightening
the brighter areas and darkening the darker areas.  Increasing
contrast via the Brightness/Contrast window in Photoshop often
produces unpleasant results.  Other methods for increasing
contrast include the Levels and Curves tools.  Decreasing
contrast via Brightness/Contrast can sometimes be useful.

Notice that contrast to some degree subsumes saturation.  In the figure above, the saturation of the green background and the yellow in the bird’s beak follows differences in the contrast.  The most striking aspects of contrast manipulation, however, tend to be seen in the white and black features in an image.  Artificially increasing the contrast tends to quickly clip the histogram at both ends (or at least to conflate pixel values near the extremes), resulting in lack of detail in both bright and dark areas of an image.  Decreasing the contrast, on the other hand, tends to have more of a benign effect, and can sometimes result in more natural-looking images.
    Contrast is very often confused with sharpness (section 10.4.5), and for good reason, since sharpness is often defined in terms of fine-scale contrast of minute features in a subject.  In terms of postprocessing of bird images in Photoshop, I rarely ever use the Contrast slider in Photoshop (via the Brightness/Contrast window).  Instead, I prefer to directly manipulate the variables influencing perceived contrast, via the Levels tool and (more rarely) the Curves tool.  These tools adjust the global contrast of the photo.  More local changes to contrast can be achieved via direct selection of regions to manipulate (section 10.6).  At the most local scale, artificial sharpening increases micro-contrast (section 4.3) on individual foreground features such as feather barbs and other anatomical details.

10.4.4 Brightness

Brightness is probably the simplest and most easily quantifiable image quality, though there are some subtleties worth briefly exploring here.  You’ll recall from section 10.2 that in an 8-bit image (such as a JPEG), each pixel component varies in numerical value from 0 to 255, with 0 denoting pure black and 255 pure white.  Brightness thus refers to the average pixel intensity, in terms of numerical pixel values, with higher average pixel values resulting in an overall brighter image. 
    As illustrated by the figure below, even subtle modifications to overall brightness can result in noticeable differences in apparent detail, contrast, and even saturation.

Fig. 10.4.5: Increasing or decreasing brightness can have secondary
effects, since brightness affects contrast and saturation.  Middle: the
original image.  Top: after decreasing brightness artificially in
Photoshop.  Bottom: after increasing brightness.  Notice that
changes to brightness affect the histogram by shifting its mass
left or right, and can also fragment portions of it.

In the top image of the figure above, the brightness has been decreased by a value of -10 in Photoshop.  Looking at the snow beneath the bird and the white throat feathers, this seems (on my monitor, at least) to have increased the apparent level of detail in those regions of the image.  The increased detail is due to an increase in contrast resulting from the stretching of the histogram between the main peak and the rightmost end.  As you can see, this also resulted in some fragmentation of the right tail of the distribution, which in extreme cases can result in posterization (section 10.2).  In the bottom image we’ve instead increased brightness, and this has resulted in less contrast and detail in the snow, while also slightly fragmenting the left tail of the distribution. 
    Now let’s compare the bird’s head patch between the top and bottom images.  Notice that increasing the brightness has also changed the colors, resulting in less red and more yellow.  This resulted from the fact that increasing the brightness shifts the histogram to the right; for an already bright image, this causes the individual color channels to be crowded together at the right end of the histogram, where they’re forced to overlap more, resulting in more color blending and therefore less prominence of individual pure colors in bright areas.  This is analogous to what happens when you experience feather glare in bright shooting conditions: too much light striking the subject overwhelms the feathers’ ability to selectively absorb some parts of the visible spectrum, resulting in pure white (full-spectrum) light being reflected.  Just remember that brightness, contrast, and saturation are not entirely independent, and that all of them derive from relative pixel intensities.
    As with other visual qualities, the apparent brightness of an image can be affected by viewing conditions, including the inherent brightness of your monitor (computer screen) as well as the lighting in your home.  I prefer to do my postprocessing in a room with fairly dim lighting; overly bright lighting tends to make me increase the brightness and contrast of my images to compete with the ambient light in the room, whereas working in a pitch black room with no lights causes the brightness from my screen to overwhelm my eyes and tire them faster.

10.4.5 Sharpness

Sharpness—the perception that a photo contains a high degree of detail—is simultaneously one of the most critical aspects of bird photos and one of the most difficult to objectively assess.  As with saturation, many beginners tend to over-sharpen their images, and ideal sharpness levels can certainly be somewhat subjective.  Let’s again forego any technical definitions
and instead consider a few examples.

Fig. 10.4.6: Sharpness.  From the original, unsharpened image (labeled 0%),
through 200% increase in sharpness at a 0.15 radius and 0 threshold.  Any
camera with an antialiasing filter over its sensor requires at least some amount
of sharpening in postprocess.  Finding the perfect amount of sharpening for each
photo is one of the most difficult tasks in postprocessing.

    The four images shown above were subjected to different amounts of artificial sharpening in Photoshop.  To some (perhaps many) eyes, the 0% version will appear less than ideal, while different viewers will likely choose differently from among the other three images in identifying what looks best to them.  Certainly, the 200% version gives the impression of the greatest amount of fine-scaled detail, and would probably be called by most the sharpest.  Whether most people would find this one the most pleasing among the four images less obvious.  Personally, I find the 100% and 200% versions to be reasonably acceptable to my eyes, while the image in the figure below (Fig 10.4.7) is, in my opinion, a prime example of blatant over-sharpening.  The image certainly contains a large amount of detail, but those details have been so exaggerated that the image looks plainly artificial.

Fig. 10.4.7: A case of blatant oversharpening.
Though this image reveals many fine details
in the subject, there are numerous artifacts
introduced as a result of overly aggressive
use of artificial sharpening in software.

    The problem with this image isn’t the amount of detail that it shows: there’s nothing wrong with an image that really is extremely sharp (e.g., due to the use of top-quality photography gear and impeccable technique).  Rather, over-sharpening refers to the use of artificial sharpening to increase sharpness beyond what the captured image can support.  All digital photos need to be artificially sharpened in postprocess to alleviate the blurring effect of the antialiasing filter that covers the imaging sensor in (most) DSLR cameras.  You can think of artificial sharpening as a process of reclaiming sharpness lost due to the antialiasing filter.  Over-sharpening occurs when that artificial sharpening process attempts to increase sharpness beyond what the image can support.  Over-sharpening creates the illusion of image detail which isn’t actually present in the image; unfortunately, that illusion doesn’t fool everyone.  For savvy internet users, who’ve seen many thousands of digital images of widely varying quality, over-sharpened images stand out like a sore thumb.  The case of the image above, one indicator of over-sharpening is the presence of isolated bright pixels (look in the gray feathers on the bird’s shoulder and breast); these result from the sharpening algorithm’s attempt to increase fine-scale contrast.  If you enlarge an over-sharpened image to a high zoom level (1000% or so), you
ll sometime see halosrings of bright pixels around dark featuresthat are introduced by the sharpening algorithm.  These types of artifacts are a sure indication of over-sharpening, though even without any of these obvious signs an image can still be considered over-sharpened if it offends your (or someone else's) sense of what level of sharpening looks good.
    Keep in mind that ideal sharpness is medium-dependent.  Just as with saturation, the only way to know how a printed image will look with a certain level of artificial sharpening is to print it and see.  Variability between computer monitors is less of an issue with sharpening than it is for saturation, though some monitors are certainly capable of rendering images with greater contrast than others, and those with a large color gamut, or a different pixel pitch, may indeed reveal more details to viewers than others, thereby affecting the perception of sharpness.