I am planning an experiment in which participants will provide perceptual evaluations of images of streets at night. I would like to ensure as best as possible that the photos simulate the range of luminances and spectra that would be experienced by someone viewing the same streets at night in the real world. I am particularly interested in ensuring the photos reflect differences in the street lighting used on the different streets. For example, if one street has an average luminance under the street lighting that is twice that of another street, how can I ensure the photos of these streets also reflect a similar difference in luminance magnitude?
I am hoping there are image post-processing methods that may be useful in achieving what I want.
Thanks!
Jim Uttley
Hi,
A- The scope of the answers: Your questions are interesting. I will not only focus on your detailed questions, but also wider thema, so that everyone will understand futhermore about this direction.
B- A bit in details: In your title, there are two issues (1) Color and (2) Luminance. And in the final question, you only want to ask about (2) luminance.
- The first issue correlates to the thema "Color Management". Herewith, you always face with the problem about the color difference between the original colors of the objects and the colors of the ones in Fotos and Videos. This issue is not easy. You must correct the color matching functions x-y-z or more correctly R-G-B curves of your CCD-Sensors of your Camera and then you must understand and characterize your monitor or printer where you want to rebuild the colors of the objects in your applications. So, it is quite complicated to combine, correct and optimize for all of them. x-y-z or R-G-B and matrizing or correction are alway not correct and dependent on many factors.
- The second issue correlates the thema "luminance" and the human perception of luminance. Technically, luminance is only one small aspect of the above mentioned thema. Herewith, you must face the problem of V-Lamda or y-Curve. At oneside, V_Lamda or y-Curve is not correct when the hardware (sensors, optics) in your camera performs their roles. In the other side, humanly V_Lamda or y-Curve is not absolutely correct with the human perception and dependent on many things (adaptation, personal...etc).
C- The-State-of-the-Art Device is Technoteam Camera (http://www.technoteam.de/product_overview/lmk/products/lmk_5/index_eng.html). The device can help you to solve your problem. For me, it is a very good device.
D- Conclusion: However, like the above mentions, the problems still exist for the device. You only can solve them absolutely, when you can understand, characterize and optimize for all of the problems what are mentioned in B)
Finally, I hope that I can help you partially.
Best regards
Vinh
Hello Jim,
I am not going to focus (pun intended) on the technical like the answer above, but as a shooter, I suggest that no matter what your desired outcome, you must also capture the images you want in equal fashion. You should use the same camera for each comparison video or stills, keep a close record of the gain settings, the f-stop used and ISO setting if for a still image- digital or film and use the same camera settings at each location!.
That way, at least for your capturing work, what you record will reflect with equal accuracy what the camera saw at each spot. I suggest that you do some initial testing in the brighter locations you plan to study, so that your exposure can be set there- to be used without overexposing- before going to the more poorly-lit locations. "Auto iris" camera settings will provide the best possible image results- as determined by the camera- but will not give you the impartial equality it looks like you want for your project. Don't use auto iris.
Happy Shooting,
Dave
Thanks all for the responses, much appreciated. They have given plenty to consider, I think I have a lot of reading to do.
I have not dealt with this issue directly, but the following technical papers may be useful (I attended conferences where the recent ones were presented).
http://papers.sae.org/2017-01-1368/
http://papers.sae.org/2016-01-1463/
https://doi.org/10.1177/147715353600100202
There is also a useful and interesting, albeit brief, discussion of issues related to the photography of street lighting installations by D. Schreuder in the book "Public Lighting" edited by J. De Boer, 1967, Philips Technical Library, in Chapter 8, "Measurements."
Please note that most of these papers deal with the luminance distribution rather than the spectral distribution. I would be very cautious about being able to reproduce in a meaningful way the spectral distributions of colored light sources in the nighttime environment.
The comments above regarding maintaining constant camera/lens settings are almost certainly crucial to any success.
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