Perceived Dynamic Range of HDR Images with no Semantic Information

Abstract

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 pixelbased 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