# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 5 Discussion  
In our research, the characteristics of the SVSV are analyzed in detail, including the video image characteristics of the SVLC and the SIRC. Secondly, the compression experiment is carried out, and the computational complexity of each encoding link in the process is analyzed through the experiment, as well as the main reasons for the compression delay. The performance of applying HEVC technology to compress SVSV in different resolutions and scenes is summarized, and a deep learning based intra-frame coding delay optimization algorithm for SVSV is proposed, while the process of CTU division in HEVC is analyzed. A CU hierarchy based on SVSV is designed by combining the characteristics of SVLC and infrared video. The proposed deep learning-based CU segmentation prediction model for SVSV is built, and a CU segmentation data set based on SVSV is built by using the official HEVC testbed _HM_16.17 to encode a large number of collected high-definition shipboard vision sensor video sequences. The data set contains the CU partition information of the SVLC video and the infrared camera video under different QP values, then use the obtained samples to train the CU partition prediction model of the SVSV. The final results show that the performance of the algorithm proposed is better when compressing the SVSV. Under the premise of less impact on the overall clarity of the video, the total compression time is comparable to that of the high efficiency video coding official test platform _HM_16.17. Compared with the test platform _HM_16.17, the average reduction is about 45.49%.  
Finally, in order to intuitively reflect the performance difference between the proposed algorithm and the traditional method, and the general optimization algorithm [13] on compressed SVSV, it is chosen to visualize the results of compressed SVLC under the constraints of different QP values (22, 27, 32, 37), as shown in Figure 13. The three methods are represented with different colors. It can be seen that the R-D curve of the proposed method is not much different from that of _HM_16.17, and the results on the bit rate and PSNR are better than reference [13], indicating that the proposed algorithm performs well on compressed SVSV.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Video Type**} & \multirow{2}{*}{**Navigation Environment**} & \multirow{2}{*}{**BD-BR/\%**} & \multirow{2}{*}{**BD-PSNR/dB**} & \multicolumn{5}{c}{\(\Delta\)**T/\%**} \\ \cline{4-9}  & & & & **QP = 22** & **QP = 27** & **QP = 32** & **QP = 37** & **Average** \\ \hline Visible light & Achoring & 2.37 & \(-0.16\) & \(-48.22\) & \(-49.30\) & \(-50.62\) & \(-53.34\) & \(-50.37\) \\ camera video & Underway & 1.74 & \(-0.14\) & \(-50.85\) & \(-50.97\) & \(-52.26\) & \(-56.01\) & \(-52.52\) \\ (1920 \(\times\) 1080) & Port & 2.71 & \(-0.16\) & \(-47.09\) & \(-48.55\) & \(-49.36\) & \(-51.80\) & \(-49.20\) \\ Visible light & Achoring & 1.75 & \(-0.13\) & \(-40.51\) & \(-41.40\) & \(-45.00\) & \(-47.55\) & \(-43.62\) \\ camera video & Underway & 1.49 & \(-0.14\) & \(-41.10\) & \(-44.58\) & \(-46.76\) & \(-51.37\) & \(-45.95\) \\ (1280 \(\times\) 720) & Port & 2.24 & \(-0.15\) & \(-39.44\) & \(-40.33\) & \(-43.64\) & \(-45.90\) & \(-42.33\) \\ Infrared camera & Achoring & 1.73 & \(-0.12\) & \(-38.98\) & \(-41.67\) & \(-43.28\) & \(-45.03\) & \(-42.24\) \\ video (704 \(\times\) & Underway & 1.44 & \(-0.12\) & \(-40.65\) & \(-41.10\) & \(-43.53\) & \(-45.66\) & \(-42.74\) \\
576) & Port & 1.84 & \(-0.13\) & \(-36.01\) & \(-40.57\) & \(-41.96\) & \(-43.30\) & \(-40.46\) \\ \hline \hline \multicolumn{9}{c}{**Average**} & 1.92 & \(-0.14\) & \(-42.54\) & \(-44.27\) & \(-46.27\) & \(-48.88\) & \(-45.49\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of intra coding time-delay optimization algorithm for shipborne vision sensor video with different QPs.