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Posts Tagged ‘What the Heck is a Histogram?’

As discussed in parts one and two of this series, histograms are very useful for assessing dynamic range, contrast and exposure while in the field and using what histograms show to adjust camera settings. Histograms also are useful after the shot in making adjustments on your computer, referred to as post processing. As noted, this series of posts shares some excellent material from Varina and Jay Patel’s ebook, entitled “What the heck is a HISTOGRAM?”  For a more detailed discussion on how to use histograms in post processing, check out their ebook. It’s excellent!

All Schutzhund photographers struggle to conquer the ever-present challenge of properly exposing for the background and the dog. Often, these are conflicting goals, as by themselves the dog and the background more often than not require completely different settings to achieve proper exposure. And, unlike landscape and portrait photography, our subjects don’t sit still, so using HDR and bracketing techniques is not an option. Shooting in RAW, while preferable, also is not practical when shooting in burst mode, at least not in my experience.

Consider this image and its histogram:

Histograms Orig 1

Histograms Org RGB 1

The background and the dog appear to be similarly exposed; that is, the dog is not significantly darker or lighter than the background and the quality of the detail and color are compatible. The level of detail also is apparent in the histogram as the bars are tall. Yet the image is a bit dark, except for the bleachers, which are bright white. This is indicated in the histogram as the spike up against the right (highlights) wall. Most of the other pixels trend towards the left (shadows) side of the histogram.

In post-processing, histograms are helpful in adjusting contrast to bring out the color and details as well as overall dynamic range to brighten highlights and deepen shadows. In this image, the shadows are already pretty deep.

Within photo editing programs, such as Adobe Lightroom and Photoshop (the two I use), photographers make these adjustments using curves or levels tools. First step is to adjust the dynamic range by moving the white point at the top of the curve or the far right in levels and the black point at the bottom of the curve or the far left in levels “so that they line up with the outer edges of the histogram. Jay and Varina caution to be sure to watch the histogram to “avoid lost detail in the highlights or shadows. But don’t ignore the image itelf. Extreme adjustments can add unwanted noise, artifacts and banding.”

The next step is to adjust the mid-tones. In curves, adding a “simple s-curve adds mid-tone contrast without eliminating details in the shadows and highlights…Pulling the right half of the curve upward stretches the right side of the histogram outward – effectively adding contrast to the brighter tones, with out moving the white point.” Pulling the curve in the opposite direction or downward opens up the left side of the histogram “adding contrast in the darker tones without moving the black point.” In the levels tool. these same effects may be achieved by moving the middle arrow.

Which tool you use is personal preference. I prefer the levels adjustment tool, but many other photographers like the curves adjustment tool.

These adjustments can be seen both in the image and the histogram below. Notice that the bleachers have been removed and the image cropped to bring greater emphasis on the dog. The histogram shifted more towards the middle and the spike next to the right (highlights) wall is significantly reduced. There is also more detail in the highlights area that was missing in the original photo.
Histograms Adjust 1
Histograms Adjust RGB 1
When confronted with a background that is brighter than the dog or a dog that is significantly darker than the background, the best bet is to isolate each area and adjust each one separately. Consider the image below with its histogram. Both the background and the dog are very dark, yet I know from experience that if I adjust both together, the brighter areas of the dog and dumbbell will end up looking really weird, with some blown out highlights. The histogram bears out how dark the image is, yet there is a lot of detail.
Histograms Orig 2
Histograms Orig RGB 2
First step is to isolate the dog and adjust to bring the shadow area of the histogram towards the mid-tones, which will brighten up the darker areas of the dog. When I selected the dog, I did not select the dumbbell or the dogs legs and feet. They are more closely aligned with the background, so I elected to adjust them with the background. Once satisfied, inverse the selection and then adjust the background. I also used the burn tool, set on mid-tones at about 20 to 30 percent opacity, in Photoshop to fine tune the dumbbell and the tan fur on the dog’s legs. Below are the results, with the histogram at the top right:
Histograms Adjusted Image 2
The image is brighter, the dog’s face shows more detail, and the one really bright area along the dogs back leg is toned down. The dog and background look in balance. I could have brightened up the image even more, but it was taken early in the morning, so I wanted to be sure to retain the warmth of the light at that time of day. Notice how the histogram in the upper right is broader and extends into the mid-tones area, while still retaining the nice bell curve shape.

This series is just an introduction to histograms and how you can use them to enhance your photography. I encourage you to read Jay and Varina’s ebook, as well as view tutorials on the curves and levels tools. As always, thanks for visiting and Happy Shooting!

