# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 3 Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
### CTU Partition Deep Prediction Model Based on Deep Learning  
Based on the characteristics of shipborne radar digital video, we proposed a CTU division depth interval prediction model on account of deep learning, and optimize the complexity of the neural network model according to previous related work [??]. The improved fast convolutional network structure is shown in Figure 7.  
The structure of a neural network is composed of two preprocessing layers, two convolution layers, one pooling layer, one merging layer, and two fully connected layers. Firstly, the encoder divides a frame of the radar image into N CTUs having a size of 64 \(\times\) 64. The preprocessing layer is responsible for extracting the Y component of the CTU that contains the main visual information, which is used as the input information of the prediction model. Then, the Y component of the CTU is glob  
Figure 7: Design of the deep learning model. and normalized on three branches in order to speed up the gradient descent to obtain the optimal solution. Convolution layer: In order to obtain more features in the CTU with low complexity, we refer to the method in Inception Net [17]. By using different sizes of convolution kernels to obtain different sizes of receptive fields in CTU, the feature level is higher after operation. Therefore, we use convolution kernels with different sizes of 8 \(\times\) 8, 4 \(\times\) 4, and 2 \(\times\) 2 in the first convolution layer to extract the low-level features of CTU division depth on three branches. In the second convolution layer, the convolution kernel with a 4 \(\times\) 4 is used to simultaneously extract higher-level features on three branches, and finally 64 feature maps are obtained on three branches. In order to comply with the non-overlapping rule of CU quad-tree partition specified by HEVC, we set the stride of all convolution operations as the kernel edge length for non-overlapping convolution operation. Pooling layer: After the convolution operation of the first layer, the maximum pooling process with stride of 4 and size of 4 \(\times\) 4 is carried out on three branches to simplify the feature at the same time. Merging layer: The features after merging are composed of all the feature maps after the operation of the second convolution layer. All features after convolution operation are merged and converted into vectors, which is more helpful to obtain global and local features. Full connection layer: The merged feature vectors pass through three full connection layers successively, including two hidden layers and one output layer. The final output result is the probability of the corresponding label \(D_{0,1,2,3,4}\) of the current CTU, and the probability maximum \(D_{x}\) is the search depth interval of CU in the current CTU. According to a large number of experiments, we found that QP is the main factor affecting the bit rate and CTU depth division in the process of compressing the shipborne radar digital video. Therefore, we add the selection of QP value as a main external feature to the feature vector of the first and second fully connection layer to improve the adaptability of the model under different QP values and the accuracy of predicting the depth interval of CTU. In the training and test phases, in order to prevent the problem of gradient disappearance, all convolution layers and the first and second full connection layers are activated with the leaked linear unit (Leaky-ReLU), as shown in Equation (3). Leaky-ReLU, a variant of the modified linear unit (ReLU), solves the problem that neurons in the negative interval of the ReLU function do not learn by introducing a fixed slope \(\alpha\). The output layer is activated by the normalized exponential function (Softmax), as shown in Equation (4), where \(x_{i}\) represents the output value of the ith node, and n indicates the total number of output nodes. In the training stage, the cross-entropy function is used to calculate the predicted loss.