Abstract
Using packet networks for first-person control of unmanned systems arises a problem of large transmitted data volumes . The largest volume of traffic during first-person control is presented by video stream frames. So, to improve the efficiency of the communication network between unmanned systems and external pilot station, it is necessary to compress video stream frames. A high compression degree can be provided by using variational autoencoders. One of the problems of using variational autoencoders for frame compression is the occurrence of specific artifacts in frames. This article proposes methods for suppressing the occurrence of artifacts when restoring frames from the latent space by a neural network decoder, as well as an empirical scale for assessing autoencoder artifacts. The approach proposed encompasses preparing pixel data of a video stream frame for encoding and further reconstruction after decoding. It is experimentally shown that one of the proposed methods allows eliminating the absolute majority of artifacts without introducing significant distortions into the reconstructed frames.
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