# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
## 4 Time Delay Optimization of Compressing SVSV Based on Deep Learning
### CU Division Prediction Model for Shipboard Vision Sensor Video Based on Deep Learning  
CNN is an efficient prediction approach developed in recent years. The structure of CNN mainly consists of three parts: convolution layer, pooling layer and fully connected layer. CNN use a convolution kernel of certain size multiplied by the corresponding position of each layer to extract features, and the parameters in the convolution kernel are shared by weight in the feature map to continuously improve learning efficiency. According to the characteristics of shipboard vision sensor video, combined with the Convolutional Neural Network (CNN) [13], this paper proposes an intra-frame coding delay optimization algorithm for SVSV, designs a shipboard vision sensor video based on deep learning CU partition prediction model, and optimizes the complexity of the neural network model, adding a threshold termination mechanism, and the improved convolution network structure is shown in Figure 11.  
The convolution network structure is composed of two preprocessing layers, three convolutional layers, one pooling layer, one merging layer and three fully connected layers. Before performing PC, the encoder will first divide the visual sensor image into N CTUs of 64 \(\times\) 64 size. The preprocessing layer is responsible for extracting the brightness matrix containing the main visual information in the image as input information for the predictive model for division of the CU of the SVSV, and then, performing global homogenization, 32 \(\times\) 32 local homogenization, 16 \(\times\) 16 local homogenization and normalization to speed up the speed of gradient descent for optimal solutions. Convolutional layer: The convolutional layer is mainly responsible for extracting local features in the SVSV. The convolutional layer performs the convolution operation on the feature maps input to this layer, and in order to be able to obtain more features in the CU with low complexity, the convolution scheme in Inception Net [26] is referred to. By using convolutional kernels of different sizes to obtain different sizes of perceptual fields in CU, the features after the operation are higher level. Therefore, three convolutional kernels of different sizes will be used, such as 8 \(\times\) 8, 4 \(\times\) 4 and 2 \(\times\) 2 in the first convolutional layer to extract the low-level features of CU division on three branches respectively. In the second and third convolutional layers, 2 \(\times\) 2 convolutional kernels of the same size are used, which are responsible for extracting higher-level features on the three branches, and finally, 64 feature maps are obtained on the three branches simultaneously. In order to fit the mutual nonoverlapping rule of CU quadtree division specified by HEVC, the step  
Figure 11: CU partition prediction model of shipborne vision sensor video. length of all convolution operations is set to the kernel edge length for non-overlapping convolution operations. The merged feature vectors are processed by three fully connected layers in three branches in turn, including two hidden layers and one output layer, and the final output results are CU division prediction values. According to the experimental results in Section 3, it is known that QP is one of the main factors affecting the code rate and CU division size in the process of compressing the video of shipboard vision sensors. Therefore, the selection of QP value is added as a major external feature to the feature vectors of the first and second fully connected layers to improve the adaptability of the model under different QP values and the accuracy of predicting CU division. \[Leaky-ReLU(x)= \begin{cases}x&x>0\\ \alpha x&x\leq 0\end{cases}