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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 which are 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 VMAF rate-distortion curve. Our results indicate that such a content-driven approach is an efficient way to reduce the number of encoding attempts, minimize necessary cloud computing resources, encode most efficiently, and maximize perceptual video quality.",
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        "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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    "docLabel": "MTS 2024, Article 37 (pp. 1 to 11)",
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    "docTitle": "Efficient Content-Driven Encoding Towards a Target Video Quality",
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    "doi": "10.5594/MOO/3048",
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      "Bitrate Ladder",
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      "Rate-Quality Curves",
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