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 sha...

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Autores principales:	Hongrui Lu, Yingjun Zhang, Zhuolin Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	shipborne radar digital video unmanned ship HEVC SCC deep learning delay of compression Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/df51090955d54ea7a965e38b2edfe414
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spelling	oai:doaj.org-article:df51090955d54ea7a965e38b2edfe4142021-11-25T18:05:01ZTime Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning10.3390/jmse91112792077-1312https://doaj.org/article/df51090955d54ea7a965e38b2edfe4142021-11-01T00:00:00Zhttps://www.mdpi.com/2077-1312/9/11/1279https://doaj.org/toc/2077-1312The 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.Hongrui LuYingjun ZhangZhuolin WangMDPI AGarticleshipborne radar digital videounmanned shipHEVCSCCdeep learningdelay of compressionNaval architecture. Shipbuilding. Marine engineeringVM1-989OceanographyGC1-1581ENJournal of Marine Science and Engineering, Vol 9, Iss 1279, p 1279 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	shipborne radar digital video unmanned ship HEVC SCC deep learning delay of compression Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581
spellingShingle	shipborne radar digital video unmanned ship HEVC SCC deep learning delay of compression Naval architecture. Shipbuilding. Marine engineering VM1-989 Oceanography GC1-1581 Hongrui Lu Yingjun Zhang Zhuolin Wang Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
description	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.
format	article
author	Hongrui Lu Yingjun Zhang Zhuolin Wang
author_facet	Hongrui Lu Yingjun Zhang Zhuolin Wang
author_sort	Hongrui Lu
title	Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
title_short	Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
title_full	Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
title_fullStr	Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
title_full_unstemmed	Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
title_sort	time delay optimization of compressing shipborne radar digital video based on deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/df51090955d54ea7a965e38b2edfe414
work_keys_str_mv	AT hongruilu timedelayoptimizationofcompressingshipborneradardigitalvideobasedondeeplearning AT yingjunzhang timedelayoptimizationofcompressingshipborneradardigitalvideobasedondeeplearning AT zhuolinwang timedelayoptimizationofcompressingshipborneradardigitalvideobasedondeeplearning
_version_	1718411651024158720

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

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