Macworld Guides cameraraw

How Camera Raw Works What Lies Under the Hood Despite the title of this chapter, I promise to keep it equation-free and relatively non-technical. Camera Raw offers functionality that at a casual glance may seem to replicate that of Photoshop. But some operations are much better carried out in Camera Raw, while with others, the choice between making the edits in Camera Raw and in Photoshop may be as much about workflow and convenience as it is about quality. To understand which ones are which, it helps to know a little about how Camera Raw performs its magic. If you're the type who would rather learn by doing, feel free to skip ahead to the next chapter, where you'll be introduced to the nitty-gritty of actually using all the controls in Camera Raw; but if you take the time to digest the contents of this chapter, you'll have a much better idea of what the controls actually do, and hence a better understanding of how and when to use them. To use Camera Raw effectively, you must first realize that computers and software applications like Photoshop and Camera Raw don’t know anything about tone, color, truth, beauty, or art. They're really just glorified and incredibly ingenious adding machines that juggle ones and zeroes to order. I won't go into the intricacies of binary math except to note that there are 10 kinds of people in this world, those who understand binary math and those who don't! You don't need to learn to count in binary or hexadecimal, but you do need to understand some basic stuff about how numbers can represent tone and color. 15 16 Real World Camera Raw with Adobe Photoshop CS Digital Image Anatomy Digital images are made up of numbers. The fundamental particle of a digital image is the pixel—the number of pixels you capture determines the image's size and aspect ratio. It’s tempting to use the term resolution, but doing so often confuses matters more than it clarifies them. Why? Pixels and Resolution Strictly speaking, a digital image in its pure Platonic form doesn’t have resolution—it simply has pixel dimensions. It only attains the attribute of resolution when we realize it in some physical form—displaying it on a monitor, or making a print. But resolution isn’t a fixed attribute. If we take as an example a typical six-megapixel image, it has the invari- ant property of pixel dimensions, specifically, 3,072 pixels on the long side of the image, 2,048 pixels on the short one. But we can display and print those pixels at many different sizes. Normally, we want to keep the pixels small enough that they don’t become visually obvious, so the pixel dimen- sions essentially dictate how large a print we can make from the image. As we make larger and larger prints, the pixels become more and more visually obvious until we reach a size at which it just isn’t rewarding to print. Just as it’s possible to make a 40-by-60 inch print from a 35mm color neg, it’s possible to make a 40-by-60 inch print from a six-megapixel image, but neither of them is likely to look very good. With the 35mm film, you end up with grain the size of golf balls, and with the digital capture, each pixel winds up being just under 1/50" of an inch square—big enough to be obvious. Different printing processes have different resolution requirements, but in general, you need not less than 100 pixels per inch, and rarely more than 360 pixels per inch to make a decent print. So the effective size range of our six-megapixel capture is roughly from 20 by 30 inches downward, and 20 by 30 is really pushing the limits. The basic lesson is that you can print the same collection of pixels at many different sizes, and as you do so, the resolution—the number of pixels per inch—changes, but the number of pixels does not. At 100 pixels per inch, our 3072-by-2048 pixel image will yield a 30.72-by-20.48 inch print. At 300 pixels per inch, the same image will make a 10.24-by-6.83 inch print. So resolution is a fungible quality—you can spread the same pixels over a smaller or larger area. Figure 2-1 Image size and resolution Chapter 2: How Camera Raw Works 17 To find out how big an image you can produce at a specific resolution, divide the pixel dimensions by the resolution. Using pixels per inch (ppi) as the resolution unit and inches as the size unit, if you divide 3,072 (the long pixel dimension) by 300, you obtain the answer 10.24 inches for the long dimension and if you divide 2,048 (the short pixel dimension) by the same quantity, you get 6.826 inches for the short dimension. At 240 ppi, you get 12.8 by 8.53 inches. Conversely, to determine the resolution you have available to print at a given size, divide the pixel dimensions by the size, in inches. The result is the resolution in pixels per inch. For example, if you want to make a 10-by-15 inch print from your six-megapixel, 3,072-by 2,048 pixel image, divide the long pixel dimension by the long dimension in inches, or the short pixel dimension by the short dimension in inches. In either case, you'll get the same answer, 204.8 pixels per inch. Figure 2-1 shows the same pixels printed at 50 pixels per inch, 150 pixels per inch, and 300 pixels per inch. 50 ppi 150 ppi 300 ppi But each individual pixel is defined by a set of numbers, and these numbers also impose limitations on what you can do with the image, albeit more subtle limitations than those dictated by the pixel dimensions. Bit Depth, Dynamic Range, and Color We use numbers to represent a pixel’s tonal value—how light