PROJECT: HISTOGRAMS

Hmmmmm…..

Not as easy as I thought it would be, but let me have a go at explaining Histograms.

“A histogram can appear either as a graph or as a bar chart, and shows the distribution of grays in the middle, to white at the right” Freeman (2008 p.16).  Freeman also goes on to state that, “At first glance this may seem unnecessarily technical and geek-like, but, once you have become accustomed to reading a histogram, you can interpret it at a glace”.

A very good quote to remember surrounding histogram’s ‘white at the right’.

One thing that has been missed in this statement, and I do feel the need to include it, is that black tones appear to the left of the screen.  There you go, as easy as that – next!!!

Cambridge In Colour states that, “Understanding image histograms is probably the single most important concept to become familiar with when working with pictures from a digital camera.  A histogram can tell you whether or not your image has been properly exposed, whether the light is harsh or flat, and what adjustments will work best.”

That all sounds easy enough, but digging in further, there is actually a lot more to be taken into consideration when understanding how your camera represents your photograph as a graph (or histogram).

As we know (or should know by now), an image is made up of pixels and each of these pixels contains a combination of our primary colours (RGB or Red, Green and Blue).  Way back during our TAOP Colour exercises, we discussed that each of these colours has a brightness value that ranges from 0 to 255 with a bit depth of 8-bits.  The creation of a histogram happens when our camera, or computer, values the brightness of these colours and counts how many pixels there are at each given level (0 through to 225) so, the more pixels present in any one tone, the higher the peak will be represented on your histogram.  Therefore, a histogram with lots of lighter tones will be skewed to the right and in contrast more darker pixels will make the graph skew to the left.  This sounds confusing, but it is actually quiet logical – when you know how.

Initially, I thought when reading a histogram that its shape represented the shape of the image, and could never quiet make out how the author of the article/book came up with their logic and explanation of how they interpreted their histogram, but I have just read an article that has shone light on the whole affair and the penny has finally dropped – I understand it now.

“You are probably making the mistake (that most make) of trying to see some kind of shape that equates to the structure (shape) of your shot. All the histogram shows is a graphical representation of the occurrence and spread of something – in this case light tones on a tonal range between pure black (the left side) and pure white (the right side) and everything (if it’s there) in between.” (Carregwen 2011).

The full forum thread for this article can be read at the following link:

<http://www.cambridgeincolour.com/forums/thread7061.htm&gt;.

The image below represents what is though to be the perfect histogram and breaks the whole process down into easily understandable segments.

Histogram

Histogram

Cambridge In Colour.  (n.d.) Histogram [Online Image].  Available at: <http://www.cambridgeincolour.com/tutorials/histograms1.htm&gt; [Accessed 4 February 2013].

Of course, as with everything photography, there is never a ‘perfect’ anything as we all interpret things differently, and there will be some instances where an image that is not represented perfectly by a histogram i.e., leaning more in either direction, can be seen as perfect in the eye of the photographer.

It should be noted here that when reading a histogram within your camera, the camera is reading the data as a compressed JPEG conversion of your image as it would take too long for the camera to process the RAW data into a histogram, so when viewing the same data on a computer, in RAW format there may be a slight difference in the information you.

Tonal Range

As we can see in the diagram above, our histogram is represented predominantly by mid-tones, which would convey that our image was perfectly exposed, hence the production of an ‘ideal histogram’, and it can be useful to understand how pixel numbers map brightness values to our graph, as this in turn will assist us in understanding the tonal range within a scene, even before pressing the shutter release.

High and Low Key Images

“Images where most of the tones occur in the shadows can be called ‘low key’ images, whereas with ‘high key’ images, most of the tones occur in the highlights” (Cambridge In Colour).

I think it is important to touch on ‘High-key’ and ‘Low-key’ images, as shooting these kinds of images can have an impact on how your camera interprets a histogram.

A low-key image would be one taken under darker circumstances, such as in a darkened alleyway or of a building in shadow.  Here, the tones of the subject(s) occur predominantly darker.

