Abstract
The advancement in the development of sensor nodes interms of acquiring multimedia data has fostered the applicability of Wireless Multimedia Sensor Network (WMSN) into various domains like surveillance and area monitoring. The energy consumption of such network is much higher than the conventional WSN due to the introduction of multimedia data to the network. Thus image compression, as energy optimized multimedia data processing and transmission technique, is critical required to prolong the life time of the network. In this paper, we evaluated the performance of three image compression algorithms (SPIHT, EZW, and WDR) with wavelet transforms (Haar, Daubechies, and biorthogonal) using three standard images. The compression techniques performance is measured by CR, PSNR, and MSE. The experiment is carried out in MATLAB. The experimental results i.e., visual and quantitative, show the supremacy of SPIHT algorithm over the rest of compression techniques explained.
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References
Ang, L., Seng, K.P., Chew, L.W, Yeong, L.S, Chia, W.C.: Wireless Multimedia Sensor Networks on Reconfigurable Hardwares, XXI 283, p. 73. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38203-1
Eldin, H.Z., Elhosseini, M.A., Ali, H.A.: Image compression algorithms in wireless multimedia sensor networks: a survey. Ain Shams Eng. J. 6(2), 481–490 (2015)
Ma, T., Michael, H., Peng, D., Sharif, H.: A survey of energy-efficient compression and communication techniques for multimedia in resource constrained systems. Faculty Publications from the Department of Electrical and Computer Engineering, p. 293 (2013)
Al-Shereefi, N.M.: Image compression using wavelet transform. J. Univ. Babylon 21(5), 1784–1793 (2013)
Chowdhury, M.M.H., Khatun, A.: Image compression using discrete wavelet transform. IJCSI Int. J. Comput. Sci. Issues 9(4) (2012)
Singh, P., Singh, P., Sharma, R.K.: JPEG image compression based on biorthogonal, coiflets and daubechies wavelet families. Int. J. Comput. Appl. (0975–8887) 13(1) (2011)
Zabala, A., Pons, X.: Impact of lossy compression on mapping crop areas from remote sensing. Int. J. Remote Sens. 34(8), 2796–2813 (2013)
Mammeri, A., Hadjou, B., Khoumsi, A.: A survey of image compression algorithms for visual sensor networks. Int. Sch. Res. Netw. ISRN Sens. Netw. Article ID 760320 (2012)
Shapiro, J.M.: Embedded image coding using zero trees of wavelet coefficients. IEEE Trans. Signal Process. 41(12), 3445–3462 (1993)
Jai, A., Potnis, A.: Wavelet based video compression using STW, 3D-SPIHT and ASWDR techniques: a comparative study. Int. J. Adv. Eng. Technol. 3(2), 224–234 (2012)
Gupta, D., Choubey, S.: Discrete wavelet transform for image processing. Int. J. Emerg. Technol. Adv. Eng. 4(3), 598–602 (2015)
Bavarva, A.A., Jani, P.: Introduction to wireless multimedia sensor network. Electronics For You (2015)
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Genta, A., Lobiyal, D.K. (2018). Performance Evaluation of Wavelet Based Image Compression for Wireless Multimedia Sensor Network. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_40
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DOI: https://doi.org/10.1007/978-981-13-1813-9_40
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