Archive for March, 2013

Understanding the basic concepts of histograms is all well and good, but what do histograms tell photographers about their images and how are they used in the field? As noted in part one, 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, I recommend this ebook. It’s excellent!

To answer that question requires understanding what “you can learn from a quick look at the graph. First, overall width of the histogram gives you some information about the contrast and dynamic range of your image…A low-contrast image will have a narrow histogram. That means a narrow dynamic range as well. Conversely, a broader dynamic range will results in a wider histogram – and an image with greater overall contrast.” This same concept applies to the three different areas of a histogram: shadows, highlights and mid-tones.

A quick aside: “Dynamic range” is another term that is used a lot in photography, but is often not defined very well. According to Sean McHugh, who pens the Cambridge in Color blog (another excellent resource), “Dynamic range in photography describes the ratio between the maximum and minimum measurable light intensities (white and black, respectively)…[In other words, in] a real-world scene [dynamic range] is simply the ratio between lightest and darkest regions (contrast ratio).” That’s not to say the concept of dynamic range is a one size fits all.  Printers, scanners and digital cameras have different dynamic ranges, as do different models within those groups. For an in-depth discussion of dynamic range and how it applies to digital photography, see Sean’s post Dynamic Range in Digital Photography

Back to histograms. With respect to exposure, “over exposure shifts the histogram to the right, and under exposure shifts it to the left.” Another indication of over or under exposure is if the bars bunch up or spike against the walls of the histogram. As noted in part one, this means there is a loss of detail in the shadows or blown-out highlights.

Remember that no one histogram is correct. “The idea is to match the shape and width of the histogram to the scene.” This is particularly useful in the field, because “on many cameras, the brightness of the LCD monitor on the back of your camera is affected by ambient light…[so] it may not accurately represent the true exposure of your photograph. The histogram provides much more accurate information.”

Consider the histogram below:

Histograms Image 12 RGB

There is a peak in the area straddling the mid-tonal and shadow areas (left side) and a flatter area from the mid-tonal to the highlights areas (right side). The peaked and flatter areas are fairly wide, which indicates good contrast. You might think, however, that this image may be a bit underexposed, as it’s shifted a bit to the left, or has only one area with a lot of detail and rest may be boring or muted. In fact, you would be correct, except for the boring part, of course (wink, wink).

Histograms Image 12

This image was taken on a cloudy day against a backdrop of the club field and trees, which are a bit monochromatic, but this was done on purpose so the puppy (as the focal point) stands out from the background. In the histogram, the background is shown as a consistent number of pixels stretching from the mid-tonal to highlights area (right side) and also to the shadows area (left side). The bars are not very tall, indicating there is not a lot of detail in these areas. The peak is the puppy. More pixels equate to significantly more detail in that portion of the image. The puppy is not brightly lit, so the peak is just left of center in this histogram.

The image below shows a similar histogram shape, but reversed. In this histogram, the background is shown as a consistent number of pixels stretching from the mid-tonal to shadows area (left side) and a little bit to the highlights area (right side). The bars are not very tall, indicating there is not a great deal of detail in these areas. The peak, which is just to right of center, is the black dog carrying the dumb bell. Again, more pixels equate to significantly more detail in that portion of the image.

Histograms Image 10 RGB

Histograms Image 10

Unlike the previous image, this one was taken on a bright, sunny day and shows a black dog happily retrieving the dumb bell. There is more reflective light, which shifts the histogram to the right just a bit. Contrast is good, as evidence by the width of the peaks and the entire histogram.

So next time you are out shooting, take a look at the histogram in camera, and use it as a guide to adjust your settings to get the image you’re after. Part three of this series will feature 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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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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Yes, I did promise the next post would be about histograms, and it will. But, first … my favorite shots from last weekend’s USCA Southeast Regional Championship. Conditions were not great for photos, though the overcast did provide for even lighting. Most of the images were shot in aperture priority at ISO 3200, f/5.6 with the camera choosing the shutter speed. Camera was Canon EOS 5D Mark iii, with EF 70-300 mm f/4.5-5.6 DO IS USM lens. My goals were to shoot a variety of images, including some angles I had not gotten before, as well as some of the people at the event.  Hope to have the post on histograms up this week.  Until then, enjoy the highlights from the SE Regionals and happy shooting!

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