# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 3 Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning  
### Flow Chart of Compressing Delay Optimization Algorithm  
The time delay optimization algorithm of compressing shipborne radar digital video based on deep learning proposed in this paper is shown in Figure 27.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Radar Range** & **Background** & **Number of Sequences** & **Frames** & **Samples in Frame** & **Raw Samples Amount after FLIIS** \\ \hline \multirow{4}{*}{1.5 NM (1:1)} & Anchor & 12 & 3600 & & 921,600 & 92,160 \\  & Under way & 16 & 4800 & \(16\times 16\) & 1,228,800 & 122,880 \\  & Port & 16 & 4800 & & 1,228,800  
Considering the particularity of radar video applications, in order to take into account the speed and efficiency of encoding radar video, we did not choose to directly predict the CTU division result of the radar image when designing the algorithm. Instead, we divided the depth interval by predicting the CTU, allowing the encoder to independently decide the best division result within the set CU search depth interval, which reduced the error between the predicted division result and the actual division result. The specific process of the time delay optimization algorithm of compressing radar digital video proposed is as follows:  
1. Input the shipborne radar digital video signal to be compressed into the encoder. If the radar video format is RGB, it will be preprocessed to the YUV format. Before officially starting encoding, the encoder will divide a frame of the radar image into N CTUs to be encoded. 2. The Y component of the CTU is input into the trained prediction model--the network structure of the model is shown in Figure 7 (we introduce the structure and training process in detail in Section 3.2)--and the pixel matrix is normalized to accelerate the convergence rate. The output of the model is the probability of \(D_{x}\). 3. Compare the probability of each classification label output by the model to determine the search depth interval of CU in the current CTU. If the probability of \(D_{0}\) is the maximum, the current CTU is not divided directly; that is, the depth is 0, and the RD-cost is no longer traversed downward. If not, determine whether \(D_{1}\) is the maximum. If the probability of \(D_{1}\) is maximum, the CU search depth interval is set to [0, 1], otherwise, the minimum value of the CU search depth interval is set to 1. Figure 6: Encoding delay optimization algorithm. Determine whether \(D_{2}\) is the maximum value; if so, set the maximum value of the CU search depth interval to 2, and calculate the RD-cost of each CU mode within the set CU search depth interval [1; 2] to determine the final CTU division result. Otherwise, judge whether \(D_{3}\) is the maximum value; if so, the maximum value of the CU search depth interval is set to 3, and the set CU search depth interval [1; 3] is traversed to calculate the RD-cost of each mode of the CU to determine the final CTU division result. If not, the minimum value of the CU search depth interval is set to 2. Check whether \(D_{4}\) is the maximum value. If \(D_{4}\) is the maximum value, set the maximum value of the CU search depth interval to 3, and calculate the RD-cost of each CU mode within the set CU search depth interval [2; 3] to determine the final CTU division result. If not, initialize the CU search depth interval to \([0,3]\). 6. In the process of predicting the CTU division depth interval, there is a very low probability that the prediction will be inaccurate; that is, if the probability is low or zero, it is not likely to happen. In order to avoid the influence of this situation on the encoded image, the CU search depth interval is initialized to \([0,3]\) after the fifth step, and the RD-cost of all CUs in each mode is calculated through traversal to determine the final CTU division result. 7.