Part 1 of our series on how to understand digital imaging fundamentals
Welcome to this series of digestible bite-size articles on the technical fundamentals of digital imaging. These articles start at the very basics and gradually introduce more advanced topics. By following the series in stages the aim is to reinforce what you have learned while exploring new areas in an easy to understand context. There will be lots of example images to illustrate points and concepts.
We're aiming at almost anyone interested in digital photography, from beginners to more advanced photographers, who want to steadily improve their technical understanding. To start with, some of you will already know most of the concepts being covered, but as the series progresses you may start to discover useful information that is new to you.
We start the series with an introduction to the rather forbidding technical term; 'dynamic range'. To start with we will explore the fundamental building blocks of image tonality. We have lots of example images to to show you how one scene can vary widely by adjusting image parameters. In future parts we will look at the exposure histogram, curves, and colour channels. But don't worry about those for now!
Above is a scene of part of Windsor Castle taken on a sunny early summer day. This is the original camera JPEG image, reproduced without any adjustments.
By the end of this series you will be able to understand the differences in images like the examples in this article, how to correct problems and how to enhance your images to make them look more impressive.
It's all about capturing and recording light
We will look at the specifics of how digital camera sensors work in a future series. But put simply, the camera's sensor records the brightness of light falling on millions of microscopic light sensitive photosites across the sensor area. These brightness values are processed by the camera and eventually become image pixels. You may be surprised to learn that a digital camera's image sensor is actual not a digital device; its data is only converted to digital when it is read off the sensor chip and converted to digital via an analogue to digital converter.
In digital form, means they will each have a value between zero (black) and maximum value (white), with everything in between in intermediate brightness levels or tones. For simplicity, this time we will work in 'luminance' or brightness values and introduce the element of colour in a future installment.
Let's assume the pixels have 8-bit values. This means they can have any one of 256 number values, or levels, of between 0 and 255. For a perfectly smooth transition from 100% black to 100% white, neighbouring pixels will increase in value by one level at a time. This is also described as a smooth tonal gradation.
Above, in section 'A' of the diagram, you can see a smooth and consistent transition from total darkness to complete brightness. In 'B', the transition is compressed primarily into the centre of the range, although the number of steps in the transition is the same. Plainly, an image that has the characteristics of the tonality of 'B' will look very different to one with the tonality of 'A'.
Here, in C, we have artificially separated the steps so you can see them individually. Stepping this visible would also be representative of severely limited gradation.
Comparing common image faults
Below you can see two columns with the left side showing different simulated renditions of the normal, untouched, picture on the right.
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Lost highlights
See above that the clouds have have lost their structure and some of the brighter areas of the stonework have lost definition, or become burned out. Darker areas look normal, though. Here, the sensitivity of the camera has been limited to the areas of mid and low brightness, leaving insufficient gradation in the bright regions of the image. The image is not over exposed, though, as the mid tones look normal. Some cameras, often simple and inexpensive ones, like camera phones, produce results like this because their tiny image sensors don't have enough tonal latitude to record bright highlights properly. These lost highlights are said to have been 'clipped'.
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Lost shadow detail
Now, we have the opposite of the first example. This time the darker areas have lost their detail and have become shaded out. The image appears darker, although the overall brightness has not been adjusted. If you examine the clouds, they are the same brightness as the original image on the right. If your camera produced an image like this, it would be because the camera's sensor was not sensitive enough to record the darker end of the tonal range. As the dark details have been lost, we would call this shadow clipping.
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Both shadows and highlights clipped
Now (above) we have the combination of burned out highlights, and blocked out shadows. If a camera produced this result, its failing would be an inability to cope with the brightest and darkest areas of the scene. The dynamic range of the image is wider than the sensitivity of the sensor, resulting the shadows and the highlights being lost, or 'clipped. This also has the side effect of increasing the contrast of the image.
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Compressed tones
See if you can spot any true black or true white tones in this version of the image. Actually, there aren't any. The image is constructed of tones that entirely in the middle of the range. There is no brightness and no real darkness, hence the grey look. Instead of the tonality of this image spanning the dark to bright limits of the tonal range, it is compressed into the middle. This image is not clipped because neither the bright or dark end of the image tonal range has been lost, though it may have lost some gradation because it occupies less of the tonal range than it could. Later on in the series we will see how this fault can be remedied quite effectively and easily.
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High Dynamic Range (HDR)
To finish off, this time the example does not show a fault, but instead an 'effect'. You may have heard of the term 'HDR' or High Dynamic Range, that describes a special effect which gives images a rather surreal and, some would say unrealistic, appearance. Above is such an example. The highlights have more definition (examine the clouds) and the shadows are more detailed. Such images also tend to have enhanced colour saturation. Software processing has either artificially boosted the dark and bright regions, or several images of the same scene, but with different exposures, have been aggregated into one new HDR image. HDR can be a useful way of getting normal looking pictures under conditions of extremes of lighting, or for artistic effect. Later in the series we'll be looking at how to make HDR images. |
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I hope that this article has, at the very least, left you with the fascination for how different one image can be made to appear.
Next time we'll start to introduce some basic tools for manipulating the image data in order to correct problems or help avoid experiencing them in the first place.
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Digital Photography 101 - How image quality can vary
DPNow Digital Photography 101 - How image quality can vary
Here is a summary or excerpt from an article that has just been published on DPNow:
If you are inte... (more)
Bob Ross Re: Digital Photography 101 - How image quality can vary
Hi Ian,
Very good idea and your first segment is well layed out. Given the number of facets to digi... (more)
Stephen Re: Digital Photography 101 - How image quality can vary
I'd very much agree with Bob about your article Ian and I too look forward to the next one. The pho... (more)
Ian Re: Digital Photography 101 - How image quality can vary
I felt including a histogram would have been overload for some of the target audience, but that wi... (more)
Graham_of_Rainham Re: Digital Photography 101 - How image quality can vary
I am often asked to explain about elements of image quality, such as these, and this shows exactly t... (more)
coupekid Re: Digital Photography 101 - How image quality can vary
Look forward to it Ian, it made interesting reading! :)... (more)
Ian Re: Digital Photography 101 - How image quality can vary
I'm working on it now :)
Ian... (more)
bouffie Re: Digital Photography 101 - How image quality can vary
Thanks for clearly explaining to a novice exactly what people are talking about when they throw thes... (more)
Ian Re: Digital Photography 101 - How image quality can vary
Part 2 is now up - intgroducing the exposure histogram - don't be intimidated by what is a very usef... (more)
