Research on joint coding for underwater single-photon video communication

Dai Weihui; Yan Qiurong; Wang Ming; Hong Zhu; Yang Cheng

doi:10.12086/oee.2021.200327

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Dai W H, Yan Q R, Wang M, et al. Research on joint coding for underwater single-photon video communication[J].Opto-Electron Eng, 2021, 48(5): 200327. doi: 10.12086/oee.2021.200327

Citation:

Dai W H, Yan Q R, Wang M, et al. Research on joint coding for underwater single-photon video communication[J]. Opto-Electron Eng, 2021, 48(5): 200327. doi: 10.12086/oee.2021.200327

Research on joint coding for underwater single-photon video communication

School of Information Engineering, Nanchang University, Nanchang, Jiangxi 330031, China

Fund Project: National Natural Science Foundation of China (61865010, 61565012) and Funding Scheme to Outstanding Young Talents of Jiangxi Province (20171BCB23007)

More Information

Corresponding author: Yan Qiurong, E-mail: yanqiurong@ncu.edu.cn

Received Date 04 September 2020

Revised Date 13 January 2021

Published Date 15 May 2021

Abstract

Abstract

In order to achieve effective and reliable video transmission, a video joint coding scheme based on dictionary learning and the concatenation of LT code and LDPC code is proposed for underwater single-photon communication system. Sparse coding based on dictionary learning greatly compresses the amount of video data. According to the deletion characteristic of underwater single-photon channel, using the LT-LDPC channel concatenated coding method can overcome the disadvantage of excessive decoding overhead of LT code. Aiming at the problem of decoding failure probability of LT coding, a double feedback mechanism for decoding success is proposed. The experimental results show that when the channel error rate is in the order of 10^-2 and the video compression rate is 75.6%, the video frames can be reconstructed with an average peak signal-to-noise ratio (PSNR) of 37.4921 dB.
- underwater single-photon video communication /
- dictionary learning /
- joint coding /
- LT coding /
- LDPC coding

