Computing dynamic range of high dynamic range (HDR) content is an important procedure when selecting the test material, designing and validating algorithms, or analyzing aesthetic attributes of HDR content. It can be computed on a pixel-based level, measured through subjective tests or predicted using a mathematical model. However, all these methods have certain limitations. This paper investigates whether dynamic range of modeled images with no semantic information, but with the same first order statistics as the original, natural content, is perceived the same as for the corresponding natural images. If so, it would be possible to improve the perceived dynamic range (PDR) predictor model by using additional objective metrics, more suitable for such synthetic content. Within the subjective study, three experiments were conducted with 43 participants. The results show significant correlation between the mean opinion scores for the two image groups. Nevertheless, natural images still seem to provide better cues for evaluation of PDR.
With the advent of computational photography, most cellphones include High Dynamic Range (HDR) modes or "apps" that capture and render high contrast scenes in-camera using techniques such as multiple exposures and subsequent "addition" of those exposures to render a properly exposed image. The results from different cameras vary. Testing the image quality of different cameras involves field-testing under dynamic lighting conditions that may involve moving objects. Such testing often becomes a cumbersome and time-consuming task. It would be more efficient to conduct such testing in a controlled, laboratory environment. This study investigates the feasibility of such testing. Natural exterior scenes, at day and night, some of which include "motion", were captured with a range of cellphone cameras using their native HDR modes. The luminance ratios of these scenes were accurately measured using various spectro-radiometers and luminance meters. Artificial scenes, which include characteristics of the natural exterior scenes and have similar luminance ratios, were created in a laboratory environment. These simulated scenes were captured using the same modes as the natural exterior scenes. A subjective image quality evaluation was conducted using some 20 observers to establish an observer preference scale separately for each scene. For each natural exterior scene, the correlation coefficients between its preference scale and the preference scale obtained for each laboratory scene were calculated, and the laboratory scene with the highest correlation was identified. It was determined that while it was difficult to accurately quantify the actual dynamic range of a natural exterior scene, especially at night, we could still simulate the luminance ratios of a wide range of natural exterior HDR scenes, from 266:1 to 15120:1, within a laboratory environment. Preliminary results of the subjective study indicated that reasonably good correlation (0.8 or higher on average) was obtained between the natural exterior and laboratory simulated scenes. However, such correlations were determined to be specific to the type of scene studied. The scope of this study needs to be narrowed. Another consideration, how moving objects in the scene would affect the results, needs further investigation.