A LLM-Based Video Frame Rate Up-Conversion Method for the Low-Cost IoT Node
Ran Li, Zhen Yang, Xiao Tu · International Journal of High Speed Electronics and Systems · 2025
The rapid development of the Internet of Things (IoT) has significantly expanded its application scope, making it a cornerstone for emerging technologies such as big data and artificial intelligence. IoT systems, particularly low-cost nodes, face unique challenges in video processing tasks, including real-time video streaming and analytics. Frame Rate Up-Conversion (FRUC) is a critical technique that enhances video quality and user experience by increasing video frame rates. Central to FRUC is Bidirectional Motion Estimation (BME), which estimates motion between frames to generate intermediate frames. However, traditional BME-based methods often suffer from motion blur and edge artifacts, which degrade interpolation quality. Neural network-based FRUC approaches have improved interpolation accuracy but are computationally intensive and require extensive labeled datasets, making them unsuitable for low-cost IoT nodes with limited resources. To address these limitations, we propose a novel FRUC framework specifically designed for resource-constrained IoT devices. This framework leverages the inference capabilities of Large Language Models (LLMs) combined with Vision-to-Language (V2L) tokenization to enable efficient and accurate video frame interpolation. Contextual learning samples are extracted from consecutive video frames and transformed into structured tokens using the V2L tokenizer. These tokens, along with task-specific prompts, are processed by the LLM to generate high-quality interpolated frames. The proposed method eliminates the need for task-specific network training, significantly reducing computational overhead and enabling real-time processing on low-cost IoT nodes.Experiments conducted on the Vimeo90K dataset demonstrate that our approach achieves superior interpolation quality compared to traditional methods while maintaining computational efficiency, making it a practical solution for IoT applications. This framework highlights the potential of integrating LLM-based reasoning with lightweight video processing technologies, paving the way for enhanced multimedia capabilities in the next generation of IoT devices.