Category Archives: Part 2 – Digital image qualities

Exercise: Camera’s Dynamic Range (DPP)

This exercise was about finding the dynamic range of my camera (Nikon D5500)

A cameras dynamic range is the difference between the lightest light and darkest dark which can be seen in a photo. Once the subject exceeds the camera’s dynamic range, the highlights wash out to white, or the darks become black blobs (noise).

I had to find a high dynamic range scene with the following conditions:

  • bright sunlight
  • at least one brightly reflecting surface
  • an area of deep shadow with a dark surface

I must admit i’ve been fairly stumped by this exercise. I noticed that many student blogs were missing this exercise. Having researched online extensively and having studied other student blogs and discussed in forums I think i’m somewhat clearer. Some articles adjust aperture while others adjust shutter speed. I’ve decided to crack on with it and not waste any more time deliberating over it.

In a nutshell – using a fixed ISO and aperture I am going to measure the difference in shutter speeds of different areas of brightness in one image. Spot metering will help me identify the correct shutter speed. The range between these results will be my camera’s dynamic range.

Firstly I had to set the exposure and make sure the it was just at the point where there were no longer any highlight clipping warnings on the reflecting surface (white wall).  The exposure settings where the highlight clipping had just disappeared  was 1/200, f.22 and ISO 400.


Next I had to measure and make note of the brightness of the white wall and two or three of the darkest shadows.  I used the spot metering setting as this enables me to measure specific areas.  I measured the white wall at 1/640, f.22, ISO 400.  I then measured 2 dark shadow areas (the bricked pavement area and the dark shadow by the palm and wall) as 1/60, f.22, ISO 400 and 1/30, f.22, ISO 400 retrospectively.

With numbers

Part 2 required me to open the image to 100% in my software and check that the white area’s pixel values showed just under 255 on all three channels. They measure at 249 on all 3.

I was the required to zoom into the shaded area, brightening it with my software and comparing the areas of real detail against noise.

Screen Shot 2016-03-20 at 12.06.49

As you can see from the above image it is now extremely difficult to tell the difference between detail and noise. The darkest areas are extremely hard to determine.

SO now i’ve got my shutter seed variations I need to calculate the dynamic range. The OCA handbook states that most SLR’s have a range of roughly 9-10 Evs.

The Nikon i’m using has a total range of 1/4000 to 30secs, but i’m just going to look at the values i’m working with:















I seem to have produced a dynamic range of 13 Ev’s – this seems a little excessive considering the average is 9-10. However, after researching the average dynamic range of my model I came across this website that mentions the Nikon D5500 having a range of 14 EV’s.

Therefore i’m happy with my results, and have learnt how important this feature is when assessing shots. It’s essential while maintaining a technically satisfactory image, but is also a great creative tool for producing silhouettes, and generally making photos more art like and aesthetic.






Exercise: Colour Cast and White Balance (DPP)

For this exercise we are to alter the white balance on several different scenes.

I felt like this exercise was very close to one from a previous module:

While the exercise above dealt with tungsten and fluorescent lighting only – I feel like I am already very aware of the impact of altering white balance.

As this exercise has called for sunlight AND open shade on a sunny day I’ve decided to move on as It’s extremely dark and miserable outside and I don’t want this exercise to slow me down more.

I incorporate white balance settings in all my photography and am very aware of it’s impact.

Exercise: Highlight Clipping (DPP)

For this exercise I have to find a scene with a wide range of brightness and shoot in manual mode. I must find the setting where the highlight clipping warning starts to appear and make a note of the setting. I then have to increase the exposure by 1 F stop using either aperture of shutter speed.

Then I must take 3 more shots decreasing the exposure each time by 1 F stop.

I have to process the images  without making any adjustments and then observe a magnified area of the highlights; making notes on:

  • Completely lost areas of visual information
  • A visible break in the form of an edge between nearly-white and total white
  • A colour cast along a fringe bordering the clipping white highlight
  • The colour saturation


F. 5.6 ISO 200 0.3/sec

At these settings the highlight clipping warning is now flashing on the white part of the wall between the guitar neck and black wall.


F.4.5 ISO 200 0.3/sec

Now at a higher exposure the highlight warning practically covers the whole of the white wall and a few characters on the black chalk wall. The colour saturation is off, the guitar demonstrates this well. The white wall has a large area of lost visual information.


