HDRn

HDRn is an abbreviation for High Dynamic Range with neural tone mapping. It denotes a family of techniques that combine high dynamic range content with neural networks to perform tone mapping and color rendering, in order to preserve details in shadows and highlights while delivering natural-looking images. The term appears in academic and industry discussions as a generic concept rather than a single specification.

Technical approaches in HDRn typically begin with an HDR source or multi-exposure captures. A neural network is trained to map the HDR data to a display-referred image, optimizing for perceptual quality, color fidelity, and contrast. The network can perform exposure fusion, local tone mapping, and gamut mapping in a single pass, often using loss functions that combine structural similarity with perceptual metrics. HDRn frameworks may also adapt to specific display characteristics and viewing conditions to improve consistency across devices.

Applications of HDRn include content creation, post-production, and real-time rendering in gaming and virtual reality, where conventional tone mapping can introduce artifacts or fail to preserve perceptual detail. Implementations require substantial computational resources and robust training data to generalize across scenes. Interoperability across devices and software remains a challenge, and there is no universal standard for HDRn delivery.

HDRn is not a replacement for established HDR standards such as HDR10+ or Dolby Vision. Rather, it can be integrated as an intermediate processing stage within pipelines aimed at optimizing perceptual quality. Ongoing research explores evaluation frameworks and methods to improve generalization and reproducibility across diverse content.