# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 2 Radar Digital Video Coding Depth Division Algorithm Based on Deep Learning
### Data Set Establishment  
In order to train the neural network model with sufficient samples, we collected a large number of high-definition shipborne radar digital video sequences having 30 fps at 1280 \(\times\) 1024 (5:4) and 1024 \(\times\) 1024 (1:1) resolutions. We regard the Y component of each complete CTU and its corresponding label as sample data, and establish a CTU division depth interval dataset based on the digital video of the shipborne radar. In order to speed up the production of datasets, we processed the shipborne radar digital video into 298 video sequences with a length of 10 s under different sailing environments. The specific parameter information is shown in Table 17. SCC official test platform SCM8.7 was used to encode the processed shipborne radar digital video sequence. In the configuration of the encoder, QP is set to the common values of 22, 27, 32, and 37 for the encoding test, and the default configuration of all-intra coding is adopted to save and record all Y components of CTU and division depth information, and the corresponding search depth interval label is set.  
After the dataset is constructed, three seed datasets are obtained by random sampling. The number of samples of the sub-datasets are shown in Table 17, in which the training set accounts for 80% of the total dataset, the verification set accounts for 10%, and the test set accounts for 10%. We consider that the content of the radar video is refreshed with the rotation period of antenna scanning, the period is long, and the inter-frame redundancy is large. Therefore, in order to avoid overfitting in the training stage due to the high repetitiveness of the data, frame level interval sampling (FLIS) was adopted in the data processing stage to enhance the difference between data. In the shipborne radar digital video with a frame of 30 fps used in this paper, the rotation period of the radar antenna scanning is about 2.5 s, and the scan line rotation in the echo area is about  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Depth Range of CTU (\(Range_{Dep}\))** & **Category Label (\(D_{x}\))** & **Depth Interval of CU Search** \\ \hline \(Range_{Dep}=[0]\) & \(D_{0}\) & None \\ \(Range_{Dep}=[1]\) & \(D_{1}\) & \([0,1]\) \\ \(Range_{Dep}=[2]\), \(Range_{Dep}=[1,\,2]\) & \(D_{2}\) & \([1,2]\) \\ \(Range_{Dep}=[1,\,3]\) & \(D_{3}\) & \([1,2,3]\) \\ \(Range_{Dep}=[3]\), \(Range_{Dep}=[2,\,3]\) & \(D_{4}\) & \([2,3]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Depth range classification of CTU.  
144\({}^{\circ}\) per second. In the two adjacent radar images, the angle of the scan line differs by about 4.8\({}^{\circ}\). Accordingly, during the process of making the training dataset, we chose to encode 10 frames of shipborne radar digital video and save the CTU division depth data of 1 frame of the radar image, and set the corresponding label \(D_{x}\) for training the network structure. For example, when the resolution of the shipborne radar digital video is 1280 \(\times\) 1024, each radar image will contain a total of 320 CTU to be encoded, which means that 9600 sample data will be generated each second. In addition, the CTU of the adjacent frame in the same situation may not change before the radar scan line is refreshed. Therefore, the data similarity of the adjacent radar image is fully decreased using FLIS.