The Art of Luminosity, Part 1

In nature, magical light is fleeting. It can transform a scene from ordinary to unforgettable, only to vanish into thin air. Will you be ready for it?

What makes good light so magical? Andvord Bay, Graham Land, Antarctic Peninsula.

Most of us can recognize good light when we’re paying attention. What can be more difficult to recognize is how light in a photograph can be properly exposed and later adjusted to enhance a particular scene, something that requires post-processing skills.

We all have a natural sensitivity to light and the quality of it. Bright light lifts our spirits, and low light can do the opposite. Contrast pulls our attention, while pastel colors make us contemplative, and the list goes on. Photographers have the opportunity to use these aspects of light as a powerful tool when creating images to tell stories or simply command attention.

What makes light especially challenging to photographers is when it is not what we had hoped. The shadows are too dark or in the wrong place. The color is wrong, or the contrast is too high. When things finally get a little better and shadows form in just the right location and the highlights are striking the subject, but it’s too dark, what will your camera settings be?

In these situations, you may struggle to select the correct camera settings, and, as a result, your image may not match what you envisioned. To help you improve your images through a better understanding of the effects and use of light, I want to share my knowledge and experience in this three-part article series. I’ll begin with the technical and end with the creative and fun.

Part one will cover the technical aspects of light and your camera’s ability to record it. I introduce you to the grayscale and histograms, important topics to review even if you’ve studied them previously.

Part two will help you with one of the most challenging aspects of photography: seeing the picture before you capture it. I will also describe how to pre-visualize the dynamic range of the scene so you can capture lighting conditions. In part three, we’ll cover processing your photographs to make the most of the light.

I have learned over the years that one must dive into the deep end to truly master the art of luminosity, and that’s what we’re going to do together. But before we get into all that, I want to share a story with you.

The Origins Of Digital Imaging Technology

I had been using digital technology for years without having any idea how or when the very first digital image was created. I first read a fictional account of what I thought might be the inception of digital imaging in the book Space by James Michener. He told of a young scientist working at the Jet Propulsion Laboratory who created an image from small printouts of ticker tape. Later, I had a chance meeting with the marketing director of the JPL and asked him about the scientist from Michener’s book. Did such a character and process exist? The director’s eyes lit up, and I knew he understood what I was referring to.

The first digital image was made in the late ’60s for NASA, as a way to record images of Mars. A scientist at JPL had invented a device that could scan a scene by recording it in tiny square segments. Each square was represented by three numbers, corresponding to the varying color densities of red, green and blue, each on a scale of 0 to 255. Those numbers were mapped to specific coordinates in the image. Because the squares were simply a mix of numbers, they could be transmitted back to Earth and reassembled. This eliminated the need to ship the film back to Earth, which would have been a nearly impossible feat. This process was highly classified at the time, and, as a result, that young scientist’s name has still not been released. However, his invention was the start of modern digital imaging technology.

Luminosity represented in a grayscale graph.

In this image, I selected a point with a density of 50 percent. This density is a midtone and in Lightroom is controlled by using the Exposure slider. This spot in the image is a snowfield in Tibet lit by the full moon at midnight on a very clear evening. I marked it on the grayscale below to illustrate how it applies to the density in this image. If you practice viewing these numbers in your images and plotting them to a grayscale chart, you have a powerful method to begin understanding how that density will be displayed, in print and on digital displays.

Luminosity & The Range Of Light

Current digital sensor technology works on a similar principle to those early attempts captured in the ’60s while exploring Mars. A pixel is the smallest point on a digital imaging sensor, also known as a sensor element. Firmware built into your camera combines data from every pixel to create the final image. Every pixel in an image file contains three numbers called density values. Each density value describes the density of the primary colors: red, green and blue (RGB). The density value for each color is a number between 0 and 255, where 0 is absolute black, and 255 is absolute white.

Dynamic Range is defined as the number of stops of light—from pure white to pure black—that a digital camera can record without creating digital noise. Each stop halves or doubles the amount of light in the exposure. When you increase the exposure by one stop, you double the amount of light in that exposure.

