Research of Video Stream Intensity Limits in UAV FPV Control in Frame Prediction Mode. Part I: Models and Methods

Currently, unmanned aerial vehicles have found wide application in various spheres of the national economy. FPV control refers to a method of controlling unmanned aerial vehicles in which the video stream from the unmanned aircraft is transmitted to the remote pilot station in real time. Due to failures in the communication network, packets with video stream data may be lost or delayed and be delivered late. One of the ways to compensate for the drop in FPS in case of frame loss or delay at the remote pilot station is to predict intermediate frames of the video stream. This paper presents a scheme for predicting intermediate frames of a video stream and presents the results of experiments to establish a realistically possible increase in FPS in a neural network codec, in which various autoencoders such as VQ-f16 are used for video compression as well as the lossless latent feature space compression algorithm DEFLATE, while the dynamic multiscale voxel flow neural network DMVFN is used for prediction. A regression model was developed to predict the prediction time. The task of analyzing FPS for various configurations of the neural network decoder on the side of the external pilot is formalized.