or dark it is—and its color—red, green, blue, yellow, or any of the myriad gradations of the various rainbow hues we can see. 18 Real World Camera Raw with Adobe Photoshop CS Bit Depth. In a grayscale image, each pixel is represented by some num- ber of bits. Photoshop's 8-bit/channel mode uses 8 bits to represent each pixel, and its 16-bit/channel mode uses 16 bits to represent each pixel. An 8-bit pixel can have any one of 256 possible tonal values, from 0 (black) to 255 (white), or any of the 254 intermediate shades of gray. A 16-bit pixel can have any one of 32,769 possible tonal values, from 0 (black) to 32,768 (white), or any of the 32,767 intermediate shades of gray. If you're wonder- ing why 16 bits in Photoshop gives you 32,769 shades instead of 65,536, see the sidebar “High-Bit Photoshop,” later in this chapter (if you don't care, skip it). So while pixel dimensions describe the two-dimensional height and width of the image, the bits that describe the pixels produce a third dimension that describes how light or dark each pixel is—hence the term bit depth. Dynamic Range. Some vendors try to equate bit depth with dynamic range. This is largely a marketing ploy, because while there isa relation- ship between bit depth and dynamic range, it’s an indirect one. Dynamic range in digital cameras is an analog limitation of the sensor. The brightest shade the camera can capture is limited by the point at which the current generated by a sensor element starts spilling over to its neighbors—a condition often called “blooming”—and produces a feature- less white blob. The darkest shade a camera can capture is determined by the more subjective point at which the noise inherent in the system over- whelms the very weak signal generated by the small number of photons that hit the sensor—the subjectivity lies in the fact that some people can tolerate a noisier signal than others. One way to think of the difference between bit depth and dynamic range is to imagine a staircase. The dynamic range is the height of the staircase. The bit depth is the number of steps in the staircase. If we want our staircase to be reasonably easy to climb, or if we want to preserve the illusion of a continuous gradation of tone in our images, we need more steps in a taller staircase than we do in a shorter one, and we need more bits to describe a wider dynamic range than a narrower one. But more bits, or a larger number of smaller steps, doesn’t increase the dynamic range, or the height of the staircase. Chapter 2: How Camera Raw Works 19 High-Bit Photoshop If an 8-bit channel consists of 256 levels, a 10-bit channel consists of 1,024 levels, and a 12-bit channel consists of 4,096 levels, doesn't it follow that a 16-bit channel should consist of 65,536 levels? Well, that’s certainly one way that a 16-bit channel could be constructed, but it’s not the way Photoshop does it. Photoshop's implementation of 16 bits per channel uses 32,769 levels, from 0 (black) to 32,768 (white). The advantage of this approach is that it provides an unambiguous mid- point between white and black, useful in many imaging opera- tions, that a channel comprising 65,536 levels lacks. To those who would claim that Photoshop's 16-bit color is really more like 15-bit color, I simply point out that it takes 16 bits to represent, and by the time capture devices that can actually capture more than 32,769 levels are at all common, we'll all have moved on to something like 32-bit floating point channels rather than 16-bit integer ones. Color. RGB color images comprise three 8-bit or 16-bit grayscale imag- es, or channels, one representing shades of red, the second representing shades of green, and the third representing shades of blue. Red, green, and blue are the primary colors of light, and combining them in different proportions allows us to create any color we can see. So an 8-bit/channel RGB image can contain any of 16.7 million unique color definitions (256 x 256 x 256), while a 16-bit/channel image can contain any of some 35 trillion unique color definitions. Either of these may sound like a heck of a lot of colors, and indeed, they are. Estimates of how many unique colors the human eye can distinguish vary widely, but even the most liberal estimates are well shy of 16.7 million and nowhere close to 35 trillion. Why then do we need all this data? We need it for two quite unrelated reasons. The first one, which isn’t particularly significant for the purposes of this book, is that 8-bit/channel RGB contains 16.7 million color definitions, not 16.7 million perceivable colors. Many of the color definitions are redundant: Even on the very best display, you'd be hard pressed to see the difference between RGB values of 0,0,0, and 0,0,1 or 0,1,0 or 1,0,0, or for that matter between 255,255,255 and 254, 255, 255 or 255, 254, 255 or 255, 255, 254. Depending on the specific flavor of RGB you choose, you'll find similar redundancies in different parts of the available range of tone and color. The second reason, which is extremely significant for the purposes of this book, is that we need to edit our images—particularly our digital raw 20 Real World Camera Raw with Adobe Photoshop CS Figure 2-2 Digital capture and human response images, for reasons that will become apparent later—and every edit we make has the effect of reducing the number of unique colors and tone levels in the image. A good understanding of the impact of different types of edits is the best basis for deciding where and