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  "docId": "10.5594-JMI.2019.2941361",
  "document": {
    "abbrevTitle": "SMPTE Mot. Imag. J",
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      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "abstract": "This paper is about a theoretical framework on perceptual reproduction quality assessment. Visual perception is the main theme for two approaches on image metrics: visual performance and image quality. Visual performance looks at the perception of standard elementary images, such as gratings, by different observers or by a single observer in different visual conditions; image quality looks at the perception of variations of the same complex scene by a standard observer. In the audiovisual field, all the configurations approved by the creative team must be reproduced in the final viewing. This cannot be measured by visual performance or image quality indexes. A third approach is needed, which may be called perceptual reproduction quality assessment. Visual perception assessment is not measuring defects, per se; it is built to evaluate the perceptual effectiveness of a given scene coding through a given reproduction technology. It is only dependent on the number of configurations being perceived through a given theater system relative to the various configurations reproduced and approved in the review room. As such, it is a statistical measure. It is, in fact, similar to the definition of relative entropy. One important fact is that this evaluation must include a model of visual perception. This paper illustrates how relative entropy and entropy loss can be estimated, while taking into account the coding transform, the projector performances, maximum light, and the characteristics of the human visual system. This scheme works easily for monochromatic content. Color, trichromacy, is quite challenging. The size of configurations goes from 2¹² to 2³⁶, around 6 billion configurations. Calculation and memory requirements are huge. Nonlinearities are adding complexity, leading to calculation of an upper limit only of entropy loss. The presentation of results is also a challenge. One way to solve this is to separately process lightness and chromatic information. In summary, the main arguments in this paper are: 1) besides image quality and visual performance, there is a need for perceptual reproduction quality assessment; 2) entropy is the proper statistical estimator for evaluating the perceptual reproduction quality; and 3) color reproduction quality estimation requires processing lightness and chromatic attributes separately.",
    "abstract$meta": {
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      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "authors": [
      {
        "bio": "François Helt has 30 years of professional video and film experience with a background in mathematics, semantics, and filmmaking. Since 1991, he has been a research and development manager for special effects software teams, including film scanners and film printer drivers. Helt was a technical manager of the European project Limelight from 1994 to 1997, which focused on designing a full digital film restoration system. Between 1997 and 2002, he was the founder and chief executive officer of the DUST Company, Sophia Antipolis, France, specializing in digital film restoration and processing. From 2004 to 2006, he was the author of the software for automatic digital film restoration and designed software for color conversion and calibration software for digital cinema. Helt was the technical director of Doremi Technologies (Europe), Sophia Antipolis, France, from 2006 to 2012 and the chief scientific officer of Highlands Technologies Solutions, Sophia Antipolis, France, from January 2013 to December 2016. He has been a scientific consultant for EclairColor, Paris, France, since January 2017. Helt also designed a projection measurement system. His current interests include visual perception models, measurement theory, and philosophy of information.",
        "name": "François Hel",
        "orcid": "0000-0002-6208-0734"
      }
    ],
    "authors$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2019/7261654/8901104/08901354.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "contentType": "orig-research",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2019/7261654/8901104/08901354.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "copyright": {
      "holder": "SMPTE",
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        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2019/7261654/8901104/08901354.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
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      "year": "2019",
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        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2019/7261654/8901104/08901354.xml) via idamsIngest.js",
        "source": "parsed",
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        "version": "smpte-idams-publication@v1"
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2019/7261654/8901104/08901354.xml) via idamsIngest.js",
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      "version": "smpte-idams-publication@v1"
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    "docId": "10.5594-JMI.2019.2941361",
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2019/7261654/8901104/08901354.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "docLabel": "SMPTE Motion Imaging Journal ( Volume: 128, Issue: 10, 2019)",
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      "confidence": "high",
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      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "docTitle": "Visual Perception Entropy Measure",
    "docTitle$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2019/7261654/8901104/08901354.xml) via idamsIngest.js",
      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "docType": "Journal Article",
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    "doi": "10.5594/JMI.2019.2941361",
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    "href": "https://doi.org/10.5594/JMI.2019.2941361",
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      "electronic": "2160-2492",
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        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2019/7261654/8901104/08901354.xml) via idamsIngest.js",
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    "journalAcronym": "MIJR",
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    "journalTitle": "SMPTE Motion Imaging Journal",
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    "keywords": [
      "Entropy",
      "Perception",
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      "Quality Metrics",
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2019/7261654/8901104/08901354.xml) via idamsIngest.js",
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    "number": "10",
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    "pages": "51–57",
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    "publicationDate": "2019-11-01",
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