# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 4 Time Delay Optimization of Compressing SVSV Based on Deep Learning
### Establishment of Data Sets  
High-quality data sets are the basis for training and verifying the neural network model, and also provide a guarantee for improving the efficiency of the algorithm. The collected abundant high-definition SVLC video sequences and SIRC videos are processed firstly and sets the corresponding categorization label for training the network model. A large amount of CU division data provides support for training the SVSV CU division prediction model. Each sample data contains the Y component of the CU and the corresponding 0 and 1 division labels. All SVLC video samples constitute a CU division data set. All SIRC video samples together constitute a CU division data set based on SIRC video.  
All video samples are compressed by HEVC official test platform _HM_16.17. On the encoder configuration, the QP is set to the general values of the coding test 22, 27, 32 and 37.  
Figure 10: CU hierarchy partition structure model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Size of CU** & **Depth of CU** & **Coordinates of CU** & **Condition of Division** & **Quantity of Division** \\ \hline \(64\times 64\) & 0 & \(\mathrm{D}(x)\) & None & 1 \\ \(32\times 32\) & 1 & \(\{\mathrm{D}(x,i)\}_{i=1}^{4}\) & \(\mathrm{D}\left(0\right)=1\) & 4 \\ \(16\times 16\) & 2 & \(\{\mathrm{D}(x,i,j)\}_{i,j=1}^{4}\) & \(\mathrm{D}\left(1,i\right)=1\) & 16 \\ \(8\times 8\) & 3 & None & \(\mathrm{D}\left(2,i,j\right)=1\) & 64 \\ \hline \end{tabular}
\end{table}
Table 5: Hierarchy partition structure classification of CU.  
The default configuration of the AI coding mode is adopted to save the Y component and division information of all CUs recorded. Due to the strong correlation between the video frames of the shipborne vision sensor, in order to avoid overfitting in the training phase due to high data repeatability, the frame level interval sampling (FLIS) method is used in the data processing phase to enhance the difference between the data. Considering that the frame rate of the SVLC video is different from that of the SIRC video, the CU division data of one frame image is selected for each encoded 10 frames when compressing the SVLC video, and the CU division data of one frame image is selected for each encoded 5 frames when compressing the SIRC video. The CU division data set of the SVLC video comes from 41 SVLC video sequences, totaling 2460 images. The CU division data set of SIRC video comes from 22 SIRC video sequences with a total of 3300 images. The specific parameter information of the data set is given in Table 6. After the production of the data set is completed, three seed data sets are obtained through random sampling, of which the training set accounts for 80% of the total data set, the validation set accounts for 10% of the total data set and the testing set accounts for 10% of the total data set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video Type** & **Number of Videos** & **(FLIS)** & **Size of CU** & **QP** & **Number of Samples** \\ \hline  & & & & 22 & \\  & & & 64 \(\times\) 64 & 27 & 2,534,400 \\  & & & & 37 & \\ Visible light & & & & 22 & \\ camera video & 22 & 1320 & 32