how you apply edits to your images. Gamma To understand the key difference between shooting film and shooting digital, you need to get your head around the concept of gamma encoding. As I explained in Chapter 1, digital cameras respond to photons quite dif- ferently from either film or our eyes. The sensors in digital cameras simply count photons and assign a tonal value in direct proportion to the number of photons detected—they respond linearly to incoming light. Human eyeballs, however, do not respond linearly to light. Our eyes are much more sensitive to small differences in brightness at low levels than at high ones. Film has traditionally been designed to respond to light approximately the way our eyes do, but digital sensors simply don’t work that way. Gamma encoding is a method of relating the numbers in the image to the perceived brightness they represent. The sensitivity of the camera sensor is described by a gamma of 1.0—it has a linear response to the in- coming photons. But this means that the captured values don't correspond to the way humans see light. The relationship between the number of photons that hit our retinas and the sensation of lightness we experience in response is described by a gamma of somewhere between 2.0 and 3.0 depending on viewing conditions. Figure 2-2 shows the approximate dif- ference between what the camera sees and what we see. How the camera sees light How the human eye sees light Chapter 2: How Camera Raw Works 21 I promised that I'd keep this chapter equation-free—if you want more information about the equations that define gamma encoding, a Google search on “gamma encoding’ will likely turn up more than you ever want- ed to know—so I'll simply cut to the chase and point out the practical implications of the linear nature of digital capture. Digital captures devote a large number of bits to describing differ- ences in highlight intensity to which our eyes are relatively insensitive, and a relatively small number of bits to describing differences in shadow intensity to which our eyes are very sensitive. As you're about to learn, all our image-editing operations have the unfortunate side effect of reducing the number of bits in the image. This is true for all digital images, whether scanned from film, rendered synthetically, or captured with a digital cam- era, but it has specific implications for digital capture. With digital captures, darkening is a much safer operation than lighten- ing, since darkening forces more bits into the shadows, where our eyes are sensitive, while lightening takes the relatively small number of captured bits that describe the shadow information and spreads them across a wider tonal range, exaggerating noise and increasing the likelihood of posteriza- tion. With digital, you need to turn the old rule upside down—you need to expose for the highlights, and develop for the shadows! Image Editing and Image Degradation Just about anything you do to change the tone or color of pixels results in some kind of data loss. If this sounds scary, rest assured that it’s a normal and necessary part of digital imaging. The trick is to make the best use of the available bits you’ve captured to produce the desired image appear- ance, while preserving as much of the original data as possible. Why keep as much of the original data as possible if you're going to wind up throwing it away later? Very simply, it’s all about keeping your options open. The fact is, you don't need a huge amount of data to represent an image. But if you want the image to be editable, you need a great deal more data than you do to simply display or print it. Figure 2-3 shows two copies of the same image. They appear very similar visually, but their histograms are very different. One contains a great deal more data than the other. 22 Real World Camera Raw with Adobe Photoshop CS Figure 2-3 Levels and appearance This image was produced by making corrections in Camera Raw, producing a 16- bit-per-channel image in Photoshop. This image was produced by converting at Camera Raw default settings, producing an 8-bit-per-channel image that was further edited in Photoshop. ii dh dh The two images shown above appear quite similar, but the histograms shown to the right of each image reveal a significant difference. The lower image contains a great deal less data than the upper one. Careful examination may reveal subtle differ- ences in hue and detail, but the biggest difference is the amount of editing headroom each image offers. Despite the vast difference in the amount of data they contain, it’s hard to see any significant differences between the two images—you may be able to see that the one with more data shows more details on the chest feathers of the top birdie, but it’s a pretty subtle difference. Figure 2-4 shows what happens when a fairly gentle curve edit is applied to the im- ages shown in Figure 2-3. The difference is no longer subtle! Figure 2-4 Levels and editing headroom Chapter 2: How Camera Raw Works 23 Here you see the images from Figure 2-3 after application of a fairly gentle S-curve (to increase contrast slightly) to both images. The differences between the data-rich (upper) and data-poor (lower) versions are now much more obvious. The data-poor version shows much less detail, and displays some unwanted hue shifts. The difference between the two images is in the way they were edited. The one with the larger amount of data made full use of Camera Raw to convert the raw file into a 