Large Multimodal Model-Based Video Encoding Optimization

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.

Print ISSN

1545-0279

Electronic ISSN

2160-2492

Published

2025-07

Content type

Original Research

Keywords

video encoding optimization, large language models (llms), per-title encoding, rate-distortion analysis

DOI

10.5594/JMI.2025/NSNH7881