This research emphasizes the importance of constructing an efficient deep learning model that can perform real-time video frame interpolation in resource-constrained mobile environments, amidst the rapid advancements in the field of deep learning technology.
<br>The study proposes a lightweight network model and system for real-time video frame interpolation in mobile environments.
<br>By integrating intelligent data adjustment, lightweight CNN architecture, and distributed computing techniques, the model is designed to operate efficiently even with limited resources.
<br>The proposed lightweight network model contributes to the field of video frame interpolation by providing a lightweight solution tailored to mobile environments.
<br>It also opens up possibilities for various industries where efficient storage utilization and high frame rates are crucial.
<br>Furthermore, the research provides insights into lightweight techniques and distributed computing strategies that can be applied to other deep learning models in resource-constrained environments.
