It is extremely important to accurately depict photographs or video taken of a scene at night, when attempting to show how the subject scene appeared. It is widely understood that digital image sensors cannot capture the large dynamic range that can be seen by the human eye. Furthermore, todays commercially available printers, computer monitors, TV’s or other displays cannot reproduce the dynamic range that is captured by the digital cameras. Therefore, care must be taken when presenting a photograph or video while attempting to accurately depict a subject scene. However, there are many parameters that can be altered, while taking a photograph or video, to make a subject scene either too bright or too dark. Similarly, adjustments can be made to a printer or display to make the image appear either too bright or too dark. There have been several published papers and studies dealing with how to properly capture and calibrate photographs and video of a subject scene at night. Most of these approaches have used a qualitative approach. Some methods have used contrast boards or gradients and the individual taking the photograph or video records his/her observations of what can and cannot be seen on the gradient. Then the photograph or video is calibrated so that the image matches what the initial observer could see. One prior method calibrates a CRT monitor, DLP projector and printer to produce images with similar contrast detection. Again, this approach is qualitative.
This study presents a method for calibrating photographs and video, for use and display on printers, computer monitors, TV’s or other displays, with a quantitative method. This method removes potential interpretation bias and provides a scientific approach for determining if a photograph or video accurately depicts the contrast of the subject scene. This is accomplished by applying a similar approach to two different methods. The first method allows for a calibrated image of an object that cannot be seen and the second method allows for a calibrated image of an object that can be seen. In both methods, this is accomplished by measuring the contrast in a scene and adjusting the image for the appropriate contrast. Since there are no, previously published, methodologies for quantitatively determining if an image accurately represents a scene, this methodology is compared to previous, qualitative methods as well as Adrian’s Visibility model.