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References

[1]	Zeng Z Q, Fu S, Zhang H H, et al. A survey of underwater optical wireless communications[J]. IEEE Commun Surv Tut, 2017, 19(1): 204-238. doi: 10.1109/COMST.2016.2618841 CrossRef Google Scholar
[2]	Kaushal H, Kaddoum G. Underwater optical wireless communication[J]. IEEE Access, 2016, 4: 1518-1547. doi: 10.1109/ACCESS.2016.2552538 CrossRef Google Scholar
[3]	Mendez P A, James R. A comparative study of underwater wireless optical communication for three different communication links[J]. IOSR J Electron Commun Eng, 2015, 10(3): 40-48. Google Scholar
[4]	Arnon S. Underwater optical wireless communication network[J]. Opt Eng, 2010, 49(1): 015001. doi: 10.1117/1.3280288 CrossRef Google Scholar
[5]	Haltrin V I. One-parameter two-term Henyey-Greenstein phase function for light scattering in seawater[J]. Appl Opt, 2002, 41(6): 1022-1028. doi: 10.1364/AO.41.001022 CrossRef Google Scholar
[6]	Zhang J, Si-Ma L H, Wang B Q, et al. Low-complexity receivers and energy-efficient constellations for SPAD VLC systems[J]. IEEE Photon Technol Lett, 2016, 28(17): 1799-1802. doi: 10.1109/LPT.2016.2572300 CrossRef Google Scholar
[7]	Ji Y W, Wu G F, Wang C, et al. Experimental study of SPAD-based long distance outdoor VLC systems[J]. Opt Commun, 2018, 424: 7-12. doi: 10.1016/j.optcom.2018.04.008 CrossRef Google Scholar
[8]	Wang C, Yu H Y, Zhu Y J, et al. Experimental study on SPAD-based VLC systems with an LED status indicator[J]. Opt Express, 2017, 25(23): 28783-28793. doi: 10.1364/OE.25.028783 CrossRef Google Scholar
[9]	Yan Q R, Li Z H, Hong Z, et al. Photon-counting underwater wireless optical communication by recovering clock and data from discrete single photon pulses[J]. IEEE Photon J, 2019, 11(5): 7905815. Google Scholar
[10]	Huang X X, Wang Z X, Shi J Y, et al. 1.6 Gbit/s phosphorescent white LED based VLC transmission using a cascaded pre-equalization circuit and a differential outputs PIN receiver[J]. Opt Express, 2015, 23(17): 22034-22042. doi: 10.1364/OE.23.022034 CrossRef Google Scholar
[11]	卓力, 张菁, 李晓光. 新一代高效视频编码技术[M]. 北京: 人民邮电出版社, 2013: 24-33. Google Scholar Zhuo L, Zhang J, Li X G. High Efficiency Video Coding Techiques for Next Generation[M]. Beijing: Posts & Telecom Press, 2013: 24-33. Google Scholar
[12]	Marpe D, Wiegand T, Sullivan G J, et al. The H. 264/MPEG4 advanced video coding standard and its applications[J]. IEEE Commun Mag, 2006, 44(8): 134-143. doi: 10.1109/MCOM.2006.1678121 CrossRef Google Scholar
[13]	Robinson B S, Kerman A J, Dauler E A, et al. 781 Mbit/s photon-counting optical communications using a superconducting nanowire detector[J]. Opt Lett, 2006, 31(4): 444-446. doi: 10.1364/OL.31.000444 CrossRef Google Scholar
[14]	Hiskett P A, Lamb R A. Underwater optical communications with a single photon-counting system[J]. Proc SPIE, 2014, 9114: 91140P. Google Scholar
[15]	韩彪, 赵卫, 汪伟, 等. 面向水下应用的改进型光子计数通信方法[J]. 光学学报, 2016, 36(8): 0806004. Google Scholar Han B, Zhao W, Wang W, et al. Modified photon counting communication method for underwater application[J]. Acta Opt Sin, 2016, 36(8): 0806004. Google Scholar
[16]	何小梅, 李晓峰, 张冬云, 等. 高效纠错编码技术在无线光通信中的性能分析[J]. 光子学报, 2008, 37(12): 2427-2429. Google Scholar He X M, Li X F, Zhang D Y, et al. Performance for efficient error correction coding in wireless optical communication[J]. Acta Photonica Sinica, 2008, 37(12): 2427-2429. Google Scholar
[17]	Irannejad M, Mahdavi-Nasab H. Low bit-rate SNR scalable video coding based on overcomplete dictionary learning and sparse representation[J]. Multidimens Syst Signal Proc, 2020, 31(2): 465-489. doi: 10.1007/s11045-019-00671-6 CrossRef Google Scholar
[18]	郭继昌, 金卯亨嘉. 一种基于字典学习的压缩感知视频编解码模型[J]. 数据采集与处理, 2015, 30(1): 59-67. Google Scholar Guo J C, Jin M H J. Dictionary learning-based compressive video sensing codec model[J]. J Data Acquisit Proc, 2015, 30(1): 59-67. Google Scholar
[19]	Aharon M, Elad M, Bruckstein A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Trans Signal Proc, 2006, 54(11): 4311-4322. doi: 10.1109/TSP.2006.881199 CrossRef Google Scholar
[20]	Chen H W, Kang L W, Lu C S. Dictionary learning-based distributed compressive video sensing[C]//28th Picture Coding Symposium, Nagoya, Japan, 2010: 210-213. Google Scholar
[21]	Sun Y P, Xu M, Tao X M, et al. Online dictionary learning based intra-frame video coding via sparse representation[C]//The 15th International Symposium on Wireless Personal Multimedia Communications, Taipei, Taiwan, 2012: 16-20. Google Scholar
[22]	Xiong H K, Pan Z M, Ye X W, et al. Sparse spatio-temporal representation with adaptive regularized dictionary learning for low bit-rate video coding[J]. IEEE Trans Circuits Syst Video Technol, 2013, 23(4): 710-728. doi: 10.1109/TCSVT.2012.2221271 CrossRef Google Scholar
[23]	荣子莉, 井帅奇, 洪劲松. 自由空间光通信中的数字喷泉码方法[J]. 湖南科技大学学报(自然科学版), 2017, 32(4): 54-60. Google Scholar Rong Z L, Jing S Q, Hong J S. Research on digital fountain code method of free space optical communication[J]. J Hunan Univ Sci Technol (Nat Sci Ed), 2017, 32(4): 54-60. Google Scholar
[24]	Prakash G, Kulkrni M, Sripati U, et al. Performance analysis of free space optical Links Encoded Using Luby Transform code[C]//2012 International Conference on Communication, Information & Computing Technology (ICCICT), Mumbai, India, 2012: 1-6. Google Scholar
[25]	陈丹, 柯熙政. 基于LDPC码的无线光通信副载波误码性能分析[J]. 激光技术, 2011, 35(3): 388-390, 402. doi: 10.3969/j.issn.1001-3806.2011.03.026 CrossRef Google Scholar Chen D, Ke X Z. Analysis on error rate of wireless optical communication using subcarrier modulation on LDPC code[J]. Laser Technology, 2011, 35(3): 388-390, 402. doi: 10.3969/j.issn.1001-3806.2011.03.026 CrossRef Google Scholar
[26]	林永照, 吴成柯, 刘薇. LT码和q-LDPC码级联方案在深空通信中的应用[J]. 电子与信息学报, 2010, 32(8): 1898-1903. Google Scholar Lin Y Z, Wu C K, Liu W. Application of the concatenation of q-LDPC and luby transform codes in deep communications[J]. J Electron Inform Technol, 2010, 32(8): 1898-1903. Google Scholar
[27]	柯熙政, 殷致云. 无线激光通信系统中的编码理论[M]. 北京: 科学出版社, 2009: 188-226. Google Scholar Ke X Z, Yin Z Y. Coding Theory in Wireless Laser Communication System[M]. Beijing: Science Press, 2009: 188-226. Google Scholar
[28]	Hong Z, Yan Q R, Li Z H, et al. Photon-counting underwater optical wireless communication for reliable video transmission using joint source-channel coding based on distributed compressive sensing[J]. Sensors, 2019, 19(5): 1042-1053. doi: 10.3390/s19051042 CrossRef Google Scholar
[29]	王新泽. LT码的度分布设计及译码算法研究[D]. 西安: 西安电子科技大学, 2014: 1-75. Google Scholar Wang X Z. Research on LT codes degree distribution optimization and decoding algorithms[D]. Xi'an: Xidian University, 2014: 1-75. Google Scholar
[30]	Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Trans Inform Theory, 2007, 53(12): 4655-4666. doi: 10.1109/TIT.2007.909108 CrossRef Google Scholar
[31]	李艳子. 超强前向纠错技术在100Gb/s WDM系统中的应用与研究[D]. 武汉: 武汉邮电科学研究院, 2012: 1-67. Google Scholar Li Y Z. Applications and research on SFEC for 100Gb/s WDM system[D]. Wuhan: Wuhan Research Institute of Posts and Telecommunications, 2012: 1-67. Google Scholar
[32]	张勋, 曹阳, 彭小峰, 等. 自由空间光通信中LT码的编码技术研究[J]. 半导体光电, 2017, 38(2): 242-245. Google Scholar Zhang X, Cao Y, Peng X F, et al. Research on coding technology of LT code in free space optical communication[J]. Semicond Optoelectron, 2017, 38(2): 242-245. Google Scholar

