Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning

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.

Fast ISP coding mode optimization algorithm based on CU texture complexity for VVC

In lately published video coding standard Versatile Video Coding (VVC/ H.266), the intra sub-partitions (ISP) coding mode is proposed. It is efficient for frames with rich texture, but less efficient for frames that are very flat or constant. In this paper, by comparing and analyzing the rate distortion cost (RD-cost) of coding unit (CU) with different texture features for using and not using ISP(No-ISP) coding mode, it is observed that CUs with simple texture can skip ISP coding mode. Based on this observation, a fast ISP coding mode optimization algorithm based on CU texture complexity is proposed, which aims to determine whether a CU needs to use ISP coding mode in advance by calculating CU texture complexity, so as to reduce the computation complexity of ISP. The experimental results show that under All Intra (AI) configuration, the coding time can be reduced by 7%, while the BD rate only increase by 0.09%.

Fast ISP Coding Mode Optimization Algorithm Based on CU Texture Complexity for VVC

In lately published video coding standard Versatile Video Coding (VVC/ H.266), the intra sub-partitions (ISP) coding mode is proposed. It is efficient for frames with rich texture, but less efficient for frames that are very flat or constant. In this paper, by comparing and analyzing the rate distortion cost (RD-cost) of coding unit (CU) with different texture features for using and not using ISP(No-ISP) coding mode, it is found that CUs with simple texture can get better coding performance in No-ISP coding mode. Based on this observations, a fast ISP coding mode optimization algorithm based on CU texture complexity is proposed, which aims to determine whether CU needs to use ISP coding mode in advance by calculating CU texture complexity, so as to reduce the computation complexity of ISP. The experimental results show that under All Intra (AI) configuration, the coding time can be reduced by 7%, while the BD-rate only increase by 0.09%.

A Fast Sample Adaptive Offset Algorithm for 360∘ Video

The increased demand for virtual reality brings more challenges to 360∘ video coding. 360∘ video needs to be projected as a planar video before encoding. This process will produce projection distortion. The degree of projection distortion depends on the location of the pixel. Traditional coding algorithms cannot respond to this feature efficiently enough. In this paper, a fast Sample Adaptive Offset (SAO) algorithm for 360∘ video is proposed. The proposed algorithm improves the SAO process. On the basis of retaining the whole SAO process, a simplified SAO process is added. First, the coding tree unit (CTU) of performing the simplified SAO process is filtered according to the rate distortion cost (RD-cost) of the intra- or inter-prediction and the location of the CTU. Subsequently, the CTU is sampled at intervals according to the equirectangular projection (ERP) characteristics, and the CTU of performing the simplified SAO process is determined. Experimental results show that the proposed algorithm achieves 60% time of SAO process reduction, with only 0.29% luma Bjontegaard delta rate (BD-rate) increases on average.