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    "abstract": "Video streaming workflows aim to maximize video quality while still maintaining smooth video streaming performance. A traditional fixed bitrate ladder consists of predetermined bitrate-resolution pairs optimized across a wide variety of content. Consequently, these pairs are rarely optimized for a given piece of content. Some encoding tools address this by encoding each piece of video content with many codec parameters and then evaluating the results using a video quality metric. However, this process requires significant computation, which increases cost and encoding time. In this paper we propose a novel content-driven workflow that predicts optimal encoding parameters to achieve a target perceptual video quality. We do so by designing a deep learning model that, based on the video input, predicts a Video Multimethod Assessment Fusion (VMAF) rate-distortion curve. Our results indicate that such a content-driven approach efficiently reduces the number of encoding attempts, minimizes necessary cloud computing resources, encodes most efficiently, and maximizes perceptual video quality.",
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    "authors": [
      {
        "name": "Trisha Mittal"
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      {
        "name": "Subhadra Gopalakrishnan"
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      {
        "name": "Jaclyn Pytlarz"
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      {
        "name": "Robin Atkins"
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      {
        "name": "Benjamin Rolling"
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        "name": "Gabe Russell"
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    "docLabel": "MIJ 2025, Volume 134, Number 1 (pp. 14 to 20)",
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    "docTitle": "Efficient Content-Driven Encoding Towards a Target Video Quality",
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    "journalTitle": "SMPTE Motion Imaging Journal",
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    "keywords": [
      "Bitrate Ladder",
      "Streaming",
      "Rate-Quality Curves",
      "Video Coding",
      "Video Compression"
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    "publicationDate": "2025-01-01",
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