# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 1 Introduction
By counting the coding results of a large number of screen content videos, four kinds of CTU depth ranges based on screen content coding were set, and CTU depth prediction based on deep learning was proposed. The model improves the coding efficiency by skipping and terminating the calculation of the RD-cost outside the prediction depth range. The article [3 ] transformed the decision-making problem of the division mode into a classification problem, and proposed a CU fast classification algorithm based on a convolutional neural network, which can perform fast coding by learning image texture, shape, and other features. The article [3 ]] proposed two fast CU dividing algorithms based on machine learning; one uses an online Support Vector Machine (SVM) for dividing CU, and the other is based on Deep-CNN to predict the CU dividing algorithm. The horizontal comparison of these algorithms proves that the deep learning method is more suitable for reducing the computational complexity of HEVC. Although these schemes for optimizing coding complexity can reduce the computational complexity and coding delay in the CTU division to a certain extent, they are not completely applicable to the ship domain. Due to the nature of the ship's work and the particularity of the geographical environment, it is difficult to frequently maintain and upgrade the equipment and instruments, and the main video data, such as shipborne radar and thermal imaging, are quite different from the traditional screen content video. To date, an accelerated coding scheme for compressing shipborne radar digital video in maritime field has not been developed. Therefore, optimizing the coding complexity by analyzing the characteristics of the digital video of the shipborne radar is a critical step to realizing the remote control of the ship. Based on the characteristics and application scenarios of shipborne radar digital video images, this study developed an algorithm to rapidly encode shipborne radar digital video by combining the algorithm with the deep learning method to predict the CTU in advance without affecting the quality of the encoding. By establishing a sufficient CTU division depth dataset based on shipborne radar digital video, the proposed CTU division depth interval prediction model is trained, which improves the efficiency of predicting the CTU division depth interval in the process of encoding shipborne radar digital video. When encoding shipborne radar digital video, the encoder can predict the search depth interval of the CU in advance by calling the trained prediction model, thus effectively reducing the computational redundancy caused by the full RD-cost of CU traversal, while reducing the computational complexity of encoding shipborne radar digital video. This paper is divided into four parts, and the specific organization is as follows. Section 3 introduces the characteristics of shipborne radar digital video in detail and presents the design of a CTU depth interval model based on radar images. Section 4 presents the optimization of the CTU division process in the SCC encoding process, and proposes a time delay optimization algorithm of compressing shipborne radar digital video based on deep learning. According to previous related research work, a CNN-based fast convolutional network structure is proposed to accurately predict the search depth interval of CU. Section 5 introduces the experimental process and analyzes the experimental results. Finally, we summarize the work in Section 6.