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    "abstract": "Ambient light can significantly degrade picture quality by reducing visibility of details and contrast. In many cases, it is difficult to control or eliminate ambient light, making it essential to compensate for its effect. This is especially true in the context of media and entertainment, where viewers often consume content in different lighting conditions and environments. Traditional image processing techniques, such as adjusting the backlight, brightness/contrast, or more professionally using a PLUGE signal, do not sufficiently model the human visual system. The result is that detail is lost under changes in ambient light. This is especially problematic for very dark scenes, which has become apparent with cinematic high dynamic range content where numerous consumer complaints have graced headline news. — This paper proposes a method of ambient light compensation by adaptively modeling the contrast sensitivity functions of the human visual system. By estimating global eye adaptation to both the content and the surround environment, the contrast sensitivity models may be used to maintain perceptual detail and contrast under varying ambient illumination. We build upon existing models of human vision and show how we can use cone sensitivity to maintain global contrast. Since displays have limited dynamic range, we show how this can be adaptive to both the content and environment.",
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    "authors": [
      {
        "affiliation": "Senior Staff Researcher, Dolby Laboratories Inc.",
        "name": "Jaclyn Pytlarz"
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      "holder": "Society of Motion Picture and Television Engineers, Inc.",
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      "year": "2023",
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      "version": "smpte-idams-publication@v1"
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    "docTitle": "Ambient Light Compensation Through Adaptive Visual Modeling",
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    "doi": "10.5594/M002028",
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    "href": "https://doi.org/10.5594/M002028",
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    "isbn": {
      "print": "978-1-61482-964-5",
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    "keywords": [
      "Ambient",
      "Light",
      "Contrast",
      "Perception",
      "Media"
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    "number": "10",
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    "pages": "11–12",
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    "publicationDate": "2023-10-16",
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