Research on Video Frame Insertion Method of Complex

Currently, the primary video frame interpolation algorithm leveraging deep learning encounters difficulties in accurately extracting key information from two adjacent frames to perform motion estimation, particularly when dealing with complex motion scenarios. This often leads to the generation of frames containing artifacts in regions with intricate movements. To address the aforementioned challenges, this paper introduces a novel video frame insertion algorithm that incorporates spatiotemporal attention mechanisms, which uses 3D convolution to extract timing information and spatial features [1], uses spatiotemporal attention module to capture the interdependency of time and spatial dimensions between frames, finds the area most closely related to complex motion in the input frame, and carries out global modeling of complex motion. The desired intermediate frame is obtained by blending multi-scale spatial features. Moreover, distillation learning is used to compress the model, improve the training speed and generalization ability, and simultaneously solve the problems of too large model parameters, high memory consumption and difficult to be widely used. The experimental outcomes demonstrate that the proposed algorithm effectively achieves rapid and high-quality frame insertion, surpassing existing algorithms in terms of computing resource efficiency while maintaining superior accuracy.