# Time Delay Optimization of Compressing Shipborne Vision Sensor Video Based on Deep Learning
###### Abstract
Reference [9] proposed a fast CU division algorithm based on machine learning, and designs an algorithm for CU division prediction using online Support Vector Machine (SVM), and another CNN-based CU division prediction algorithm named DeepCNN. The authors compare the two algorithms laterally and proved that the CNN method is more effective in reducing the computational complexity of HEVC. Reference [10] designed a variable-filter-size Residue-learning CNN (VRCNN) and proposed a CNN-based post-processing algorithm for HEVC. To solve the problem that the traditional rate distortion cost calculation is too complicated during Coding Tree Unit (CTU) division, reference [11] proposed a low-complex shallow asymmetric-kernel CNN for intra-frame prediction of patterns and designed an HEVC intra-frame coding fast learning framework. Reference [12] proposed a combined CNN and LSTM structure for CU segmentation prediction, where the LSTM network is responsible for solving the time-domain correlation in the CU segmentation process. The scheme solves the problem of overly complex recursive CU segmentation search based on quadtrees in the CTU segmentation process to a certain extent and significantly reduces the coding complexity of HEVC. Reference [13] proposed a CNN-based intra-frame segmentation decision network, and another CNN combined with LSTM for inter-frame segmentation decision network, which enables the use of deep learning methods instead of CU segmentation by predicting the intra-frame and inter-frame CU segmentation results while establishing a large coding test universal sequence CU segmentation data set for open use. In references [14; 15; 16], a CNN-based PU angle prediction model, named AP-CNN, was proposed to replace the original PU angle prediction in the HEVC lossless coding model. In the literature [16], an optimized architecture LAP-CNN based on AP-CNN was designed to further reduce the complexity of the model. In reference [17], the authors combined CNN with image recovery techniques for the loop filtering segment in HEVC to improve the overall performance of coding. In reference [18], CNNs were used to filter the video luminance and chrominance components separately in the intra-frame coding mode instead of the traditional loop filtering mode. Reference [19] introduced CNN into inter-frame coding and designed a block-level up/down sampling model to improve the interframe codingperformance of HEVC. To reduce the distortion of video image compression at low bit rates, reference [20] designed a quality-enhanced convolutional neural network (QE-CNN) and proposed a time-constrained quality-enhanced optimization (TQEO) scheme based on HEVC without any modification of the HEVC encoder, and the results of the intra-frame coding test also proved the effectiveness of the scheme. In reference [21], in order to ensure stable transmission in unstable network environments after video compression, a neural network-based low-complexity fault-tolerant coding algorithm was designed to improve HEVC coding efficiency while reducing the bit error rate, and named LC-MSRE. With the popularity of HD 3D video, ISO and ITU jointly introduced the 3D-HEVC technology that can support 3D video compression, as an extension of HEVC standard, 3D-HEVC adds depth mapping coding techniques. In reference [22], based on 3D-HEVC, a CNN technology was used to reduce the computational complexity of 3D video compression, and a depth edge classification CNN (DEC-CNN) framework was designed to classify the depth map edges. In reference [23], the researchers designed a LeNet-5-based CNN optimization model with an early termination CU division strategy to reduce the computational complexity of Rate Distortion cost (RD cost) in intra-frame prediction. In addition to the above approaches that focus on CU division decisions to reduce HEVC computational complexity, some researchers have considered the relationship between CTU division depth and HEVC computational complexity.