South Bank, London ~ Julie Harding

South Bank, London ~ Julie Harding

This would be represented by a histogram that is skewed mostly to the left.

Whereas a high-key image would be predominantly light, with most of the tonal range in highlights.

Ice Hotel, Sweden ~ Julie Harding

Ice Hotel, Sweden ~ Julie Harding

And this would be represented by a histogram that is skewed mostly to the right.

Cameras measure light as reflected as opposed to incident, so they are unable to ascertain the absolute brightness of your subject, so it is good to remember that they tend to record everything as mid-tone.  Therefore, a good habit to adopt is to manually adjust exposure (by checking your histogram) to achieve either a brighter or darker image as required.

It should also be noted that post-processing software is better equipped at recovering images that have been underexposed as high-key scenes, where the image is overexposed or ‘blown’ and may have solid white areas within the frame are not recoverable.  This result can also be referred to as ‘clipping’.

A Scenes Dynamic Range

Not to be confused with the growing photographic trend within digital photography known as HDR, the Dynamic Range of a scene is the product of two things, Freeman (2009 p. 38) refers to these products as; the light falling onto a scene (its luminance) and the light reflected from the different surfaces within that scene (its reflectance), however, light is the most important element here.

As we know, all light has a different temperature, which is solely dependent on source, so a naked light source offers more illumination than a source, which has been diffused.  We can therefore expect the dynamic range of our camera to be high under intense sunlight, where the sun’s rays are able to penetrate into the smallest, darkest nooks and crannies; whereas the camera’s dynamic range will be lower if we were shooting where there was cloud cover or the air was polluted. Spot lights, studio lights or street lamps also offer us a higher dynamic range, which can also assist in the creation of 3D effects as the higher the range, the deeper the shadows it produces.  Another high dynamic range scenario could be longer exposed images.  Another factor to consider here is your choice of shooting method, as shooting towards the light, or including it within the frame will produce a higher dynamic range than shooting away from the source or diffusing the light in some way.

This is all well and good, but our results would finally be dependent on the dynamic range within our camera and if, when shooting a scene you have a 10-stop range, then it is reasonable to class the camera you are using as having a normal dynamic range.

Below is a table of Freeman’s thoughts behind the contributions to dynamic range, and he also adds;

Freeman's Thoughts Surrounding Dynamic Range

Freeman’s Thoughts Surrounding Dynamic Range

“True high dynamic range needs HDRI techniques, while medium-high dynamic range (perhaps two or three stops beyond the camera’s ability) can be dealt with using less specialized method’s”

Freeman (2009 p. 38).

Source:

Reference:

Carregwen.  (2011) Re: Histograms :-s [Online Article/Forum].  Available at: <http://www.cambridgeincolour.com/forums/thread7061.htm&gt; [Accessed 4 February 2013].

Cambridge in Colour.  (n.d.) Camera Histograms: Tones & Contrast  [Online Article].  Available at: <http://www.cambridgeincolour.com/tutorials/histograms1.htm&gt; [Accessed 4 February 2013].

Cambridge In Colour.  (n.d.) Histogram [Online Image].  Available at: <http://www.cambridgeincolour.com/tutorials/histograms1.htm&gt; [Accessed 4 February 2013].

Freeman, M.  (2008) Mastering Digital Photography.  East Sussex: The Ilex Press Limited.

Freeman, M.  (2009) Perfect Exposure.  East Sussex: The Ilex Press Limited.

Bibliography:

Cambridge in Colour.  (n.d.) Camera Histograms: Tones & Contrast  [Online Article].  Available at: <http://www.cambridgeincolour.com/tutorials/histograms1.htm&gt; [Accessed 4 February 2013].

Freeman, M.  (2008) Mastering Digital Photography.  East Sussex: The Ilex Press Limited.

Steinmueller, U., Gulbins, J.  (2010) The Digital Photography Workflow Handbook – From Import to Output.  Heidelberg: Steinmueller Photo.

Rowse, D.  (n.d.) Understanding Histograms [Online Article].  Available at: <http://digital-photography-school.com/understanding-histograms&gt; [Accessed 4 February 2013].

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