Overview

Overview

Overview: In recent years, underwater wireless optical communication has played an important role in environmental inspection, marine exploration fields, etc., and has received more and more attention. It has the advantages of high bandwidth, fast speed, strong anti-electromagnetic interference ability, etc. Because the video can convey information intuitively, the transmission of video in underwater wireless optical communication has become a research hotspot. In order to achieve effective and reliable video transmission, a video joint coding scheme based on dictionary learning and concatenation of LT code and LDPC code is proposed for underwater single-photon communication system. The scheme improves the data compression rate and data coding efficiency, and achieves the optimal synchronization of transmission quality and transmission efficiency. Aiming at the problem of large amount of video data, the dictionary learning sparse coding method is used, and each frame of video image transmits the sparse matrix information, and the decoding of each video frames does not affect each other. This method greatly reduces the amount of video data and improves the effectiveness of the communication system. In view of the deletion characteristic of underwater single-photon channel, LT-LDPC channel concatenated coding method is used. LT digital fountain code is specially designed to deal with various deletion channels. As long as enough fountain packets are received, the data can be restored. However, since the decoding overhead is too large, erroneous symbols are corrected by cascading LDPC codes to reduce the decoding overhead. Through experiments, the influence of encoding method and decoding success judgment threshold on decoding overhead is analyzed. The results prove that cascaded coding can overcome the disadvantage of excessive decoding overhead of LT code. At the same time, the decoding overhead decreases with the increase of the judgment threshold of decoding success. Aim to solve the problem of decoding failure probability of LT code, a double feedback mechanism for decoding success is proposed, which can also realize the clear recovery of video frames in a communication environment with a high bit error rate. Experimental results show that as the judgment threshold of decoding success increases, the average PSNR of video frames gradually decreases. As the video compression rate increases, the average PSNR value becomes smaller. When the channel error rate is in the order of 10^-2 and the video compression rate is 75.6%, the video frames can be reconstructed with an average peak signal-to-noise ratio (PSNR) of 37.4921 dB.