# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 4 Time Delay Optimization of Compressing SVSV Based on Deep Learning
### Analysis of Experimental Results  
In order to verify the effectiveness of the intra-frame encoding delay optimization algorithm for reducing the compression delay of the shipboard vision sensor video, this paper uses Tensorflow 1.13.0 to build a convolutional neural network model, embeds the trained model into the HEVC dedicated testbed _HM_16.17 and compiles and tests it in Ubuntu OS, the hardware configuration of the test environment is the same as Section 3.2. The hardware configuration of the test environment is the same as in Section 3.2. For the encoder configuration, the AI default configuration was used for encoding. The test set contains shipboard vision sensor videos in three different navigation scenarios and at different resolutions. In this paper, the coding-saving ratio \(\Delta T\) and the BD-BR and the BD-PSNR of the VCED-M33 proposal are used as metrics to evaluate the effectiveness of the algorithm. The smaller the value of BD-BR, the lower the bit rate of the compressed video, and the larger the value of PSNR, the higher the quality of the compressed video. The smaller the value of \(\Delta T\), the less time consuming the encoding is. Equation (16) lists the calculation method of \(\Delta T\). BD-PSNR is the same as PSNR calculation method, as shown in Equation (5).  
\[\Delta T=\frac{1}{n}\sum_{QP}\frac{T_{HM16.17}-T_{prop}}{T_{HM16.17}}\times 100\% \tag{16}\]  
where \(T_{HM16.17}\) represents the encoding elapsed time of encoder _HM_16.17, \(T_{prop}\) represents the encoding elapsed time of the methods in this section and n is the total number of encodings.  
The results show that the intra-frame coding delay optimization algorithm proposed reduces the total compression time by about 45.49% on average, BD-BR by 1.92% on average, and BD-PSNR by 0.14 dB on average compared with _HM_16.17 in AI coding mode. Among them, the compression time decreases by about 50.70% on average when compressing the SVLC video with the resolution of 1920 \(\times\) 1080 and by about 43.97% on average when compressing the SVLC video with the resolution of 1280 \(\times\) 720. This fully illustrates that the higher the resolution of the SVSV, the better the adaptation of the intra-frame coding delay optimization algorithm for SVSV proposed and the stronger the compression performance.  
From Table 7, it can also be analyzed that the setting of QP value is also a major factor limiting the performance of the intra-frame coding delay optimization algorithm for the SVSV. The larger the QP value set, the higher depth CTUs and the coarser the CU division. For example, when compressing a SIRC video with a resolution of \(704\times 576\), the average compression savings ratio at a QP value of 22 is 38.55%. When the QP value is 37, the average compression savings ratio increases by nearly 6.11%, up to 44.66%. The performance of the algorithm is also affected by the actual content of the video when the QP value is certain, and the less targets in the video and the flatter the content, the better the algorithm compression performance. For example, when compressing a shipboard visible camera video with a resolution of 1280 \(\times\) 720 at a QP value of 32, the average compression time in the port-resting environment is 3.12% higher than in the sailing state and 1.36% higher than in the anchoring state. It can be concluded from the above analysis that the intra-frame coding delay optimization algorithm proposed largely reduces the intra-frame PC computational complexity and has a higher performance in compressing the SVSV, especially in compressing the high-resolution SVSV, which significantly reduces the compression time.