16-bit/channel image in Photoshop. Additional edits were done in 16-bit/channel mode. The one with the smaller amount of data was converted to an 8-bit/channel image at camera default settings, and the edits were performed in 8-bit/channel mode in Photoshop. Losing Data and Limiting Options The sad truth is that every edit you make limits the options that are avail- able to you afterward. You can keep many more options open by making full use of Camera Raw controls and by converting to a 16-bit/channel image rather than an 8-bit one. But no matter what you do, edits degrade the data in an image file in three different ways. 24 Real World Camera Raw with Adobe Photoshop CS Clipping. The black and white input sliders in Photoshop's Levels com- mand and the Exposure and Shadows sliders in Camera Raw are clipping controls. They let you force pixels to pure white (level 255) or solid black (level 0). Depending on how you use the sliders, you may clip some levels—in fact, it’s often desirable to do so. On the highlight end, you normally want to make sure that specular highlights are represented by level 255, so if the image is underexposed, you usually want to take pixels that are darker than level 255 and force them to pure white. But if you go further than that, you may clip some levels—for example, if you have pixels at levels 252, 253, and 254, and you set the white input slider in Levels to level 252, then all the pixels at levels 252, 253, and 254 are forced to 255. Once you make this edit permanent, the differences between those pixels are gone, permanently. On the shadow end, you often want to clip some levels because typically there's a good deal of noise in the shadows. If everything below level 10 is noise, for example, it makes perfect sense to set the black input slider in Levels to 10, to force everything at level 10 and below to solid black. Again, you lose the distinction between the unedited levels 0 through 10 permanently, but it’s not necessarily a bad thing. Figure 2-5 shows how clipping works. However, if you're used to adjusting clipping in Photoshop's Levels, you'll find that the Exposure and Shadow controls in Camera Raw behave a bit differently from Levels’ black and white input sliders, partly because the latter works on linear-gamma data rather than the gamma-corrected data that appears in Photoshop, partly because Camera Raw’s Exposure slider can make negative as well as positive moves. If the camera can capture the entire scene luminance range, as is the case with the image in Figure 2-5, it’s usually best to adjust the Exposure and Shadows sliders to near-clipping, leaving a little headroom (unless you actually want to clip to white or black for creative reasons). If the camera can't handle the entire scene luminance range, you'll have to decide whether to hold the highlights or the shadows, and your choice may be dictated by the captured data—if highlights are completely blown, or shadows are completely plugged, there isn’t much you can do about it in the raw conversion. See the sidebar “How Much Highlight Detail Can I Recover?” later in this chapter. Figure 2-5 Black, white, and saturation clipping This raw image is under- exposed, but it captures the full luminance range of the scene, with no clipping of highlights or shadows. Ideally, you want to adjust the Exposure slider to push the data as far toward the right end of the histogram as possible without actually forcing clipping. In addition to clipping highlights with Exposure or shadows with the Shadows slider, you can force individual channels to clip by adding too much saturation. In this case, increasing the saturation has clipped the blue channel. Chapter 2: How Camera Raw Works 25 When you increase the Exposure slider value too far, you clip highlight pixels to solid white. When you increase the Shadows slider value too far, you clip shadow pixels to solid black. highlight clipping 26 Figure 2-6 = ic Tonal range compression Channel: (RCD o and expansion wn this sdter 837 Ga When you use the Brightness slider in Camera Raw or the gray slider in Levels to brighten the midtones, you compress the highlights and expand the shadows. The images and histograms above show Camera Raw's Brightness control, and the histogram at right shows the results of using lh the gray input slider in Levels on an 8-bit/channel image. The gaps al Real World Camera Raw with Adobe Photoshop CS Tonal range compression. When you compress a tonal range, you also lose levels, in a somewhat less obvious way than you do with clipping moves. For example, when you lighten the midtones without moving the white clipping point, the levels between the midtone and the highlight get compressed. As a result, some pixels that were formerly at different levels end up being at the same level, and once you make the edit permanent, you've lost these differences, which may potentially represent detail. See Figure 2-6. Tonal range expansion. A different type of image degradation occurs when you expand a tonal range. You don't lose any data, but you stretch the data that’s there over a broader tonal range, and hence run the danger of losing the illusion of a continuous gradation. Almost everyone who has used Photoshop for more than a week has encountered the experience of pushing edits just a little too far and ending up with banding in the sky or posterization in the shadows. It’s simply caused by stretching the data over too broad a range, so that the gaps between the available levels