Synchronous restoration of video key frame loss based on digital media communication protocol

Abstract

AbstractThis paper presents a synchronous recovery method for video key frame loss, aiming to analyze the experimental video through digital media feature extraction algorithm. By analyzing and designing effective communication protocols in real-time embedded systems, video data can be better processed. On this basis, key frames are restore synchronously through digital media communication protocol, and verified by comparing with other algorithms. Experimental results shows that the recall and precision of this algorithm for key frame extraction are 90.1% and 100% respectively, Among the three algorithms compared, the recall and precision of video key frame extraction based on single feature algorithm are the highest, 80.2% and 85.9% respectively. At the same time, the synchronization restoration time of this algorithm for lost key frames is 12.2 s, which took less time than other algorithms. It can be seen that the algorithm based on digital media feature extraction is of great significance for synchronous restoration of video key frame loss. It can be seen that the algorithm based on digital media feature extraction is of great significance for the synchronous recovery of video key frame loss, and can effectively promote the development of video data diversity and the improvement of information interaction. Under the digital media communication protocol, it can effectively synchronize the audio and video receiving end of multimedia information, and then recover and predict the lost frames in the video sequence. First, it can enhance the internal characteristics of video frames and the similarity and consistency of images between frames. Secondly, it can significantly improve the accuracy and efficiency of video data processing. Finally, from the perspective of practical development, the synchronous recovery of video key frame loss based on digital media communication protocol can effectively promote the sustainable development of media data, which has certain social value and practical significance in the current era of big data.