Digital Photography For Beginners: Histograms

In this Photography Tutorial we are going to look at the role of Histograms in Digital Photography.

What is a histogram?
One of the revolutionary steps in the history of digital photography is being able to calculate and display an image’s histogram.
A graphic bar showing the tonal distribution in an image, the histogram sorts and reveals the different luminosities of pixels distributed on a scale of brightness, from 0 to 255, or from black (left) to white (right), with 254 different shades of gray in between. The height of each bar corresponds to the number of pixels of the same shade.
Why should you care about the technical details? Because understanding the way a histogram works allows you to review all your shots, check out exposure and whether some details have been lost in shadows or burnt in too much light.

If you open your Adobe Photoshop and select Window->Histogram or Image -> Histogram (depending of the soft version you use), you’ll see your image’s histogram and be able to clear out any luminosity and/or color problems.

Whilst the color histogram is most helpful to any pro, as home user you will get plenty of reliable information that covers all the photo basics you need from the luminosity/image histogram that we’ll analyze further.
When do we have a problem?
 

When do we have a problem?

Although the answer to this question might seem obvious, there is no actual recipe that guarantees a perfect image if you have a perfect histogram. The rule of thumb says that a correctly exposed image is observed in a histogram with smooth, gradually ascending and descending curves towards its extreme limits (0 and 255), as is the example on the left. Does this automatically exclude all the other possible histograms for being bad ones? Technically, yes, but don’t go delete half of your images just yet.
Technical precision isn’t always satisfactory, nor is it mandatory, but understanding it will help you overcome common issues and will prove useful knowledge when you want to break the rules and capture different, unique perspectives through your photos.

How do we interpret histograms?

  

With many pixels quite close to 0 value indicating plenty of dark areas, some of the details in the shadows are lost forever. There are also few to none pixels in the highlight area (255), turning the histogram above into a classic proof of an underexposed image.

 

In this case, the large quantity of pixels close to or with value 255 are a clear sign of overexposure. It’s not hard to notice that some sections of the image seem to be burnt and subtle highlight details are completely gone and lost.

 

 

Midtones and few to no contrasts result in a hazy image and a histogram like the one above.
 

 

And there’s always the combed histogram, a special example that only appears when image post processing has been done in either Photoshop or some other similar soft. Why does this happen? Because playing with Levels or Curves (two settings available in all post processing programs) involves contrast adjustments that cause a redistribution of tones with tone losses in the process, determining a comb-like histogram, hence the name.

And back to perfect histograms
The great thing is that most of the modern DSLRs have a histogram option available in their settings, allowing you to snap judge the quality of a shot on the spot and take another one if necessary.
Although an accurate exposure – underlined by a smooth histogram – confirms that your camera captured all details from both highlights and shadows, remember that people don’t care about pixels and tab graphs. They are impressed by the originality and authenticity they find in your images.
So learning the language of histograms will probably be enough to master perfect exposures, but learning when to say “No” to perfect exposures will get you one step closer to mastering photography.

Words & Pictures Adela Trofin

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