F.7.1 ISO 200 0.3/sec

Image three shows no highlight clipping warnings. There is no loss of visual information in the highlight areas and no breaks between areas of nearly white and total white. The colours (mainly the guitar and calendar) become more saturated with each decrease in exposure.


F.9 ISO 200 0.3/sec

Image four is now starting to appear too dark and under exposed. The colours of the text on the chalk wall are becoming less visible.


F.11 ISO 200 0.3/sec

Image five is now far too dark compared to the original image (F.5.6) There’s not one part that is correctly exposed. I don’t think that it is so dark that it would be rendered useless, but compared to the first image it is severely under exposed.


I wonder if perhaps I would have got slightly different/stronger results had I used a scene with more variety. The shot is dominated by white, which may have hindered my results slightly. Had I used a smaller area of white, possibly repositioned in the shot (i.e not covering a large space) and not contrasted with black, but some other colours this exercise may have worked out differently.

However, it’s all about trial and error and I am aware of the importance of highlight clipping and it’s effects after finishing this exercise.

Exercise: Tolerance for noise (DPP)

For this exercise I have to take a series of identical photographs indoors in daylight of a scene that included some sharp detail and a textureless area like a white wall with some of the textureless area in shadow.

I had to set the camera on a tripod, use aperture priority setting to keep the depth of field consistent and take a series of images covering the whole range of ISO settings on my camera.

My Nikon D5500 ISO range is as follows:

100, 125, 160, 200, 250, 320, 400, 500, 640, 800, 1000, 1250, 1600, 2000, 2500, 3200, 4000, 5000, 6400, 8000, 10000, 12800, 16000, 20000 and 25600.

I’ve chosen to upload several pictures throughout the sequence, rather than the whole sequence.


ISO 100

At the lowest ISO there is no noise whatsoever.


ISO 500

At 500 the noise levels are still hardly present at all.


ISO 2000

At 2000 the noise is starting to become evident.


ISO 8000

At 8000 the noise is now extremely noticeable, especially in the shadowed area to the left of the vase.


ISO 16000

At 16000 the image is covered with noise, making it appear to be of a much lower quality.


ISO 25600

Finally at 25600 it’s totally speckled with noise, really reducing the overall quality. I would not want to submit a picture with this much noise.


I’ve always been aware of the results of using a high ISO but this exercise really did enforce the importance of using the lowest possible, and the unwanted effects of using a high ISO. For this exercise I think anything over ISO 800 starts to show an unattractive amount of noise.

Exercise: Sensor Linear Capture (DPP)

Objective: To simulate a linear image by applying curves, replicating an image before in-camera processing.

A linear image represents an image before the camera has applied it’s in camera processing. Almost all digital cameras apply some processing. Film however, does not. The linear image appears much darker. A camera sensor’s response is called ‘linear’. The more light that falls on the sensor the stronger the response but at exactly the same rate from dark to very bright.  This is different to how our eyes respond and how film responds.  Our eyes cope with a wide range of brightness by compressing the light so that really bright seems less than it is.  Film responds in a similar way but to a lesser extent.

Gamma correction is performed in the camera to produce the kind of image we expect to see.  If this processing didn’t happen the image would look very dark and the histogram would show most of the tonal values to the left side.  A typical gamma correction curve makes an image brighter.

Original image:


Original image, converted to 16 bits per channel, curve applied:

Linear Image

As you can see, applying the curve has resulted in a much darker image.

Original histogram:

Screen Shot 2016-02-21 at 17.36.16

As you can see from the above histogram – the levels are roughly based in the middle.

Linear histogram:

Screen Shot 2016-02-21 at 17.36.29

The liner’s histogram is grouped to the far left, something that we expect to see with a darkened image.

Liner image with curve applied (to re-create original image):

Linear Image 2

Noise comparison:

Screen Shot 2016-02-21 at 18.19.45

There is some noise already, but if you compare it to the below image (linear image with curve applied) you can see that the noise distortion is slightly greater in the corrected image.

Screen Shot 2016-02-21 at 18.19.54


This exercise has shown me that lightening an image to simulate in-camera processing exaggerates the noise levels, especially the noise in the shadowed areas. It has high lightened the importance of making sure my exposure is correct through camera settings rather than relying heavily on post processing software.