# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
###### Abstract
Reference [24] set two CTU depth ranges and determined the best division result of CTU based on the texture complexity of the currently encoded CTU, which determines the depth range the CTU recursively computes RD-cost. Reference [25] transformed the division pattern decision problem into a classification problem and proposes a fast classification algorithm for CU based on convolutional neural network by learning the image texture, shape and other features for fast encoding. Although all of the above methods have effect on reducing the complexity of video coding, the geographical peculiarities of unmanned ships lead to low transmission bandwidth when the ship is underway. At the same time, the SVSV is more different than ordinary video images, and it requires the specialized data sets to train the network and reduce the distortion of small objects at sea, so these methods are unadaptable for the compression of targets at sea (especially for small targets). Until now, there has been no HEVC accelerated coding scheme developed for SVSV in the maritime domain. Therefore, by analyzing the characteristics of SVSV and combining the results of SVSV compression, optimizing the sessions with high compression delay is one of the key steps to realize real-time transmission and storage of SVSV from unmanned ships. 1. In this paper, by analyzing the characteristics of SVSVs (visible light video, thermal imaging video) and combining the results of video compression, a deep learning-based algorithm for optimizing the compression latency of shipboard vision sensor videos without affecting the video compression quality is proposed. 2. By collecting the segmentation results of shipboard vision sensor video compression, a CTU segmentation structure data set is built based on SVSV, training the proposed CU segmentation prediction model to improve the prediction accuracy. 3. In the process of compressing the SVSV, the encoder predicts the CTU division in advance by invoking the trained CU division prediction model, which reduces the time-consuming cost of calculating the CU rate distortion, decreases the coding complexity of the shipboard vision sensor video, and significantly reduces the compression latency. This paper is divided into six parts, and the specific organization is as follows. Section 2 details the image characteristics of shipboard vision sensor video, which includes SCLV video and thermal imaging video. Section 3 analyzes the results of HEVC compression of SVSV, including the relevant parameters used in the experiments, the time consumed in each step of the SVSV compression process, and the final results. In Section 4, we optimize the CTU partitioning process for compressing shipboard vision sensor videos, and designs a hierarchical model for CU partitioning based on SVSV compression, and a CNN-based CU partitioning prediction model is proposed, which uses a large number of collected SVSV data sets to train the network model to improve the prediction accuracy. Section 5 discusses our completed and future work. Finally, in Section 6 a conclusion is made on the work of this paper.