# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 4 Analysis of Experimental Result  
In order to verify the effectiveness of the proposed algorithm in reducing the time delay of compressing shipborne radar digital video, we embedded the trained model into the SCC dedicated test platform SCM8.7, and compiled and tested it on the 64-bit Ubuntu 20.04.2 LTS operating system. The hardware configuration of the test environment we built was an Intel Core i7-8700 [EMAIL_ADDRESS] GHz, the running memory was 16 G, and the neural network was constructed using Tensorflow. For the encoder configuration, the All Intra (AI) default configuration was used for coding. The test set contained shipborne radar digital video of four different ranges in various scenarios. The number of samples is shown in Table 7. The coding savings ratio \(\Delta T\), BD-BR, and BD-PSNR in the VCED-M33 proposal are taken as the evaluation indexes of algorithm effectiveness. The smaller the value of BD-BR, the lower the bit rate after compression. The greater the value of BD-PSNR, the higher the quality after compression. The smaller the value of \(\Delta T\), the less time it takes to encode. Equations (6) and (7) list the calculation methods of PSNR and \(\Delta T\). \[PSNR=10\cdot\lg\frac{M\cdot N\cdot A^{2}}{\sum_{n=0}^{N-1}\sum_{m=0}^{M-1} \left[f(m,n)-\hat{f}(m,n)\right]^{2}} \tag{6}\]  
\[\Delta T=\frac{1}{n}\sum_{QP}\frac{T_{SCMS,7}-T_{prop}}{T_{SCMS,7}}\times 100\% \tag{7}\]  
where \(f(m,n)\) and \(\hat{f}(m,n)\) are the images before and after encoding, \(M\) and \(N\) are the width and height of the image, and \(A\) is the maximum gray value of the image. \(T_{SCMS,7}\) represents the coding time of the encoder SCM8.7, \(T_{prop}\) represents the coding time of the method in this paper, and n is the total coding times. The experimental results are shown in Table 7 below. The results in Table 7 show that a time delay optimization algorithm of compressing shipborne radar digital video based on deep learning generally reduces the encoding delay by about 39.84%, whereas the BD-BR only increases by 2.26%. Although the value of BD-PSNR decreased about 0.19 dB, it does not affect the subjective perception. In mooring and port waters where the echo area is more complex, there are more CTUs with a high depth. For example, in the range between 1.5 and 3 NM, the increase in target objects affects the encoding speed to a certain extent. In a navigation area with a simple echo area, if the range is 6 or 12 NM, because there are fewer targets, most of which are flat background areas, the encoding speed is higher. In order to intuitively reflect the performance difference between the proposed algorithm and the traditional method in compressing the shipborne radar digital video, under the constraints of different QP values (22,27,32,37), we visualize the 3 NM range radar digital video results encoding port waters, as shown in Figure 7. The two approaches are represented with different colors. It can be seen that there is no significant difference between the R-D curve of the proposed method and that of SCM8.7, which indicates that the proposed algorithm performs well in terms of coding quality.