# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 1 Introduction
saving by about 50% compared with Advanced Video Coding (AVC). However, the coding complexity simultaneously increases by 253% [2 ]. The SCC coding scheme is proposed based on the HEVC standard, but is different from the Coding Unit (CU) division of HEVC. According to the texture complexity of the Quantization Parameter (QP) and CTU, HEVC recursively divides the CU downward and calculates the RD-cost to obtain the best division method. However, due to the introduction of IBC and PLT modes in SCC, the RD-cost of various modes of CU are calculated by traversal. Even in CUs with high texture complexity, the downward division can be stopped. This method of sacrificing computing power to improve coding performance is obviously not suitable for an environment with limited computing resources. In recent years, numerous research has been conducted using machine learning methods to reduce the complexity of HEVC coding. In order to solve the problem that the quad-tree-based recursive CU division search is too complicated in the CTU dividing process, the method in [3 ] established a large-scale coding test general sequence CU dividing dataset. The study proposed a Hierarchical CU Partition Map (HCPM), and designed an end-to-end ETH-CNN intra-frame division decision-making network and an ETH-CNN-LSTM inter-frame division decision-making network. By predicting the intra-frame and inter-frame CU division results, deep learning methods were used instead of search decision for CU division in HEVC. Literature [3 ] proposed a fast decision-making algorithm for CU based on deep learning, which uses the neural network structure in the Inception module to decide the CTU division in advance, in order to achieve stable transmission in an environment with high network fluctuations. Method [3 ] regarded coding efficiency and bit error rate as the main factors affecting CU division, and proposed a Low-Complexity Mode Switching-based Error Resilient Encoding (LC-MSRE) based on a neural network. The approach used SMIF-intra and STMIF-inter to compare the intra-frame and inter-frame CU distortion to determine the best way to divide the CTU. With the process of the PU angle prediction link in the intra-prediction process, HEVC increases the PU prediction modes to 35, including 33 angle prediction modes and Planar and DC prediction modes. Method [3 ; 3 ] proposed a CNN-based PU angle prediction model (AP-CNN), which replaced the traditional PU angle prediction mode in the HEVC lossless coding mode. Ref. [3 ] was based on the original AP-CNN in [3 ], improving the loss function and training strategy to enhance the prediction accuracy. Ref. [3 ] proposed an optimized architecture LAP-CNN based on AP-CNN, which reduced the complexity of the model and increased the coding speed. In [3 ], the author designed a CNN optimization model based on LeNet-5, and adopted an early termination of the CU division strategy, which reduced the computational complexity of the rate distortion cost (RD-cost) in intra-prediction. In order to reduce the coding complexity of SCC, the method in [3 ] adopted a network structure (Deep-SCC) for predicting the CU division mode, in which Deep-SCC-I is responsible for quickly predicting the original sample value, and Deep-SCC-II is responsible for reducing the optimal computational complexity of the prediction mode. In addition to the above-mentioned methods of reducing the computational complexity of HEVC with the CU division decision as the core, some researchers have considered the relationship between the depth of CTU division and the computational complexity of HEVC. The method in [3 ] sets two CTU depth ranges. According to the texture complexity of the currently coded CTU, it determines the depth range in which the CTU recursively calculates the RD-cost and determines the best segmentation result of the CTU. The method in [3 ] distinguished between the CU division methods in HEVC and SCC.