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<p>High Dynamic Range (HDR) imaging has become increasingly popular for its ability to capture a wider range of brightness, but displaying HDR content on Standard Dynamic Range (SDR) screens poses a significant challenge. Tone mapping, the process of adapting HDR images into SDR images for SDR displays, plays a crucial role in maintaining both visual quality and computational efficiency. In real-time applications, it is not only the quality of the tone-mapped image that matters but also the speed and computational complexity of the algorithm. This paper presents a novel global histogram-based Tone Mapping Operator (TMO) that combines superior speed with high-quality tone mapping. The key innovation is an efficient eye sensitivity model enabling single-pass histogram construction, significantly reducing processing time. The method enhances contrast while mitigating common issues like over- and under-contrast stretching. Experimental results show up to 80% reduction in execution time compared to leading techniques while maintaining competitive image quality across diverse HDR datasets.</p>

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Simplified eye sensitivity model based fast tone mapping algorithm for HDR images

  • 期刊论文

  • 发表日期：

    2025年4月4日

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  • Naureen Mujtaba,
  • Ishtiaq Rasool Khan,
  • Nadeem Ahmad Khan,
  • Muhammad Awais Bin Altaf

摘要

High Dynamic Range (HDR) imaging has become increasingly popular for its ability to capture a wider range of brightness, but displaying HDR content on Standard Dynamic Range (SDR) screens poses a significant challenge. Tone mapping, the process of adapting HDR images into SDR images for SDR displays, plays a crucial role in maintaining both visual quality and computational efficiency. In real-time applications, it is not only the quality of the tone-mapped image that matters but also the speed and computational complexity of the algorithm. This paper presents a novel global histogram-based Tone Mapping Operator (TMO) that combines superior speed with high-quality tone mapping. The key innovation is an efficient eye sensitivity model enabling single-pass histogram construction, significantly reducing processing time. The method enhances contrast while mitigating common issues like over- and under-contrast stretching. Experimental results show up to 80% reduction in execution time compared to leading techniques while maintaining competitive image quality across diverse HDR datasets.