become visibly obvious. See Figure 2-6. Medan: 138 Percent | did n nk This range This range ee nd are from expansion, the spikes from compression. Figure 2-7 Gamma conversions Chapter 2: How Camera Raw Works 27 If all this makes you think that editing images is a recipe for disaster, you've missed the point. You need to edit images to make them look good. Sometimes you want to throw away some data—shadow noise being a good example—and the inherent data loss is simply something that comes with the territory. It isn’t something to fear, just something of which you should be aware. The importance of the preceding information is that some editing methods allow you more flexibility than others. Color Space Conversions One other operation that usually entails all three of the aforementioned types of image degradation is color space conversions. When you convert from a larger gamut to a smaller one, colors present in the source space that are outside the gamut of the destination space get clipped (see Figure 1-5 in the previous chapter for an illustration of gamut clipping). A significant number of levels also get lost in conversions between spaces with different gammas or tone curves. The bigger the difference between the gammas, the more levels get lost. Figure 2-7 shows what hap- pens when you convert a linear-gamma gradient to a gamma 1.8 working space in both 8-bit/channel and 16-bit/channel modes. Even in 16-bit per channel mode, the shadows get stepped on pretty hard, and in 8-bit per channel mode, about 25 percent of the levels have disappeared. a linear (gamma 1.0) gradient original data 16-bit conversion 8-bit conversion to gamma 1.8 to gamma 1.8 The Camera Raw Advantage The reason all this stuff about data loss and image degradation is relevant is that one of the main tasks Camera Raw performs is to convert images from native, linear-gamma camera RGB to a gamma-corrected working space. When you use the controls in Camera Raw, you aren't just editing the pixels you captured, you're also tailoring the conversion. As you saw 28 Real World Camera Raw with Adobe Photoshop CS back in Figures 2-3 and 2-4, it’s possible to arrive at the same image ap- pearance with a robust file that contains plenty of data and hence offers plenty of editing headroom, ora very fragile file containing relatively little data that will fall apart under any further editing. Since the raw conversion is at the beginning of the image-processing pipeline, and the converted images may be subjected to many different color space conversions and many different edits to optimize them for different output processes, you'll save yourself a world of grief if you use Camera Raw’s controls to deliver as robust a file as you can muster. If you're skilled in Photoshop, and you avoid exposure problems, you may well be able to arrive at the desired image appearance by converting all your images at camera default settings and doing all the work in Photoshop, but the resulting files will be much more fragile than if you learn to exploit the controls that Camera Raw offers. From Raw to Color At long last, we come to the nitty-gritty of the conversion from Camera Raw to gamma-corrected RGB. In the next chapter, Using Camera Raw Controls, we'll look at the various ways it makes sense to use the various controls Camera Raw offers. Here, though, we'll look at how they actually apply to the raw conversion. Demosaicing and Colorimetric Interpretation The first stage of the process, demosaicing, introduces the color informa- tion, turning the grayscale image into an RGB one. This stage is also where the initial colorimetric interpretation occurs—the grayscale is converted to a “native camera space” image, with linear gamma and primaries (usually, but not always, R, G, and B—some cameras add a fourth color filter) defined by the built-in profiles that define each supported camera's color space. (See the sidebar “Camera Raw and Color” for more details on how Camera Raw handles the tricky task of defining camera color.) The demosaicing and colorimetric interpretation happen automatically to produce the default rendering you see in the File Browser and the larger one you see when you open the image in Camera Raw. Chapter 2: How Camera Raw Works 29 Operationally, the first step is the colorimetric interpretation. The de- mosaicing is then performed in linear-gamma camera space. A little noise reduction, and any chromatic aberration corrections, are also done in the native camera space. (Chromatic aberration corrections could cause unwanted color shifts if they were done later in a non-native space.) White Balance and Calibrate Adjustments White Balance (Color Temperature and Tint), in addition to any adjustments made in Camera Raw’s Calibrate tab, actually tweak the conversion from native camera space to an intermediate, large-gamut processing space. (This intermediate space uses ProPhoto RGB primaries and white point, but with linear gamma rather than the native ProPhoto RGB gamma 1.8.) These operations work by redefining the colorimetric definition of the camera RGB primaries and white rather than by redistributing the pixel values. It's simply impossible to replicate these corrections in Photoshop, Camera Raw and Color One of the more controversial as- pects of Camera Raw is its color- handling, specifically the fact that Camera Raw has no facility for applying custom camera pro- files. Having tried most camera profiling software, and having ex- perienced varying degrees