A study on secure coding of intelligent inspection video in plant areas based on improved deep reinforcement learning

To realize effective data compression, reduce resource occupation, and provide a smoother video transmission experience. The security coding method of an intelligent factory detection video based on improved deep reinforcement learning is studied. First, the transmission and network model of the factory inspection video are analyzed, and a conditional depth network based on Transformer is designed. The network learns the image features through a feature extractor, and processes these features through a motion estimation and compression module. Multi-layer and multi-hypothesis motion compensation and a self-encoder are used to encode features based on the predicted features. The optimal coding strategy is learned using the depth deterministic strategy gradient method, and the residual is outputted in the decoding stage to reconstruct the video frame. The experimental results show that this method can realize the security coding of intelligent inspection video in plant area, and the BDBR (Bjontegaard delta bitrate) index can be saved by 68.01%, and the BD-PSNR (Bjontegaard delta PNSR) index can be increased by 3.49 dB on average; 50 iterations minimize the inefficient strategy ratio; The maximum average return is about 152%; It has small memory occupation, low bandwidth requirements, and high real-time video transmission; High user experience rating.