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Posts Tagged ‘color histogram’

I attended a very interesting lecture recently on color theory and color contrast in photography. It got me thinking about how to use color and color contrast in IPO photography. Yes, I know, most all IPO photographers shoot in color, so you may be asking yourself, what’s the big deal? Well, as with many elements in an image’s composition, color plays an important part in telling the image’s story as it evokes an emotional response and draws attention to or away from particular elements. Used wisely, color enhances images and the viewer’s experience.

Before considering colors in typical IPO settings and how to effectively use color in IPO photography, a quick review of color theory will be beneficial.

We perceive color from reflective, absorptive or transmitted light. In other words, the color of an object depends on how much of particular wavelengths of light are absorbed or reflected.  For example, an apple appears red, because it absorbs all of the other colors and reflects red wavelengths of light.  A black object absorbs all the wavelengths of light. Solid object reflect light, while transparent objects will transmit light through them.

This visible light spectrum correlates to a wavelength range of 400 – 700 nanometers (nm) and a color range of violet through red. The visible colors from shortest to longest wavelength are violet, blue, green, yellow, orange and red.  For more information, see the following articles:

With this in mind, go back to when you first learned about color as a child. Recall, that a traditional color wheel used by artists has 12 colors, as shown below, which can be divided into three categories:

  • Primary – Red, blue and yellow, which make up all other colors.
  • Secondary – Orange, green and violet, which result from mixing primary colors.
  • Tertiary – Red-violet, red-orange, yellow-orange, yellow-green, blue-green and blue-violet, which result from mixing primary and secondary colors.

Color Wheel 1 - Shutterstock

Each color is called a hue. Color Value refers to a color’s lightness or darkness.  Within each color value are tints and shades. Tints refer to a color where white is added to lighten it, such as pink (red + white), and shades refer to a color where black is added to darken it, such as brown (orange + black).  See the color wheel below.

Traditional Artists Color Wheel 2

Color directly across from each other on the color wheel are called complementary colors; for example, red and green, blue and orange, and yellow and violet. These colors can compliment each other or contrast with each other. There is no hard and fast rule about whether or not a particular color combination is pleasing.

But wait! There’s a twist when it comes to photography! Artists mix pigment, but photographers mix light, and light uses a set of different primary colors. As explained in the Franklin Institute’s helpful article:

“The primary colors of light are red, blue and green, and the secondary are yellow, cyan and magenta … Red and green paint, for example, make brown paint, but red and green light makes yellow light … When beams of light are mixed without any absorption, an additive process occurs. The more we mix the beams, the closer they get to white light.”

These primary colors form the basis of the “Additive Color (RGB) Model, named because black is the base and light is ‘added’ to eventually get to white, which is all of the colors together. Additive colors are seen in televisions, nature and computer screens.” Our retinas also are sensitive to these same primary colors. “Just as any color of the [light] spectrum can be made by mixing the three primary colors, so do our own eyes discern the various colors by sensing different wavelengths with these three receptors.”

With this in mind, a photographer’s RGB color wheel might look like this:

RGB color wheel

One more bit of theory before moving on. “Saturation is how intense colors appear. Over saturation of color can result in loss of detail or clipping. Vibrance is a smart-tool used in photo editing software that increases the intensity of the more muted colors and leaves the already well-saturated colors as they are.” For more about the hue, color value, saturation and vibrance, see the following articles:

Without getting too much into the weeds of color management for image files (a topic for another day) and color theory, what’s key here is red, blue and green light are not only the primary colors in nature (the outdoors being the most frequent location of IPO photography), but they also are the primary colors we use to see and to create color in televisions and computer monitors, the most frequent viewing medium for IPO photographs. So, it makes sense to think of color in photography for the purposes of image composition in this context.

Part 2 of this series will discuss what colors may symbolize and how we perceive them on an emotional level. Part 3 will wrap up this series with examples and tips on how to effectively use color in IPO photography.  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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