of dis- appointment, I've concluded that unless you're shooting in the stu- dio with controlled lighting and a custom white balance for that lighting, camera profiling is an exercise in frustration if not futil- ity, and I've come to view Camera Raw’s incompatibility with cus- tom camera profiles as a feature rather than a limitation. The way Camera Raw handles color is ingenious and, thus far, unique. For each supported cam- era, Thomas Knoll, Camera Raw’s creator, has created not one but two profiles: one built from a tar- get shot under a D65 (daylight) light source, the other built from the same target shot underan Illu- minant A (tungsten) light source. The correct profiles for each cam- era are applied automatically in producing the colorimetric inter- pretation of the raw image. Cam- era Raw’s White Balance (Color Temperature and Tint) sliders let you interpolate between, or even extrapolate beyond, the two built- in profiles. For cameras that write a read- able white balance tag, that white balance is used as the “As Shot” setting for the image; for those that don’t, Camera Raw makes highly educated guesses. Either way, you can override the initial settings to produce the white bal- ance you desire. It's true that the built-in profiles are “generic” profiles for the cam- era model. Some cameras exhibit more unit-to-unit variation than others, and if your camera differs substantially from the unit used to create the profiles for the camera model, the default color in Cam- era Raw may be a little off. So the Calibrate controls let you tweak the conversion from the built- in profiles to optimize the color for your specific camera. This is a much simpler, and arguably more effective, process in most situations than custom camera profile creation (see “Using the Calibrate Controls” in Chapter 3, Using Camera Raw, for a detailed description of the process. 30 Real World Camera Raw with Adobe Photoshop CS so it's vital that you take advantage of Camera Raw to set the white balance and, if necessary, to tweak the calibration for a specific camera. (I'll save the detailed description of how to use these controls for the next chapter, Using Camera Raw Controls.) The remaining operations are carried out in the intermediate linear-gam- ma version of ProPhoto RGB. In many cases, it’s possible to achieve a similar appearance by editing in Photoshop, but the Camera Raw controls still offer some significant advantages. The Exposure control is a case in point. Exposure The Exposure slider is really a white-clipping control, even though it affects the whole tonal range. The big difference between the Exposure slider and the Brightness slider is that the former lets you change the white clipping point, whereas the latter does not. With positive values, the Exposure slider behaves very much like the white input slider in Photoshop's Levels com- mand, clipping levels to white. But since it’s operating on linear-gamma data, it tends to be gentler on the midtones and shadows than white clip- ping in Photoshop on a gamma-corrected image, and it offers much finer control over the white clipping than does the white input slider in Levels ona gamma-corrected image. With negative values on the Exposure slider, the story is very differ- ent. Unlike most raw converters, Camera Raw offers “highlight recovery.” Most raw converters treat all pixels where one channel has clipped at the highlights as white, since they lack complete color information, but Cam- era Raw can recover a surprising amount of highlight detail even when it exists only ina single channel. It does, however, maintain pure white (that is, clipped in all channels) pixels as white, unlike some other converters that let you turn clipped pixels gray, and then it lowers the gamma to darken the rest of the image, using special algorithms to maintain the color of the non-white pixels. See the sidebar “How Much Highlight Detail Can 1 Recover?” for more technical details, and see Figure 2-8 for a real-world example of highlight recovery. It's simply impossible to match Camera Raw’s highlight detail recovery effectively in Photoshop on a gamma-corrected image. In linear-gamma space, fully half of the captured data describes the brightest f-stop, so you have a large number of bits describing the highlights. Once the image is converted to a gamma-corrected space, you have far fewer highlight bits to play with. Chapter 2: How Camera Raw Works 31 Figure 2-8 Highlight recovery This image is overexposed, as indicated by the white spike at the right end of the histogram. GE Pron ——— Reducing the value of the Exposure slider to -0.75 stops brings the highlights back into range. The amount of headroom varies from camera to camera, but this particular camera easily allows a three-quarter stop recovery on this image. Increasing the Brightness slider value to 60 and the Contrast slider value to 64 counteracts the darkening effect of the Exposure adjustment. Raising the Shadow slider to 6 puts punch back in the shadows. 