# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
###### Abstract  
As the technology for offshore wireless transmission and collaborative innovation in unmanned ships continues to mature, research has been gradually carried out in various countries on methods of compressing and transmitting perceptual video while driving ships remotely. High Efficiency Video Coding (H.265/HEVC) has played an extremely important role in the field of Unmanned Aerial Vehicle (UAV) and autopilot, and as one of the most advanced coding schemes, its performance in compressing visual sensor video is excellent. According to the characteristics of shipborne vision sensor video (SVSV), optimizing the coding aspects with high computational complexity is one of the important methods to improve the video compression performance. Therefore, an efficient video coding technique is proposed to improve the efficiency of SVSV compression. In order to optimize the compression performance of SVSV, an intra-frame coding delay optimization algorithm that works in the intra-frame predictive coding (PC) session by predicting the Coding Unit (CU) division structure in advance is proposed in combination with deep learning methods. The experimental results show that the total compression time of the algorithm is reduced by about 45.49% on average compared with the official testbed \(HM16.17\) for efficient video coding, while the Bjontegaard Delta Bit Rate (BD-BR) increased by an average of 1.92%, and the Peak Signal-to-Noise Ratio (BD-PSNR) decreased by an average of 0.14 dB. shipborne vision sensor video; unmanned ship; H.265/HEVC; deep learning; delay of compression +
Footnote †: journal: Marine Science and Engineering  
that reduces the complexity of encoding shipborne radar digital video by using the HEVC technique combined with deep learning algorithms, while reducing the storage pressure on hardware and improving the efficiency of ship-to-shore communication. In reference [2], HEVC is used to compress intelligent video surveillance and achieve real-time classification of \"human-vehicle\" objects. Reference [3] proposed a method to reduce the complexity of the prediction part in HEVC by using Bayesian networks, and the improved HEVC coding scheme is applied to vehicular self-organizing networks (VANETs) to improve road safety. Reference [4] considered the application of HEVC techniques to vehicle surveillance to ensure surveillance video quality and storage costs as the resolution of VANETs increases and commercial VANETs do not contain large hard disk matrices. In order to reduce the pressure of storing video recordings in vehicles and to prevent problems such as falsification of digital recording evidence, [PERSON] et al. used the Advanced Video Coding (H.264/AVC) coding scheme and processed the I-frames in it and added an anti-forgery watermark [5]. Reference [6] provided a fast-transcoding solution for video in the field of the vehicular network (LoV), which intends to solve the coexistence problem of AVC technology and HEVC technology in the field of vehicular network. By exploiting the mapping relationship between decoding information in AVC and CU in HEVC, the authors improve the transmission efficiency of real-time video communication systems in vehicular networks. Reference [7] adds the ISODATA clustering algorithm and performs frame correlation analysis to the HEVC technique for UAV applications to achieve custom keyframes. Reference [8] used the H.264/AVC technique to compress video stream data in intelligent traffic system and combined it with deep learning methods for real-time monitoring of vehicles in the traffic stream. Machine learning methods have provided researchers with new directions in problems related to optimizing the efficiency of HEVC compression. Among them, the processing schemes represented by convolutional neural networks (CNN) are the most popular.