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Note: I have been trying to write this post for three weeks! Seriously! While it is my intention to post at least biweekly, these past few months have been very challenging. Here’s hoping the rest of 2013 is calmer, which will translate into more regular postings. Happy Shooting – and thanks for your patience!

A very cool feature on DSLR cameras is the histogram, yet many photographers do not take advantage of this tool while shooting or in post processing. Histograms can be very helpful to Schutzhund photographers as we often shoot fast moving dogs in varying and adverse lighting conditions. Histograms really are not as mysterious as they seem. The goal of this series of three posts is to demystify histograms and to offer tips and ideas on how to use them to improve your photography.

In researching this series of posts, I came across an excellent e-book by Varina and Jay Patel, entitled “What the heck is a HISTOGRAM?” I recommend it, and I am grateful to Varina and Jay for giving me permission to share some of their material.

Defining Histograms

Simply stated, histograms “provide a simple, and highly effective map of your image, based upon tonal values…the distribution of light and dark in an image – from the darkest black on the left to the brightest white on the right.”

An 8-bit histogram is comprised of 256 bars, each representing a tonal value. From left to right, 0 on the far left show true black, while 255 on the far right shows true white. The left third of the histogram represents shadows and the right third represents highlights. The middle area, which overlaps both the shadows and highlights areas, represents the mid-tones. The height of each bar indicates number of pixels in the image for that particular tonal value. A 16-bit histogram represents the same information, but the tonal values shown (number of bars) range from 0 to 65,535.

Below is an example:

Histograms Image 2

Histograms Image 2 RGB

This histogram shows the bars “spread out over the entire range of tones…The tallest bars are near the center of the histogram.” The bell-shape curve illustrates “an image with lots of brighter-mid-tone values,” although there is a small spike on the far right.

Many photographers drive themselves barking mad as they think that all the histograms for all their images must show a bell-shaped curve to be correct. This is a misnomer as there is no one correct histogram shape.  Consider the two images below:

Histograms Image 4

Histograms Image 4 RGB

The histogram for this image is more heavily weighted to the darker tones and shows higher spikes toward the 0 value or true black. It flattens out across the shadows section of the histogram and then shows more of a bell-shaped curve in the mid-tones, before gently dropping off to essentially no bars towards the 255 value or true white. This is not surprising given that the dog is black and there are a lot of shadows in the background. The tall spikes on the left side, especially near the left wall of the histogram, are indicative of the loss of detail in the ears, under the dog’s chin and along its belly.

Histograms Image 5

Histograms Image 5 RGB

In the image above, you can see there is no detail in the bright sun or the silhouetted tree line. This is shown in the histogram by the spikes along the left (shadows / blacks) and the right (highlights / white) walls. The mid-tonal area in-between is the sky. As you can see, both images look fine, but neither has a histogram with a classic bell-shaped curve.

A quick note about the terms “clipping” and “blown-out highlights.” In the image above, the dark tones and the tones in the center of sun have been clipped; that is there is no detail. “Blown-out highlights” is another term that essentially means the same thing; detail has been lost. Blowing out highlights most often occurs when an image is over exposed. These are just very general definitions. A future post, after this series on histograms, will look at these terms in more detail.

Types of Histograms

The most commonly used is the RGB histogram, which is a “composite that combines the tonal values for each color channel (red, green and blue) into a single graph.” Recall that digital images are “made up of pixels, and each pixel contains color information for red, green and blue. Every color you see in your image is actually made up on a combination of those three colors in varying amounts.”  Each channel has its own histogram, which are merged to create the RGB histogram.

Color histograms show all three channels individually and appear on a single graph. “Colors other that red, green and blue…indicate areas where the graphs overlap…[and] make it easier for us to read the histogram and compare channels.” Photographers use color histograms to see “how the color intensity is distributed throughout the image.” It also is useful for “determining which individual channel is clipped…[and] exactly where the detail is lost.”

Luminosity histograms “take into account the fact that the human eye is more sensitive to green light and less sensitive to red or blue light…It shows a perceived brightness.” It does this by showing “average values adjusted for human perception of light.” For a more in-depth discussion of the types of histograms, see Jay and Varina’s book.

The next post in this series will discuss how to interpret RGB histograms, as again these are the most commonly used, and based on what the histogram is telling you what you can do in the field to adjust exposures to avoid clipping or blown out highlights. This series of posts will conclude with some tips on how to adjust your images in post-processing using the histogram as your guide. Until then, Happy Shooting!

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