32 Real World Camera Raw with Adobe Photoshop CS How Much Highlight Detail Can I Recover? The answer, of course, is “it de- pends.” If the captured pixel is completely blown out—clipped to white in all three channels—there is no highlight detail to recover. If a single channel, (or, better, two channels) still contain some in- formation, Camera Rawwill do its best to recover the detail and attri- bute natural-looking color to it. The first stage of highlight re- covery is to use any headroom the camera leaves by default—this varies considerably from vendor to vendor, with some leaving no headroom at all. The next stage uses Camera Raw’s highlight re- covery logic to build color infor- mation from the data in one or two unclipped channels. Next, the amount of highlight compres- sion introduced by the Brightness slider is reduced, stretching the available highlight data over a wider tonal range. The final stage is application of a gamma curve to map the midtones and shadows correctly. Several different factors limit the amount of highlight data you can recover, and these vary from camera model to camera model. The first is the sensor clipping it- self—the point at which all three channels clip. You can recover a lot of highlight data when only one channel contains data, but if you stretch the highlights too far, the transition between the totally blown-out highlights and the re- covered ones looks unnatural. Also, some cameras run the sensor chip slightly past its linear range, producing hue shifts near the clip- ping point, and these hue shifts get magnified by the extended highlight recovery process—if you try to stretch the highlight data too far, you'll get strange colors—so in either case the practical limit may be lower than the theoreti- cal one. Most cameras use analog gain to provide different ISO speeds, but some use digital gain in- stead—a high-ISO image from these cameras is essentially just an underexposed image with built-in positive exposure compensation applied—so a lot of highlight data can be recovered by undoing the positive exposure compensation. The white balance also has an effect on highlight recovery, since it scales the clipped channels to match the unclipped one. When you're attempting extreme high- light recovery it’s often a good idea to adjust the Exposure slider before setting white balance, be- cause the white balance is likely to change as you stretch the high- lights anyway. In practice, most cameras will let you recover at least a quarter stop of highlight data if you're willing to compromise a little on the white balance. Many cameras will let you recover at least one stop, possibly more, but the full four-stop range offered by the Exposure slider is beyond the use- ful range for most cameras. I don't advocate deliberate overexposure, but if you're shooting in changing lighting conditions, the linear- gamma nature of digital captures makes it preferable to err on the side of slight overexposure rather than underexposure, because un- derexposing to hold the highlights will make your shadows noisier than they need be. In these situ- ations, Camera Raw’s highlight recovery provides a very useful safety net. Shadows The Shadows slider is the black clipping control. It behaves very much like the black input slider in Photoshop's Levels command, but its effect tends to be more drastic, simply because it’s operating on linear-gamma data, which devotes very few bits to the deepest shadows. As a result, the Shadows control is a bit of a blunt instrument. Chapter 2: How Camera Raw Works 33 Of all the Camera Raw controls, Shadows is the one that most demands caution. Rather than using it to set a black clipping point, I recommend leaving a little headroom, so that you can fine-tune the black point in Photoshop on the gamma-corrected image, where you can operate with a little more finesse. Brightness and Contrast The Brightness and Contrast controls affect the rendering tone curve controlling the conversion from linear-gamma ProPhoto RGB to the final gamma-corrected output space. They work completely differently from the Photoshop controls that share their names—Photoshop’s Contrast and Brightness. They behave similarly to Photoshop's Levels and Curves, respectively (Brightness is a gamma adjustment, Contrast is an S-curve) but with one important difference. The Camera Raw controls use an algo- rithm that preserves the original hue, whereas hard curve adjustments to the composite RGB curve in Photoshop can cause slight hue shifts. If you make little or no adjustment with the Exposure slider, it’s prob- ably a wash as to whether you use Camera Raw’s Brightness and Contrast sliders or make the adjustments post-conversion using Photoshop's tools. But if you make significant Exposure adjustments, it's well worth using Camera Raw’s Brightness and Contrast sliders to fine-tune them, and if you make major Exposure adjustments, using Brightness and Contrast to counteract the extreme lightening or darkening the Exposure adjustments produce is essential (see Figure 2-8, earlier in this chapter). Saturation The Saturation slider operates similarly to the master saturation slider in Photoshop's Hue/ Saturation command—the slight differences are mostly due to Camera Raw’s slider operating on the linear-gamma data while Photoshop's operates on gamma-corrected data. As with the Exposure and Shadows controls, the Saturation slider can introduce clipping, so it’s best used with caution, if at all. Size Camera Raw allows you to convert images at the camera's native resolution, or at larger or smaller sizes—the specific sizes vary from camera model to camera model, but they generally correspond to 50 percent, 66 percent, 100 percent, 133 percent, 166 percent, and 200 percent of the native size. 34 Real World Camera Raw with Adobe Photoshop CS For cameras that capture square pixels, there’s usually very little difference between resizing in Camera Raw and upsizing in Photoshop using Bicubic