Abstract
The High Efficiency Video Coding Standard (HEVC) is one of the most advanced coding schemes at present, and its excellent coding performance is highly suitable for application in the navigation field with limited bandwidth. In recent years, the development of emerging technologies such as screen sharing and remote control has promoted the process of realizing the virtual driving of unmanned ships. In order to improve the transmission and coding efficiency during screen sharing, HEVC proposes a new extension scheme for screen content coding (HEVC-SCC), which is based on the original coding framework. SCC has improved the performance of compressing computer graphics content and video by adding new coding tools, but the complexity of the algorithm has also increased. At present, there is no delay in the compression optimization method designed for radar digital video in the field of navigation. Therefore, our paper starts from the perspective of increasing the speed of encoded radar video, and takes reducing the computational complexity of the rate distortion cost (RD-cost) as the goal of optimization. By analyzing the characteristics of shipborne radar digital video, a fast encoding algorithm for shipborne radar digital video based on deep learning is proposed. Firstly, a coding tree unit (CTU) division depth interval dataset of shipborne radar images was established. Secondly, in order to avoid erroneously skipping of the intra block copy (IBC)/palette mode (PLT) in the coding unit (CU) division search process, we designed a method to divide the depth interval by predicting the CTU in advance and limiting the CU rate distortion cost to be outside the traversal calculation depth interval, which effectively reduced the compression time. The effect of radar transmission and display shows that, within the acceptable range of Bjøntegaard Delta Bit Rate (BD-BR) and Bjøntegaard Delta Peak Signal to Noise Rate (BD-PSNR) attenuation, the algorithm proposed in this paper reduces the coding time by about 39.84%, on average, compared to SCM8.7.
Highlights
In recent years, various automation technologies centered on unmanned ships have developed rapidly
The Y component of the coding tree unit (CTU) is input into the trained prediction model—the network structure of the model is shown in Figure 7—and the pixel matrix is normalized to accelerate the convergence rate
According to a large number of experiments, we found that Quantization Parameter (QP) is the main factor affecting the bit rate and CTU depth division in the process of compressing the shipborne radar digital video
Summary
Various automation technologies centered on unmanned ships have developed rapidly. The observation of shipborne radar digital video is the most intuitive and efficient way to understand the navigation state of the target ship. As the basis of ship-to-shore intelligent information interaction, is a key technology of unmanned ship remote control. The. International Convention for the Safety of Life at Sea (SOLAS) requires that ships of 500 gross tonnage (GT) and above should be equipped with radar devices, and ships of. ]. The main data to be used in the future, such as driving video and radar digital images of unmanned ships, need to be compressed and transmitted to intelligent equipment for processing. Number of Sequences Frames Anchor Under way Port Samples in Frame 16 × 16 20 × 16 Raw Samples Amount
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