Image illustrating how RGB values are represented in a histogram.

Not all the scenes will push the dynamic range limits of our cameras, and there are many that will fall well within several stops of the available contrast ratio. This scene of Mount Everest was shot from 20+ miles away when the atmospheric haze combined with the distance softened the light and made it fit well within the dynamic range of a full-frame sensor camera.

When you point your camera at a scene, a specific contrast ratio must be captured. If your camera does not have the dynamic range to capture the required contrast ratio, it will not reproduce the scene as well as you had hoped.

To get beyond the dynamic range limitations of your sensor, you can take a bracketed set of exposures and combine them. Bracketing means that you vary the shutter speed with each shot until the entire dynamic range is captured—use a fast shutter speed for the highlights and a longer shutter speed for the shadows. This method is often referred to as high dynamic range (HDR) photography. The HDR method increases the amount of signal each pixel in the blacks and shadows receives, so it decreases the amount of digital noise.

Image showing brackted images and a final composite with expanded dynamic range.

The bracketed set of images above was shot with varying shutter speeds. Exposure times range from 1/8 sec to 8 seconds at ƒ/19. Below is the final image after merging in Adobe Lightroom. The seven images offered plenty of data to create a file with detail in every region of this image and with a very high dynamic range.

Perfecting Your Exposure

Recording a perfect exposure for any scene is quick and easy using the histogram built into your digital camera.

The histogram is simple to understand. The left side of the histogram represents the darker values, and the right is the lighter values. The center of the histogram shows the midtones. The height of the graph indicates the number of pixels with values in that tonal range.

If your image is dark, then the majority of the data appears on the left (dark) side of the histogram. If your image is very light, then the majority of the data appears on the right side.

Image of a whale and an accompanying histogram.

This scene of a killer whale breaching was captured at 1/350 sec, ƒ/5.6, ISO 800. This exposure was a compromise of settings to achieve a shutter speed that was fast enough to stop action while keeping the ISO low enough to obtain as much of the natural early-morning color as possible.

There are no “ideal” histogram shapes because many different-looking histograms can represent perfect exposures. To determine if your histogram indicates a correct exposure, you must understand three terms:

  • Clipping: This is when data “climbs” either edge of the histogram. Clipping indicates that you have exceeded the dynamic range of the sensor, and some detail is lost.
  • Overexposed: When data is clipped on the right side of the histogram, the image is overexposed, and detail is lost in the whites.
  • Underexposed: When data is clipped on the left side of the histogram, the image is underexposed, and detail is lost in the darks.

While clipping should be avoided as much as possible, the good news is that today’s modern sensors allow a little bit of clipped data to be recovered. Capturing the original file in RAW format simplifies the recovery of data in post-processing software such as Adobe Lightroom.

Expose To The Right (ETTR) is a technique used by photographers to maximize the amount of data captured in an exposure. The right side of the histogram contains more data, and the more data you capture in an exposure, the better the end result will be. If your exposure is on the right side of the histogram without clipping highlights, then you have successfully exposed to the right. This will make your image look too bright most of the time, creating the necessity to post-process it to your intended luminosity. In addition, you do not want to simply increase your ISO or open up your aperture. Increasing the ISO creates additional digital noise, while changing your aperture affects depth of field and diffraction, making your image less sharp.

Two examples of different images using the ETTR technique.

Notice that both exposures shown here are using the ETTR technique. The night scene is counter-intuitive as the mountain of data is on the left side of the histogram, but if you look closely, you can see that the exposure follows the basic rule of ETTR: The brightest data is pushed as far right as possible without clipping.

ETTR allows for several improvements in the final RAW file: greater signal-to-noise ratio, fuller-color gamut, and more latitude during post-production.

The best way to understand the benefits of ETTR is to look at the bit depth of various areas of brightness in an image. The bit depth of the brightest value is 4,096. If you reduce the exposure to one ƒ-stop darker, then the brightest value in the image is reduced to 2,048, or one-half of the previous exposure. Move another ƒ-stop darker, and the brightest value is reduced by another one-half to 1,024.