Smoother or downsizing in Photoshop using Bicubic Sharper. However, if you need a small file, it's usually more convenient to convert to a smaller size in Camera Raw than to downsample in Photoshop after the conversion. For cameras that capture non-square pixels, the native size is the one that most closely preserves the original pixel count, meaning that one dimension is upsampled while the other is downsampled. The next size up preserves the pixel count along the higher-resolution dimension, upsampling the lower-resolution dimension to match and create square pixels in the converted image. This size preserves the maximum amount of detail for non-square-pixel cameras, and it will typically produce bet- ter results than converting to the smaller size and then upsampling in Photoshop. The one size up is also useful for Fuji SuperCCD cameras, which use a 45-degree rotated Bayer pattern. The one size up keeps all the original pixels and fills in the holes caused by the 45-degree rotation. The native pixel count size actually uses the rotation and filling in from the one-size- up processing, and then downsamples to the native pixel count. Sharpening Camera Raw's sharpening is relatively unsophisticated, with only one parameter: strength. It’s handy for doing quick-and-dirty sharpening for preliminary versions of images, but it’s not as flexible as Photoshop's sharpening features because it’s applied to the entire image, and it lacks a radius control to let you tailor the sharpening to the image content. Camera Raw offers the option to apply sharpening to the preview image only, leaving the converted image unsharpened. This option can be quite useful in helping you set the overall image contrast, because a completely unsharpened image generally looks flatter than one that has had some sharpening applied. Some pundits claim that sharpening should always be applied in linear- gamma space (as is Camera Raw’s sharpening). Frankly, I've yet to see any major benefit in doing so, and the relative lack of control over sharpening in Camera Raw always leads me to sharpen post-conversion in Photoshop unless speed outweighs quality. Chapter 2: How Camera Raw Works 35 Luminance and Color Noise Reduction While the Sharpening control is mildly convenient, the Luminance Smoothing and Color Noise Reduction controls in Camera Raw are sim- ply indispensable. Luminance noise manifests itself as random variations in tone, usually in the shadows, though if you shoot at high ISO speeds it can spread all the way up into the midtones. Color noise shows up as random variations in color. Before the advent of Camera Raw, I used to rely on rather desperate Photoshop techniques that involved converting the image to Lab so that I could address color noise and luminance noise separately, usually by blurring the a and b channels to get rid of color noise, and blurring or despeckling the Lightness channel to get rid of Luminance noise. Com- pared to the controls offered by Camera Raw, these techniques were very blunt instruments indeed—the round trip from RGB to Lab and back is fairly destructive due to rounding errors, and working on the individual channels is time-consuming. Thanks to some nifty algorithms, Camera Raw lets you address color noise and luminance noise separately without putting the data through a conversion to Lab—the processing is done in the intermediate large- gamut linear RGB. Camera Raw’s noise reduction controls are faster, less destructive, and more effective than anything you can do in Photoshop. So use them! Watch the Histogram! The histogram display is one of Camera Raw’s most useful but often most- overlooked features. Throughout this chapter, I’ve harped on the useful- ness of the histogram as a tool for analyzing the image, and especially for judging clipping. But the histogram in Camera Raw differs from the histograms you see on-camera in an important way. Camera Raw’s histogram is more trustworthy than the histograms that cameras display—they almost invariably show the histogram of the JPEG you'd get if you shot JPEG at the current camera settings rather than raw. As a result, they're useful as a rough guide to exposure, but not much more. Most camera vendors apply a fairly strong default tone curve to the default, in-camera raw-to-JPEG conversion, perhaps in an effort to pro- duce a default result that more closely resembles transparency film. 36 Real World Camera Raw with Adobe Photoshop CS Instead, Camera Raw’s histogram shows you, dynamically, the histo- gram of the converted image. It lets you see clipping in its various forms— clipping highlights to white, clipping shadows to black, or clipping one or more channels to totally saturated color. It also lets you see the effect of the various controls on the converted image data. Watching what happens to both the histogram and the preview image as you operate the controls will give you a much better understanding of what’s happening to the image than simply looking at the preview alone. In the next chapter, we'll look in detail at the many ways you can use the Camera Raw controls to get the best out of your raw captures. But if you're new to digital imaging, or even if you're just new to digital capture, it’s well worth spending some time mulling over the contents of this chapter, because digital capture really is significantly different from film, and understanding how numbers are used to represent images is key to grasping and, eventually, exploiting that difference.