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    "abstract": "The consumption of video content on the internet is increasing at a constant pace, along with an increase of video quality. As an answer to the ever-growing demand for high quality video, compression technology improves steadily. About every decade, a new major video compression standard is issued, providing a decrease of bitrate by a factor two. Interestingly, the technology does not change radically between codecs generations. Instead, the same principles are re-used and pushed further. There has been several attempts to depart from this model, but none achieved to be competitive. Recently, the research community has started focusing on deep learning-based strategies. Could it be the new contender to the classical approach? This paper analyzes the benefits and limitations of deep learning-based video compression methods, and investigates practical aspects such as rate control, delay, memory consumption and power consumption. Overlapping patch-based end-to-end video compression strategy is proposed to overcome memory limitations.",
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      "version": "smpte-idams-publication@v1"
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    "authors": [
      {
        "affiliation": "Ateme",
        "name": "Thomas Guionnet"
      },
      {
        "affiliation": "Ateme",
        "name": "Marwa Tarchouli"
      },
      {
        "affiliation": "Ateme",
        "name": "Thomas Burnichon"
      },
      {
        "affiliation": "Ateme",
        "name": "Mickael Raulet"
      }
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      "version": "smpte-idams-publication@v1"
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      "version": "smpte-idams-publication@v1"
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      "holder": "Society of Motion Picture and Television Engineers, Inc.",
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        "version": "smpte-idams-publication@v1"
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      "year": "2023",
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      "version": "smpte-idams-publication@v1"
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      "note": "Ingested from SMPTE IDAMS <publication> (_source/SMPTE/Conference Repository/Conference Papers/2023/10372504/10372505/10372579.xml) via idamsIngest.js",
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      "version": "smpte-idams-publication@v1"
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    "docLabel": "SMPTE Meetings and Conferences ( October 2023)",
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      "version": "smpte-idams-publication@v1"
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    "docTitle": "The future of video compression - Moving beyond hybrid codecs with machine learning",
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    "doi": "10.5594/M002026",
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    "href": "https://doi.org/10.5594/M002026",
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    "isbn": {
      "print": "978-1-61482-964-5",
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    "keywords": [
      "Video Compression",
      "Video Codec",
      "MPEG-2",
      "H.264",
      "AVC",
      "HEVC",
      "VVC",
      "Artificial Intelligence",
      "Machine Learning",
      "Deep Learning",
      "End-to-end Video Encoding"
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    "number": "10",
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    "pages": "16–22",
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    "publicationDate": "2023-10-16",
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    "publisher": "SMPTE",
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      "city": "White Plains, NY",
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