While there is no perceivable difference between 4,096 and 2,048 bits, reducing the bit depth by 50 percent increases the potential for digital noise and banding, narrowing the editing options. The issue is with shadows, where a difference between 64 to 32 bits is a significant decrease in data. When you lighten dark areas in post-processing, you also significantly increase digital noise. Dealing with shadows is what creates a balancing act between keeping your exposure to the right while maintaining a fast-enough shutter speed to capture action. By exposing as far to the right or as brightly as possible (without clipping the highlights), your image—especially in the shadows—is the best result from the camera.

Example of a low-dynamic range image.

Low Dynamic Range. This scene was captured in low light, before sunrise. Without direct sunlight, the only contrast in this scene is being created by the reflected light from the atmosphere.

A handy camera tool when practicing ETTR is the highlight clipping warning function (commonly called “blinkies”). When activated, the camera LCD flashes a color or pattern over areas where data is clipped. It’s a great reminder to adjust exposure.

There are two ways to achieve optimal exposure in your digital camera: using a histogram preview in your live view or digital viewfinder, or taking an exposure and previewing the file with the histogram displayed. I prefer the second option, as I do not like the histogram showing in my viewfinder while I’m composing because it overlaps important subject matter. When I have the time to do so, I prefer to record a file and then adjust for the second image.

Most digital cameras can present the histogram in two formats, either as a single graph representing all three colors or as three separate graphs, one for each color (red, green and blue). For the best results, use the three-color display, which is more precise, especially if only one color is clipped. Typically the red channel will clip while photographing the sunrise or sunset, and the blue channel will clip when photographing during blue hour.

Example of a high-dynamic range image.

High Dynamic Range. Most sunrise/sunset scenes looking into or toward the sun have more dynamic range than a single camera file can record. In these situations, it is best to capture the scene in more than one RAW image file, then combine them in post-processing.


  • Keep the data from clipping at either end.
  • Keep the data as close to the right/bright side of the histogram as possible (except for low-light exposure).
  • View your histogram in red, green and blue (RGB) to know when specific colors are clipping.

Exposing to the right is very helpful when optimizing your exposure for the scene, especially when you do not want to use the HDR technique.

How It Measures Up

To understand dynamic range, let’s compare digital imaging with film. The average dynamic range of a sheet of film is between 5 to 6 stops. In today’s digital cameras, the typical dynamic range is between 8 to 12 stops, with some sensors capturing up to 14 stops. The greater the number of stops between dark and light, the greater the dynamic range.

Top image exposure: 1/180 sec, ƒ/8, ISO 400. Bottom image exposure: 10 sec, ƒ/2.8, ISO 1600. Here are two examples of how many stops of light are present in recognizable scenes. Direct daylight is one stop brighter than the light in the shadow of a cloud. The light in a night scene recorded during a full moon with no manmade light is 15 stops darker than daylight.

We often hear phrases such as “that photographer has an eye” or “that photographer can see a picture.” These phrases refer to one’s ability to pre-visualize the final picture before it is taken. First, you must be able to see the subject. Then you must be able to see the light. Then you must be able to see the composition. And, finally, you must know exactly what you plan to do in post-production. Only then are you able to turn that golden nugget into a beautiful work of art. As I mentioned previously, making good light into a great image requires three skills:

  • Recognizing the quality of the light in any scene, especially the contrast ratio.
  • Capturing the light for your camera with the optimal settings for the light in the scene.
  • Post-processing the file to achieve the artistic vision you set out to achieve when you took the picture.

Acquiring these three skills takes years of experience observing light in the natural world and then reproducing it into a printed or displayed image. No matter how your work is displayed, your understanding of the light, your camera and post-processing will improve over time—but only if you work at it! 

This three-part article series is excerpted from The Art of Luminosity by Marc Muench, available as a free download at

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