Large Multimodal Model-Based Video Encoding Optimization

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Publisher: SMPTE
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Motion Imaging Jour.
Volume: 134, No. 4, pp. 46–52
Abstract: In the realm of video encoding, achieving the optimal balance between encoding efficiency and computational complexity remains a formidable challenge. This paper introduces a groundbreaking framework that utilizes a Large Multimodal Model (LMM) to revolutionize the per-title video encoding optimization process. By harnessing the predictive capabilities of LMMs, our framework estimates the encoding complexity of video content with unprecedented accuracy, enabling the dynamic selection of encoding configurations tailored to each video's unique characteristics. The proposed framework marks a significant departure from traditional per-title encoding methods, which often rely on expensive and time-consuming sampling in the rate-distortion space. Through a comprehensive set of experiments, we demonstrate that our LMM-based approach significantly reduces the computational complexity required for sampling-based per-title video encoding by an astounding 13 times and maintains the same level of bitrate saving. These findings pave the way for more efficient and adaptive video encoding strategies and highlight the potential of multimodal models in enhancing multimedia processing tasks. The implications of this research extend beyond the immediate improvements in encoding efficiency, offering a glimpse into the future of multimedia content distribution and consumption in an increasingly video-centric digital landscape.
Publication Date: 2025-07-01
DOI: 10.5594/JMI.2025/NSNH7881
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2025/NSNH7881
Author(s): Zhengfang Duanmu
Mingzhe Jiang
Keyword(s): Video Encoding Optimization, Large Language Models (LLMs), Per-Title Encoding, Rate-Distortion Analysis
Copyright: © 2025 SMPTE

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Zhengfang Duanmu and Mingzhe Jiang; Large Multimodal Model-Based Video Encoding Optimization, MIJ 2025, Volume 134, Number 4 (pp. 46 to 52); SMPTE, 2025. Available at https://doi.org/10.5594/JMI.2025/NSNH7881

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Zhengfang Duanmu and Mingzhe Jiang; Large Multimodal Model-Based Video Encoding Optimization, MIJ 2025, Volume 134, Number 4 (pp. 46 to 52); SMPTE, 2025. Available at https://doi.org/10.5594/JMI.2025/NSNH7881

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<span class="citation">Zhengfang Duanmu and Mingzhe Jiang; <cite>Large Multimodal Model-Based Video Encoding Optimization</cite>, MIJ 2025, Volume 134, Number 4 (pp. 46 to 52); SMPTE, 2025. Available at <a href="https://doi.org/10.5594/JMI.2025/NSNH7881" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2025/NSNH7881</a></span>

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Zhengfang Duanmu and Mingzhe Jiang; Large Multimodal Model-Based Video Encoding Optimization, MIJ 2025, Volume 134, Number 4 (pp. 46 to 52); SMPTE, 2025
doi: 10.5594/JMI.2025/NSNH7881
url: https://doi.org/10.5594/JMI.2025/NSNH7881

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<li>
Zhengfang Duanmu and Mingzhe Jiang; <cite id="bib-10-5594-jmi-2025-nsnh7881">Large Multimodal Model-Based Video Encoding Optimization</cite>, MIJ 2025, Volume 134, Number 4 (pp. 46 to 52); SMPTE, 2025
<span class="doi">10.5594/JMI.2025/NSNH7881</span>
</li>