This paper demonstrates that our LMM-based approach not only significantly reduces the computational complexity required for sampling based per-title video encoding—by an astounding 13 times—but also maintains the same level of bitrate saving. These findings not only pave the way for more efficient and adaptive video encoding strategies but also highlight the potential of multi-modal models in enhancing multimedia processing tasks.
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 Multi-modal Model (LMM) to revolutionize the process of per-title video encoding optimization. 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 not only significantly reduces the computational complexity required for sampling based per-title video encoding—by an astounding 13 times—but also maintains the same level of bitrate saving.
The implications of this research...
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Technical Papers 2024 Session: Advances in Video Coding – encoder optimisations and film grain
