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    "abstract": "Over the past decade, video consumption applications have surged, reaching new heights with the metaverse's emergence. This expansion burdens networks, data centers, and devices due to increased data volume and processing, leading to substantial energy consumption and high CO 2 emissions annually. Priority should be given to developing lightweight video compression algorithms to tackle this. Current standards fall short of achieving the desired efficiency. This study conducts a comprehensive analysis of Motion Estimation (ME) in leading metaverse video compression algorithms, VP9 and HEVC. Using Matlab, an exhaustive evaluation focuses on ME, allowing an objective comparison and integrates novel sustainability assessments. The findings highlight areas for future video compression improvements, paving the way for sustainable and optimized video storage and transmission in the metaverse.",
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    "authors": [
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        "name": "Natalia Molinero Mingorance"
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    "docLabel": "MIJ 2024, Volume 133, Number 1 (pp. 22 to 30)",
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    "docTitle": "Green Video Compression for Metaverse: Lessons Learned from VP9 and HEVC",
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      "print": "1545-0279",
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    "keywords": [
      "HEVC",
      "Metaverse",
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    "pages": "22–30",
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