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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2016

01.06.2016

Design of Bandwidth Efficient Compressed Sensing Based Prediction Measurement Encoder for Video Transmission in Wireless Sensor Networks

verfasst von: V. Angayarkanni, S. Radha

Erschienen in: Wireless Personal Communications | Ausgabe 3/2016

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Abstract

Real time transmission of image and video requires a high degree of processing and computing power. A new emerging technique called compressed sensing is used to address this issue and lower the sampling rate of signals. This paper presents an effective compressed sensing based prediction measurement (CSPM) encoder compatible for wireless multimedia sensor networks. CSPM encoding focuses on a significant reduction in data storage and saving in transmission energy. The compression performance of CSPM method is evaluated using metrics such as compression ratio and bit rate. The video is reconstructed by the orthogonal matching pursuit algorithm. The recovered video quality is analyzed by peak signal to noise ratio and structural similarity index. The transmission of encoded data is tested in real time environment using Telos B motes. The experimental results show that the CSPM encoding technique is able to deliver the video at good quality and achieve a high compression ratio of 90.7 % compared to conventional encoders.
Vorheriger Artikel A Localization Scheme for Underwater Sensor Networks Without Time Synchronization
Nächster Artikel Optimal Coherent Combining Scheme for Relay Networks

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Literatur
1.
Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2007). A survey on wireless multimedia sensor networks. Computer Networks, 51(4), 921–960.CrossRef
2.
Candes, E. J. (2006). Compressive sampling. In Proceedings of the international congress of mathematicians, Madrid, Spain. European Mathematical Society.
3.
Candes, E., Braun, N., & Wakin, M. (2007). Sparse signal and image recovery from compressive samples. In 4th IEEE International symposium on biomedical imaging: from nano to macro, 2007 (pp. 976–979), 12–15. ISBI 2007.
4.
Wang, X., Zhao, Z., Zhao, N., & Zhang, H. (2010). On the application of compressed sensing in communication networks. In 5th international ICST conference on communications and networking in China (CHINACOM), 2010 (pp. 1–7), 25–27.
5.
Cai, T. T., & Wang, L. (2011). Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Transactions on Information Theory, 57(7), 4680–4688.MathSciNetCrossRef
6.
7.
Pudlewski, S., & Melodia, T. (2010). A distortion-minimizing rate controller for wireless multimedia sensor networks. Computer Communications, 33(12), 1380–1390.CrossRef
8.
Pudlewski, S., & Melodia, T. (2013). A tutorial on encoding and wireless transmission of compressively sampled videos. IEEE Communications Surveys and Tutorials, 15(2), 754–767.CrossRef
9.
Pudlewski, S., & Melodia, T. (2010). On the performance of compressive video streaming for wireless multimedia sensor networks In IEEE international conference communications (ICC) (pp. 1–5), 23–27.
10.
Loganathan, A., Hemalatha, R., & Radha, S. (2013). Comparison of encoding techniques for transmission of image data obtained using compressed sensing in wireless sensor networks. In Recent trends in information technology (ICRTIT), 2013 international conference on IEEE.
11.
Xiaochun, X., & Lingjuan, Y. (2009), A new video codec based on compressed sensing. In 2nd international congress on image and signal processing, 2009. CISP’09 (pp. 1–5), 17–19. doi:10.​1109/​CISP.​2009.​5304399.
12.
Mashud Hyder, M., & Mahata, K. (2009). A scalable distributed video coder using compressed sensing. In India conference (INDICON), 2009 annual IEEE. IEEE.
13.
Xiang, S., & Cai, L. (2011). Scalable video coding with compressive sensing for wireless videocast. In IEEE international conference on communications (ICC), 2011. IEEE.
14.
Xiaochun, X., et al. (2009). Fast encoding of video based on compressive sensing. In IEEE youth conference on information, computing and telecommunication, 2009. YC-ICT’09. IEEE.
15.
Imran, N., Seet, B.-C., & Fong, A. C. M. (2011). Performance analysis of video encoders for wireless video sensor networks. In IEEE Pacific Rim conference on communications, computers and signal processing (PacRim).
16.
Hou, Y., & Liu, F. (2011). A low-complexity video coding scheme based on compressive sensing. Fourth international symposium on computational intelligence and design (ISCID), 2011 (Vol. 2).
17.
Candes, E. J., & Wakin, M. B. (2008). An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), 21–30.CrossRef
18.
19.
Lee, H. N. (2011). Introduction to compressed sensing (Lecture notes). Spring Semester.
20.
Ezhilarasan, M., Thambidurai, P., Praveena, K., Srinivasan, S., & Sumathi, N. (2007). A new entropy encoding technique for multimedia data compression. In International conference on computational intelligence and multimedia applications (Vol. 4, pp. 157–161), 13–15.
21.
22.
Sayood, K. (2012). Introduction to data compression (3rd ed.). Amsterdam: Elsevier.MATH
23.
Karahanouglu, N. B., & Erdogan, H. (2012). A* Orthogonal matching pursuit: Best first search for compressed sensing signal recovery. Digital Signal Processing, 22, 555–568.MathSciNetCrossRef
24.
Cei, T. T., & Wang, L. (2011). Orthogonal matching pursuit for sparse signal recovery with noise. In Proceedings of IEEE transactions on information theory (Vol. 57, No. 7).
25.
Aasha, N. S., Radha, S., Reshma, M., Hariraman, S., & Swathi, P. (2014). Video compressed sensing framework for wmsn using a combination of matrices. In international conference on next generation computing and communication technologies (ICNGCCT 2014), Dubai.
26.
27.
Pudlewski, S., Prasanna, A., & Melodia, T. (2012). Compressed-sensing-enabled video streaming for wireless multimedia sensor networks. IEEE Transactions on Mobile Computing, 11(6), 1060–1072.CrossRef
28.
Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.CrossRef
29.
Chikkerur, S., Sundaram, V., Reisslein, M., & Karam, L. J. (2011). Objective video quality assessment methods: A classification, review, and performance comparison. IEEE Transactions on Broadcasting, 57(2), 165–182.CrossRef
30.
Yunus, F., et al. (2013) Optimum parameters for mpeg-4 data over wireless sensor network. International Journal of Engineering and Technology (0975–4024) 5.5.
31.
Silveira, D., et al. (2014) Reference frame context-adaptive variable-length coder: A real-time hardware-friendly approach for lossless external memory bandwidth reduction in current video coding systems. Journal of Real-Time Image Processing 1–17. doi:10.​1007/​s11554-014-0443-9.
32.
33.
Metadaten
Titel
Design of Bandwidth Efficient Compressed Sensing Based Prediction Measurement Encoder for Video Transmission in Wireless Sensor Networks
verfasst von
V. Angayarkanni
S. Radha
Publikationsdatum
01.06.2016
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2016
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-016-3176-1

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