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  "docId": "10.5594-JMI.2022.3207816",
  "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": "Super resolution (SR) is a technique that is getting increased attention. It was first deployed in high-end TVs to upconvert content, and now content providers are looking at how it can be used on the production side to guarantee the same high quality on all devices. Different techniques can be used to apply SR to standard definition (SD) and high definition (HD) content for transmitting ultra high definition (UHD) resolution. During a recent trial with France TV at the French Open, SD content with a 16:9 aspect ratio was processed offline to create UHD content, using a hybrid pipeline of deep restoration and SR methods. A pretrained deep neural network was used to produce an intermediate upscaled and deblurred HD version, which was then passed on to a faster, less memory-intensive, and machine-learning (ML)-based SR filter to produce the final UHD 4K resolution output. The result is a significantly better subjective viewing experience in terms of details and sharpness compared with classical filters used in live systems. Adequately addressing analog tape recordings, which have been digitized, is a significantly bigger technical challenge, which will need to be resolved in the future. While a one-model-fits-all approach for video enhancement sounds attractive due to its simplicity, the best visual quality is produced by applying purpose-built models trained for addressing the general types of degradation found in the source video.",
    "abstract$meta": {
      "confidence": "high",
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      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "authors": [
      {
        "bio": "Xavier Ducloux is the senior innovation manager at Harmonic, Cesson-Sévigné, France, where he is in charge of monitoring technological breakthroughs and developing innovation in the video delivery domain, including technology and ecosystem partnerships. With 28 years of experience in the digital TV industry, he has led a vast range of video compression projects with the Thomson Group, Cesson-Sévigné, and Harmonic, representing both companies in MPEG. He frequently speaks at industry-leading conferences across the world, is the author of many international publications, and holds multiple patents in video compression and delivery.",
        "name": "Xavier Ducloux"
      },
      {
        "bio": "Jon Frydensbjerg is the co-founder and chief technology officer at Pixop, Odense, Denmark, where he oversees research and product development enabling the innovation and growth of Pixop’s machine-learning (ML)-based video enhancement technology. With 20 years of experience in global technology, he has led and contributed to the development of a wide range of computer vision and cloud computing projects. He holds an MSc in informatics engineering, specializing in computer vision and graphics, from Aalborg University, Aalborg, Denmark.",
        "name": "Jon Frydensbjerg"
      },
      {
        "bio": "Morten Kolle is the co-founder and chief executive officer at Pixop, Odense, Denmark, where he is responsible for sales, marketing, and business development leading the global growth and performance of Pixop. With more than 25 years of experience as an entrepreneur and manager in the Danish IT sector, he has been a leader in building successful software development teams and bringing innovative technologies to the market. He founded the casual game development company Certus in 1996, which he sold successfully after running it for 10 years.",
        "name": "Morten Kolle"
      }
    ],
    "authors$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
    },
    "contentType": "orig-research",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.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",
      "holder$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
      },
      "year": "2022",
      "year$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
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    "copyright$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "docId": "10.5594-JMI.2022.3207816",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "docLabel": "SMPTE Motion Imaging Journal ( Volume: 131, Issue: 10, 2022)",
    "docLabel$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "docTitle": "Optimizing UHD Production with Super Resolution Techniques",
    "docTitle$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "docType": "Journal Article",
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
      "source": "parsed",
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    "doi": "10.5594/JMI.2022.3207816",
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      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "href": "https://doi.org/10.5594/JMI.2022.3207816",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
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    "issn": {
      "electronic": "2160-2492",
      "electronic$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
        "source": "parsed",
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        "version": "smpte-idams-publication@v1"
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      "print": "1545-0279",
      "print$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
        "source": "parsed",
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    "issn$meta": {
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      "source": "parsed",
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      "version": "smpte-idams-publication@v1"
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    "journalAcronym": "MIJR",
    "journalAcronym$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "journalTitle": "SMPTE Motion Imaging Journal",
    "journalTitle$meta": {
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
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      "updated": "2026-10-07T17:09:33.718Z",
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    "keywords": [
      "Artificial Intelligence",
      "Live Distribution",
      "Restoration",
      "Super Resolution",
      "UHD-1"
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    "keywords$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
      "source": "parsed",
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    "number": "10",
    "number$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9962270/09962279.xml) via idamsIngest.js",
      "source": "parsed",
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    "pages": "45–54",
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    "publicationDate": "2022-11-01",
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      "source": "parsed",
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    "publisher": "SMPTE",
    "publisher$meta": {
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      "city": "White Plains, NY",
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      "country": "USA",
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