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High dynamic range (HDR) imaging offers an enhanced visual experience by capturing a wider range of real-world luminance levels in digital images. Driven by the increasing demand for high-quality visuals, HDR monitor technology has seen significant advancements. As such monitors become commonplace in both consumer and professional settings, efficient methods are needed for both converting standard dynamic range (SDR) content to HDR—known as reverse tone mapping—and optimizing natural HDR lighting content for display on HDR monitors. A reverse tone mapping procedure aims to produce natural lighting levels, but even on high-end HDR monitors, such images still require adjustment to avoid hard clipping. This paper presents a solution that jointly does both steps: (1) reverse tone mapping to a display-aware HDR representation, and (2) direct generation of an image tailored for a chosen monitor brightness value. We propose a novel neural network architecture conditioned on the target peak brightness via a lightweight multi-layer perceptron (MLP) module injected at the bottleneck, which predicts a bracketed stack of LDR exposures serving as the method’s HDR representation. In this manner, the ill-posed tone mapping problem is guided by auxiliary information about display characteristics, improving visual quality. Experiments throughout the full consumer HDR range (100–4000 nits) show consistent improvements over the display-agnostic baseline in peak luminance utilization, local contrast, color and perceptual quality.
Mitrache et al. (Sat,) studied this question.