One of the greatest benefits digital photography has over film is the ability to check an image’s exposure at capture (when you can do something about it). But as photographers we rely so much on our eyes, sometimes it’s difficult to accept that the image we see on our LCD is not a reliable representation of the tones you captured.
Not only does ambient light limit the range of tones visible on the LCD, every picture you click contains more shadow and highlight information than the camera LCD’s jpeg preview shows in even the best conditions. In fact, if a high dynamic range image looks good on your LCD, you’ve probably blown the exposure. Fortunately, our digital cameras do provide a reliable and unvarying representation of our exposure: the histogram.
A histogram is a graph of the tones in an image. While I imagine that any graph has the potential to evoke high school science trauma flashbacks, a histogram is really quite simple, simple enough to be read and interpreted in the blink of an eye. And not only is your histogram easy to read, it’s really the most reliable source of exposure feedback.
When an image is captured on a digital sensor, your camera’s “brain” samples each photosite (the individual pixels in the megapixel number used to measure sensor resolution), determining a brightness value that ranges from 0 (black) to 255 (white). Every brightness value from 1 to 254 is a shade of gray—the higher a photosite’s number, the brighter its tone.
Armed with the brightness values for each photosite in the image, the camera is ready to build the image’s histogram. The horizontal axis of the histogram has 256 discrete columns (0-255), one for each possible brightness value, with the 0/black column on the far left, and the 255/white column on the far right (they don’t display as individual columns because they’re crammed so close together).
Despite millions of photosites to sample, your camera builds a new histogram for each image instantly, adding each photosite’s brightness value to its corresponding column on the histogram, like stacking poker chips—the more photosites of a particular brightness value, the higher its corresponding column will spike.
Reading a histogram
The version of a picture that displays on your camera’s LCD is great for checking the composition, but the range of tones you can see in your LCD preview image varies with many factors, such as the camera’s LCD brightness setting and the amount of ambient light striking the LCD. Most important, because there’s more information in captured than the LCD preview can show even in the best conditions, you’ll never know how much recoverable data exists in the extreme shadows and highlights by relying on the LCD preview.
It’s human nature to try to expose a scene so the camera’s LCD image looks good, but an extreme dynamic range image that looks good on the LCD will likely have unusable highlights or shadows. As counterintuitive as that feels, exposing an image enough to reveal detail in the darkest shadows brightens the entire scene (not just the shadows), likely pushing the image’s highlights to unrecoverable levels. And making an image dark enough on the LCD to salvage bright highlights darkens the entire scene, all but ensuring that the darkest shadows to be too black. In fact, a properly exposed extreme dynamic range scene (a scene with both bright highlights and dark shadows, such as a sunrise or sunset) will look awful on the LCD (dark shadows and bright highlights). The histogram provides the only reliable representation of the tones you captured (or, in your live-view LCD display or mirrorless electronic viewfinder, of the tones you’re about to capture).
There’s no such thing as a “perfect” histogram shape. Rather, the histogram’s shape is determined by the distribution of light in the scene, while the left/right distribution (whether the graph is skewed to the left or right) is a function of the amount of exposure you’ve chosen to give your image. The histogram graph’s height is irrelevant—information that appears cut off at the top of the histogram just means the graph isn’t tall enough to display all the photosites possessing that tone (or range of tones).
When checking an image’s histogram for exposure, your primary concern should be to ensure that the none of the tone data is cut off on the left (lost shadows) or right (lost highlights). If your histogram appears cut-off on the left side, shadow detail is so dark that it registers as black. Conversely, if your histogram appears cut off on the right side, highlight detail is so bright that it registers as white.
Managing a histogram
In a perfect world, when you see your histogram cut off on the left (everything cut off on the left is detail-less black), you simply increase the exposure until the histogram shifts right (brighter) far enough that no shadow data is cut off. And if you see your histogram is cut off on the right, you decrease the exposure until the histogram shifts left (darker) enough that no highlight detail is cut off. Problem solved.
But many scenes contain a broader range of light, from the darkest shadows to the brightest highlights, than the camera can handle. In these scenes you can blend multiple exposures that cover the entire range of tones, apply a graduated neutral density filter (to moderate the sky). When those options aren’t available or practical, I usually save the highlights and sacrifice the shadows.
While the general goal is to ensure that none of the tone data is cut off on the left or right side of the histogram, the exposure you choose for a scene is ultimately a creative choice that isn’t bound to the way the scene looks to your eye. Though I often expose my scenes to match the amount of light my eyes see, sometimes I decide to make the scene darker or brighter than what I see.
While luminosity histogram most of us are familiar with is the luminosity histogram. But each photosite on a conventional sensor actually measures the tone of one of three colors: red, green, and blue (RGB). The RGB histogram uses the same pixel sampling process to separate the luminosity histogram into three separate, more granular, graphs, one for the brightness each color from 0 to 255.
The luminosity histogram shows the detail you captured, but it doesn’t tell you whether you captured all the color. In fact, the luminosity histogram could look fine even when two of the three RGB channels are clipped (cut off, indicating color is lost). So in high dynamic range scene (extreme highlights and shadows), or scenes with an extreme amount of one color (such a brilliant sunset or a backlit poppy), checking the RGB histogram to ensure that none of the image’s color channels are clipped is especially important. The solution for a clipped RGB channel is to reduce the exposure.
Exposing to the right
“Expose to the right” (ETR) is popular digital exposure technique that attempts to increase the exposure until the histogram is as far to the right as possible without clipping. I’m going to go into great detail about ETR except to say that is a valid approach the does indeed ensure the cleanest image data. Without getting too deep into the technical weeds, it is true that on a digital sensor there is in fact more information captured on the right side of the histogram.
I don’t usually teach ETR to beginners because the margin for error is much smaller on the right side of the histogram, failure (blown highlights) will ruin an image, and I probably won’t be with them long enough for them to master the technique. My advice to anyone trying to employ ETR in their photography is to know your camera well enough to know how far to the right you can push its histogram before the highlights aren’t recoverable. And when exposing to the right, you must-must-must rely on the RGB histogram, regardless of what the luminosity histogram says, to ensure that you haven’t clipped one of the color channels.
All that said, even if you’re not employing the ETR technique, when you see your histogram bunched to the left with a large empty gap on the right, you should increase your exposure until the histogram looks more centered, or even a little skewed to the right.
A picture is worth a thousand words
Trusting a histogram over a picture you can actually see requires a leap of faith. I can explain the concept until I’m blue in the face, but in my workshops the point doesn’t usually hit home without a demonstration. For example, the two Horseshoe Bend sunstar images below are from the same file—on the left is the way the picture looked when I captured it, along with its histogram; on the right is the same picture with just a few minutes of very basic processing in Lightroom and Photoshop (no plugins, blending, or any other elaborate processing).
If I’d have exposed this scene bright enough for the shadows to look good on my LCD (more like my eyes saw them), the highlights would have been hopelessly overexposed (white); if I’d have darkened my highlights enough to look good on my LCD, the shadows would have darkened to an unrecoverable black. I knew my best chance for capturing this high dynamic range scene with a single click was to ignore the LCD and trust the histogram.
Despite an image that didn’t look good at all on my LCD, the histogram on my Sony a7R II showed me that I’d captured virtually all of the scene’s shadows and most of its highlights. And because I captured this image in raw mode, I was confident that I had even more shadow and highlight information than my histogram indicated, a fact instantly confirmed with a rightward tug of Lightroom’s Shadows slider. With minimal processing effort, I was able to achieve final result you see here. (This is why landscape photographers are always begging for cameras with more dynamic range, and also why I use the Sony a7RII.)
Trusting My Histogram