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  "docId": "10.5594-JMI.2022.3160801",
  "document": {
    "abbrevTitle": "SMPTE Mot. Imag. J",
    "abbrevTitle$meta": {
      "confidence": "high",
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      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "abstract": "Deep perceptual preprocessing has recently emerged as a new way to enable further bitrate savings across several generations of video encoders without breaking standards or requiring any changes in client devices. In this article, we lay the foundation for a generalized psychovisual preprocessing framework for video encoding and describe one of its promising instantiations that is practically deployable for video-on-demand, live, gaming, and user-generated content (UGC). Results using state-of-the-art advanced video coding (AVC), high efficiency video coding (HEVC), and versatile video coding (VVC) encoders show that average bitrate [Bjontegaard delta-rate (BD-rate)] gains of 11%–17% are obtained over three state-of-the-art reference-based quality metrics [Netflix video multi-method assessment fusion (VMAF), structural similarity index (SSIM), and Apple advanced video quality tool (AVQT)], as well as the recently proposed nonreference International Telecommunication Union-Telecommunication?(ITU-T) P.1204 metric. The proposed framework on CPU is shown to be twice faster than × 264 medium-preset encoding. On GPU hardware, our approach achieves 714 frames/sec for 1080p video (below 2 ms/frame), thereby enabling its use in very-low-latency live video or game streaming applications.",
    "abstract$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "authors": [
      {
        "bio": "Aaron Chadha received MA and MEng degrees (Hons.) in information and computer engineering from the University of Cambridge, Cambridge, U.K., in 2014, and a PhD degree in computer vision and machine learning from the University College London, London, U.K., in 2018. He is currently a senior machine learning engineer with iSIZE Technologies, London, U.K. His research interests include machine learning, image/video recognition, compression, and domain adaptation. He has experience in working with multiple modalities, including event-based neuromorphic vision sensing.",
        "name": "Aaron Chadha",
        "orcid": "0000-0002-6431-0855"
      },
      {
        "bio": "Mohammad Ashraful Anam received a PhD degree in electronic engineering from the University College London, London, U.K., where he received the Lombardi Prize for the Best PhD thesis in electronic engineering. He is currently a senior software engineer at iSIZE Technologies. His research interests include machine learning, cloud computing and distributed systems, error tolerant computing, and high-performance cloud computing.",
        "name": "Mohammad Ashraful Anam"
      },
      {
        "bio": "Matthias Treder received a PhD in human pattern recognition from Radboud University Nijmegen, Nijmegen, The Netherlands, in 2009. He then worked on the application of machine learning to neuroimaging data at the Technical University of Berlin, Berlin, Germany, from 2009 to 2013; the University of Cambridge, Cambridge, U.K., from 2013 to 2015; and the University of Birmingham, Birmingham, U.K., from 2016 to 2017. He joined Cardiff University, Cardiff, U.K., in 2017 and became an assistant professor in computer science in 2018. In 2021, he joined iSIZE Technologies as a senior machine learning engineer.",
        "name": "Matthias Treder"
      },
      {
        "bio": "Ilya Fadeev studied cyber-security at Tver State University, Tver, Russia, from 2012 to 2017. He is currently a senior software engineer with iSIZE Technologies. He has a strong background in C++ and general-purpose graphics processing unit (GPGPU) programming, including hardware-specific algorithm and code optimizations. He has experience working with machine learning, computer vision, and video codecs.",
        "name": "Ilya Fadeev"
      },
      {
        "name": "Yiannis Andreopoulos"
      }
    ],
    "authors$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "contentType": "orig-research",
    "contentType$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "copyright": {
      "holder": "SMPTE",
      "holder$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
      },
      "year": "2022",
      "year$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
      }
    },
    "copyright$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "docId": "10.5594-JMI.2022.3160801",
    "docId$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "docLabel": "SMPTE Motion Imaging Journal ( Volume: 131, Issue: 4, May 2022)",
    "docLabel$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
    },
    "docTitle": "Toward Generalized Psychovisual Preprocessing For Video Encoding",
    "docTitle$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.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",
    "docType$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "doi": "10.5594/JMI.2022.3160801",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "href": "https://doi.org/10.5594/JMI.2022.3160801",
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      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "issn": {
      "electronic": "2160-2492",
      "electronic$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
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      "print": "1545-0279",
      "print$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
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    "issn$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "journalAcronym": "MIJR",
    "journalAcronym$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "journalTitle": "SMPTE Motion Imaging Journal",
    "journalTitle$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "keywords": [
      "Deep Neural Networks",
      "Perceptual Optimization",
      "Video Delivery"
    ],
    "keywords$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "number": "4",
    "number$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "pages": "39–44",
    "pages$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
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    "publicationDate": "2022-05-01",
    "publicationDate$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "publisher": "SMPTE",
    "publisher$meta": {
      "confidence": "high",
      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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    "publisherLocation": {
      "city": "White Plains, NY",
      "city$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
        "source": "parsed",
        "updated": "2026-10-07T17:09:33.718Z",
        "version": "smpte-idams-publication@v1"
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      "country": "USA",
      "country$meta": {
        "confidence": "high",
        "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Journal Article Repository/2022/7261654/9772088/09772094.xml) via idamsIngest.js",
        "source": "parsed",
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      "updated": "2026-10-07T17:09:33.718Z",
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      "bibliographic": [
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        "orphan/10.5594-JMI.2022.3160801/h:21cce789",
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        "confidence": "medium",
        "note": "References parsed from the published PDF (Journal Article Repository/2022/7261654/9772088/09772094.pdf). SMPTE's library delivery has no reference file or full text for this paper (src/main/reports/smpte-upstream/referenceFilesTruthTable.md). Cross-check: Crossref lists 12 reference(s) for this DOI; 12 parsed from the PDF.",
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    "volume": "131",
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      "source": "parsed",
      "updated": "2026-10-07T17:09:33.718Z",
      "version": "smpte-idams-publication@v1"
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