Digital Exposure (Exposing to the Right)

Article and Photography by Ron Bigelow

I want to start off by stating that not everyone accepts the information presented in this section on exposure. So, I will present the information and let the reader decide whether or not to utilize this exposure method (I do).

Exposure is one of the most basic of photographic skills. Proper exposure allows the photographer to maximize the quality of the image and to determine where detail will appear in an image. Yet, different photographic materials/equipment require different exposure methods. Photographers shooting color slide film must always keep in mind the very limited dynamic range of the film and the fact that the image can not be adjusted after exposure. This often means that they must expose in such a manner as to protect the highlights from blowing out. On the other hand, photographers that shoot black and white negative film have a very wide dynamic range and can make extensive adjustments to the image in the dark room. They often follow Ansel Adams advice to expose for the shadows and develop for the highlights. Digital cameras also have a best method of exposure. This method often differs from what many photographers utilize. 

In color photography, people often expose for the mid tones. They may take a meter reading off a gray card, or they may find a mid tone object in the image and meter off that. Some adjustments to the exposure may then be made to ensure that detail is kept in the shadows or highlights. On film, this makes the image look natural. The mid tones in the image appear as they did in the actual scene. 

Figure 1: Sedona

Figure 1 shows an image that was exposed this way. The image was shot during the last few minutes of evening light. The exposure was set so that the rocks would come in at the right tonal level. Figure 2 shows the histogram of this image. The histogram shows that the image is composed mostly of darker tones with fewer of the lighter tones present in the image. In other words, the histogram accurately reflects the tonal distribution present in the scene at the time of exposure. For film, this would be an acceptable exposure. However, such an exposure would fail to take advantage of the unique characteristics of the digital sensor. Such an exposure would fail to maximize both the number of shades available in the image and the signal to noise ratio.

Figure 2: Histogram -- Normal Exposure
To understand what is going on in this situation, we have to go back to the issue of bits. Since the exposure technique that is going to be discussed is generally used with raw, we will be dealing with sensors where each pixel is capable of rendering 4,096 shades of color.
Figure 3: Four Stop Exposure on a Five Stop Dynamic Range Sensor (Before Application of Any Tonal Curves)
Looking at Figure 2 again shows that the histogram does not extend all the way to the right. Since a histogram shows the distribution of pixels, from the pixels that received little light on the left side of the distribution to the pixels that received a lot of light on the right side, the gap to the right side of this distribution shows that no pixels reached full exposure (full well capacity). In other words, if the sensor is a 5 stop dynamic range sensor, no pixels reached five stops of exposure. A better guess would be that the brightest pixels in this image received a little more than 4 stops of light. For the sake of simplicity, let's assume that the sensor received exactly four stops of light. Figure 3 shows that when a five stop dynamic range pixel is exposed to 4 stops of light, it is capable of rendering only 2,048 shades (before the application of any tonal curves). Thus, this exposure method threw away half of the 4,096 shades that the camera is capable of rendering. As a consequence, this exposure reduced the quality of the image.
Figure 4: 100% Crop from a Low Noise Image
Figure 5: 100% Crop from a High Noise Image

In addition, this exposure method reduces image quality in another way. All digital sensors have noise. Noise is the digital equivalent of grain in film. At the pixel level, noise is a random variation in the charge on a pixel that is due to factors other than the scene that is being photographed. At the image level, noise manifests itself by random detail in an image that distracts from the real detail (that comes from the scene that was photographed) in the image. Noise is usually most noticeable in areas of little detail such as featureless skies or out of focus backgrounds. Figure 4 shows a 100% crop from a section of sky from an image that has very little noise. As can be seen, the image looks very clean with no distracting random detail. Figure 5 shows a 100% crop from an out of focus background of an image with significant noise. The image has an almost sandpaper like look. The noise noticeably reduces the quality of the image.


The real issue for digital cameras is the signal to noise ratio (SNR), which is the ratio of the signal the pixels get to the noise that is generated during the exposure. The higher the SNR, the better the image quality. Anything that degrades the SNR, degrades the image quality. The SNR can be degraded by either increasing the noise or decreasing the signal. On the noise side of the SNR, sensors have several types of noise. Some of them vary with the amount of exposure (more exposure means more noise but also more signal). However, one type of noise is relatively constant; it does not vary with the amount of exposure. This noise is known as dark current noise, and it is in large part responsible for determining the noise floor (along with the readout noise) for the sensor. Anything that increases the signal with respect to the noise floor increases the SNR. Conversely, anything that decreases the signal with respect to the noise floor reduces the SNR.

A look back at Figures 2 and 3 indicate how a "normal" exposure impacts the SNR. The histogram in Figure 2, again, shows that no pixels reached full well capacity. As before, assuming a four stop exposure on a five stop dynamic range sensor, Figure 3 shows that the brightest pixels in the image are filled only half way. The green part of the pixel in Figure 3 shows that the pixel has only half the number of shades as a full well capacity sensor. This green part also represents the amount of signal that the pixel has received. Accordingly, the pixel has received only half as much signal as a full well capacity pixel. Since the signal has been reduced with respect to the noise floor, the SNR has been reduced by the normal exposure.

Figure 6: Histogram -- Maximum Exposure
The solution to both the issue of the reduced number of shades of color and the SNR degradation is to maximize the exposure (also known as exposing to the right). In the case of maximizing the exposure, the exposure is increased until the brightest pixels reach their full well capacity. In this case, the brightest pixels receive as much light as they can. The proper exposure can be checked with the camera's histogram. Figure 6 shows an image that used the maximum exposure. The histogram has been shifted to the right. It can be seen that there is no longer a gap on the right side of the histogram as in the normal exposure; instead, the brightest pixels just touch the right side of the chart.
Figure 7: Five Stop Exposure on a Five Stop Dynamic Range Sensor (Before Application of Any Tonal Curves)

Figure 7 shows what one of the brightest pixels with a maximized exposure would look like. It can be seen that this pixel is capable of rendering 4,096 shades. Comparing this figure to Figure 3 shows that we have recaptured the 2,048 shades that were lost with the normal exposure. By increasing the number of shades in the image, maximizing the exposure has increased the quality of the image.

Figure 7 also indicates how maximizing the exposure improves the SNR. The green part of the pixel in the figure shows that the pixel is now completely filled up with signal. Thus, the pixel has maximized its signal. Since the signal has been maximized with respect to the noise floor, the SNR has been significantly increased.

Consequently, maximizing the exposure insures that the image has the largest number of shades and the best SNR possible for any given set of conditions. Of course, the problem is that the image may now appear overexposed. If printed without any corrections, the image might be too light. The solution is easy; the entire image needs to be darkened in a linear fashion. What this means is that every pixel needs to be darkened by the same amount. This is easily handled by the exposure adjustment in the raw converter.

There is one caveat to this technique. The photographer must be careful not to carry the exposure too far. If too much exposure is given, the highlight details will be blown. Utilization of the histogram and the "blinkies" (when areas of the LCD screen on the camera blink to indicate areas of overexposure) will help the photographer determine the proper exposure.