In this paper, statistics such as distribution of peak luminance, region of peak luminance in frames, colors of high dynamic range contents are analyzed Based on the analysis, essential requirements for future high dynamic range displays are discussed. For our statistical study, various types of high dynamic range content that have been provided by studios or content providers are considered. Since they have been being supplied by limited studios and network-based content providers, a large amount of the content is movies that utilize limited dynamic range, average luminance and color gamut compared with the other dynamic contents. In spite of the trend, we claim that capability of high dynamic range displays do not need to be restricted by considering the current content industry since very bright high dynamic range contents that have higher luminance and wide color information absolutely need to be also considered when defining specification of future high dynamic range displays. To support this fact, we review the analysis results and requirements which are needed to sufficiently represent vivid high dynamic range presentation to match the human visual perception capability.
Stack-based high dynamic range (HDR) imaging is a technique for achieving a larger dynamic range in an image by combining several low dynamic range images acquired at different exposures. Minimizing the set of images to combine, while ensuring that the resulting HDR image fully captures the scene’s irradiance, is important to avoid long image acquisition and postprocessing times. The problem of selecting the set of images has received much attention. However, existing methods either are not fully automatic, can be slow, or can fail to fully capture more challenging scenes. In this paper, we propose a fully automatic method for selecting the set of exposures to acquire that is both fast and more accurate. We show on an extensive set of benchmark scenes that our proposed method leads to improved HDR images as measured against ground truth using the mean squared error, a pixel-based metric, and a visible difference predictor and a quality score, both perception-based metrics.