The methodology is simple, repeatable, and I think useful. All necessary data is displayed on the camera LCD, namely image brightness (%) and f/stop. The measurement set up requires a uniform constant light source large enough to fill the FOV, with the camera locked down. I used the lens wide to ensure max effective aperture, but the f/stops are relative anyway. Starting with the f/stop that gives 109% I closed down in half stops recording brightness at each change in f/stop reading. The change in f/stop reading is finely repeatable and I took that point (closing) as being the actual aperture value. The process is equivalent to decreasing the test subject's brightness in half stop increments, and because it is all relative it is unimportant whether the f/number is the true value, provided the increments are accurate (which they might not be towards the high f/stop end). I used ND filters to work aperture over a limited range. Each gamma was set to its default setting, gain to 0dB, daylight WB.

In plotting the results the geometric value of the f/number was used in place of the rounded figure which is displayed on the LCD. Any small irregularities in the plotted curves are related to experimental error.

Using this method you can quantitatively determine the effect of changes to the default settings, such as knee (on the standard gamma curves), black gamma, etc.

 The default curves for Standard and Cinegamma are here plotted in linear and log form.The final figure (5) shows the type of influence that gamma level setting has on the cinegamma #4 curve.

 

 

 

 APPENDIX: understanding these plots.

 

It has been said that most people don't understand technical stuff. I doubt that is true, but if any of the above is not clear then perhaps the following will assist.

 

Using images to show the effect of a changing a parameter is limited because one image is just one example. A set of images taken to show the effects of "twiddling the knobs" are most meaningful to the person who took them, which is why anyone who shoots video (and film) for a living always do their own tests. Yes, they start out with guidance from others, but rather than using that guidance as a recipe they run tests for the "look" they want for their project. Really understanding how an image interprets a scene requires knowledge of the original scene, which we can't get unless we were present. Describing the scene is inadequate (requires the old "1000 words"), which is why technical tests use standardised cards. People have tried to communicate the effects of different gamma curves by subjective terms such as "the brighter gamma", which really tell us very little.

Graphs show us how the camera translates subject brightness into image brightness, and importantly how camera adjustments affect that process. Alister used a graph to explain "knee", much more clearly than using just words and a photo.

The camera can record images of relative brightness that range from 0 to 109% -- black to "super" white --- I think everyone understands that. But how much light (subject brightness) is needed to get a specific image level depends on many things (aperture, filters, image processing algorithms). How the image is processed is influenced by our set picture profile, especially the selected gamma curve. In addition, the light coming into the camera is divided into RGB streams, each processed separately; best put that aside for the moment and think just of the total (white) light stream.

So, just as image brightness is given in relative terms ( 0 to 109%) so it is easiest to think of subject brightness in relative terms. I chose to make brightness (think: white card) relative to that which gave 109% when using the cinegamma curves. The curves are very flat at that point, and I might have chosen instead to plot subject brightness relative to that which gave 109% for a standard gamma (so then the curves would have shown subject relative brightness of 5.55 for 109% using cine 4). Or I could have chosen any other subject level --- the important thing is that all the curves are relative to the chosen "standard".

Looking at the curves you immediately see the way cinegamma handles highlight regions. Each halving of relative brightness (1.0 to 0.5, 0.5 to 0.25, etc) is a stop reduction in brightness (on our blank card), so we see that cine4 results in brighter mid-tone images than cine1 (for same settings otherwise). At the same time we see that STD1 will give a more fully modulated image (near saturated) at the same settings, and because the STD curve is steeper the image contrast will be greater; bigger change in image brightness for a given change in subject brightness.

Looking at these graphs we know, when we start shooting tests for our next project, that if we want to achieve contrast similar to the STD curves but need to use a cine curve because our subject has a wide brightness range (e.g. outdoors, white clouds, sunlit and shadow), then we will have to grade the image in post. If our subject is quite flat (small brightness range) then a STD curve might be preferable. In general, the cine gammas capture the most subject data (that is, within black level and white clipping) but require grading in post because otherwise the images look flat. If you want camera output direct to TV, then the STD gammas will be most pleasing (but may have white clipping and/or choked darks).

Going to the final graph, it shows that adjusting gamma level has a big influence on rendering highlights, but not much in mid-tones. And in other curves we see that log curves are more revealing of response at the darker (relative) range of the subject. Of course this set of curves on my blog is a limited one and there are many more parameters that can be plotted (black gamma, for example). And we haven't considered separately the RGB streams, where (for example) we might be concerned with how highlights are handled by each of them, or how skin tones can be distorted at higher zebra (image brightness) levels.

You get a lot of information from response curves and they make it a lot easier to understand what the available controls do. Of course someone has to generate the curves and in the same way that Kodak and Fuji always publish data for their film emulsions (response curves, modulation transfer function, etc) I think it a pity that video camera manufacturers don't do similarly.