# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 2 Radar Digital Video Coding Depth Division Algorithm Based on Deep Learning
### Modeling of CTU Division Depth Interval  
The quad-tree dividing rules formulated by HEVC are also applicable to SCC. HEVC specifies a CTU of 64 \(\times\) 64 sized as the root node, and each leaf node of the CTU is called a CU. Each CTU is divided into four non-overlapping CUs; in turn, the size of the CU may be  
Figure 4: Intra-CTU partition of SCC. Figure 3: Intra-CTU partition of HEVC. Figure 2: CTU division of radar digital video: (**a**) the CTU partition results of HEVC; (**b**) the CTU partition results of SCC. 64 \(\times\) 64, 32 \(\times\) 32, 16 \(\times\) 16, or 8 \(\times\) 8. Each encoded CTU will have its own division method. A CTU may contain several CUs of different sizes, CUs of the same size, or only contain one CU; that is, the CTU is not divided (CU size = CTU size). The size of the set QP value also determines the degree of detail of the CU division. The smaller the QP value, the less the image distortion, and the more precise the CU division. The larger the QP value, the more the details of the image are lost, and the rougher the CU division. At present, SCC mainly relies on traversing all 64 \(\times\) 64 to 8 \(\times\) 8 CU divisions--a total of 85 CUs--and calculating the RD-cost to determine the best CTU division method. However, this exhaustive search mode generates a large number of redundant calculations. In order to reduce the coding delay caused by an exhaustive search, a CTU depth interval model based on radar images was established, as shown in Figure 11. In the CTU depth interval model designed by us, the CU of 64 \(\times\) 64 is defined as the CU of depth 0, 32 \(\times\) 32 is defined as the CU of depth 1, 16 \(\times\) 16 is defined as the CU of depth 2, and 8 \(\times\) 8 is defined as the CU of depth 3. Thus, there are four types of depths in the CTU. Each CTU can be regarded as a random permutation of CUs of different sizes, with a total of 85 permutations. In this paper, we use depth (x, y, z) to represent the depth intervals of different combination modes, where x, y, z are the depths of CUs of different sizes, and if the two are equal, then the same depth is taken. For example, Depth (0) indicates that the current CTU only contains CUs with a depth of 0, and Depth (3) indicates that the current CTU only includes CUs with a depth of 3. Depth (1,2) represents that the current CTU contains CUs with the depth of 1,2. Depth (1,2,3) indicates the current CTU has a maximum depth of 3 and the minimum depth is 1, including CUs with depths of 1, 2, and 3. The CTU dividing results of many coded radar videos show that the CTU dividing depth directly affects the selection of IBC and PLT modes by CU. Therefore, in order to train the neural network model more conveniently and avoid erroneously skipping the IBC/PLT mode when encoding the radar image, thus causing the loss of coding performance, we classify the depth interval of CTU, as shown in Equations (1) and (2). In addition, we designed the corresponding CU search depth interval \(D_{x}\), as shown in Table 11, with five  
Figure 5: CTU depth interval model. total labels. For example, when \(D_{0}\) is the maximum value of prediction, it indicates that the prediction depth of the current CTU is 0, and the downward division is no longer continued. If the value of \(D_{2}\) is maximum, it means that there is a CU with a depth of 2 in the current CTU, and CU with a depth of 1 may exist, so the search depth interval of the CUs is limited to [1, 2]. If \(D_{3}\) is the maximum value of prediction, it shows that the prediction depth interval of the current CTU is [1, 3], and the search mode is limited to CUs with depths of 1, 2, and 3. By using the neural network to predict the current CTU, the probability that the CU belongs to these five search depth intervals is obtained.