Bộ mã hóa hình ảnh dựa trên sóng tần số thấp hiệu quả về tính toán cho Mạng cảm biến đa phương tiện không dây/Mạng Internet vạn vật (WMSNs/IoT)

Multidimensional Systems and Signal Processing - Tập 34 - Trang 657-680 - 2023
Mohd Tausif1, Ekram Khan1, Antonio Pinheiro2,3
1Department of Electronics Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, India
2University of Beira Interior, Covilha, Portugal
3Instituto de Telecommunicacoes, University of Beira Interior, Covilha, Portugal

Tóm tắt

Bài báo này đề xuất một sự điều chỉnh đơn giản và hiệu quả đối với thuật toán mã hóa hình ảnh nhúng phân vùng bộ nhớ bằng không tiên tiến nhất (ZM-SPECK) nhằm giảm bớt độ phức tạp tính toán mà không làm tăng đáng kể bộ nhớ. Đã quan sát rằng việc so sánh từng yếu tố của các khối/tập với ngưỡng hiện tại trong mỗi mặt phẳng-bit là một trong những quá trình tốn thời gian trong thuật toán ZM-SPECK. Đóng góp chính của bài báo này là tránh quá trình phức tạp về mặt tính toán đó bằng cách sử dụng độ lớn của hệ số lớn nhất trong mỗi subband, được tìm kiếm và lưu trữ trong quá trình tính toán biến đổi Wavelet rời rạc (DWT), trước khi mã hóa. Hơn nữa, tỷ số đỉnh-tín hiệu trên nhiễu (PSNR) của kỹ thuật đề xuất chính xác giống như tỷ số thu được bởi ZM-SPECK. Các kết quả mô phỏng cho thấy phương pháp đề xuất có thể giảm độ phức tạp của ZM-SPECK khoảng 29%, khiến nó phù hợp cho các nút cảm biến bị hạn chế tài nguyên trong các mạng cảm biến đa phương tiện không dây (WMSNs), Internet vạn vật (IoT), mạng khu vực cơ thể, v.v.

Từ khóa

#Mã hóa hình ảnh #ZM-SPECK #Độ phức tạp tính toán #Mạng cảm biến đa phương tiện không dây #Internet vạn vật

Tài liệu tham khảo

Ahmad, I., Hee, L. M., Abdelrhman, A. M., Imam, S. A., & Leong, M. S. (2021). Challenges and approaches of energy harvesting for wireless sensor nodes in machine condition monitoring systems: A review. Measurement, 183, 109856. https://doi.org/10.1016/j.measurement.2021.109856 Akyildiz, I. F., & Vuran, M. C. (2010). Wireless sensor networks. West Sussex: Wiley. Averbuch, A., Neittaanmaki, P., Zheludev, V., Salhov, M., & Hauser, J. (2022). An hybrid denoising algorithm based on directional wavelet packets. Multidimensional Systems and Signal Processing, 1–33. Bao, Y., & Kuo, C. C. J. (2001). Design of wavelet-based image codec in memory-constrained environment. IEEE Transactions on Circuits and Systems for Video Technology, 11(5), 642–650. https://doi.org/10.1109/76.920193 Bhattar, R. K., Ramakrishnan, K., & Dasgupta, K. (2002). Strip based coding for large images using wavelets. Signal Processing: Image Communication, 17(6), 441–456. https://doi.org/10.1016/S0923-5965(02)00019-X Bose, A., & Maity, S. P. (2022). Secure sparse watermarking on DWT-SVD for digital images. Journal of Information Security and Applications, 68, 103255. https://doi.org/10.1016/j.jisa.2022.103255 Brahimi, T., Laouir, F., Boubchir, L., & Ali-Chérif, A. (2017). An improved wavelet-based image coder for embedded greyscale and colour image compression. AEU - International Journal of Electronics and Communications, 73, 183–192. https://doi.org/10.1016/j.aeue.2017.01.008 Chew, L. W., Ang, L.-M., & Seng, K. P. (2008). New virtual SPIHT tree structures for very low memory strip-based image compression. IEEE Signal Processing Letters, 15, 389–392. https://doi.org/10.1109/LSP.2008.920515 Chew, L. W., Chia, W. C., Ang, L.-M., & Seng, K. P. (2009). Very low-memory wavelet compression architecture using strip-based processing for implementation in wireless sensor networks. EURASIP Journal on Embedded Systems, 2009, 1–16. https://doi.org/10.1155/2009/479281 Chew, L. W., Chia, W. C., Ang, L.-M., & Seng, K. P. (2012). Low-memory video compression architecture using strip-based processing for implementation in wireless multimedia sensor networks. International Journal of Sensor Networks, 11(1), 33–47. https://doi.org/10.1504/IJSNET.2012.045033 Chia, W. C., Chew, L. W., Ang, L.-M., & Seng, K. P. (2012). Low memory image stitching and compression for WMSN using strip-based processing. International Journal of Sensor Networks, 11(1), 22–32. https://doi.org/10.1504/IJSNET.2012.045037 Chrysafis, C., Said, A., Drukarev, A., Islam, A., & Pearlman, W. A. (2000). SBHP-a low complexity wavelet coder (Vol. 6, pp. 2035–2038). Chrysafis, C., & Ortega, A. (2000). Line-based, reduced memory, wavelet image compression. IEEE Transactions on Image Processing, 9(3), 378–389. https://doi.org/10.1109/83.826776 Chung-Hsien, Y., Jia-Ching, W., Jhing-Fa, W., & Chang, C.-W. (2007). A block-based architecture for lifting scheme discrete wavelet transform. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 90(5), 1062–1071. https://doi.org/10.1093/ietfec/e90-a.5.1062 Czarlinska, A., & Kundur, D. (2009). Wireless image sensor networks: Event acquisition in attack-prone and uncertain environments. Multidimensional Systems and Signal Processing, 20(2), 135–164. Gnutti, A., Guerrini, F., Adami, N., Migliorati, P., & Leonardi, R. (2021). A wavelet filter comparison on multiple datasets for signal compression and denoising. Multidimensional Systems and Signal Processing, 32(2), 791–820. Guo, J., Mitra, S., Nutter, B., & Karp, T. (2006). A fast and low complexity image codec based on backward coding of wavelet trees (pp. 292–301). Hsiang, S.-T., & Woods, J. W. (2000). Embedded image coding using zeroblocks of subband/wavelet coefficients and context modeling (Vol. 3, pp. 662–665). Hu, C.-K., Yan, W.-M., & Chung, K.-L. (2004). Efficient cache-based spatial combinative lifting algorithm for wavelet transform. Signal Processing, 84(9), 1689–1699. https://doi.org/10.1016/j.sigpro.2004.05.014 Kidwai, N. R., Khan, E., & Reisslein, M. (2016). ZM-SPECK: A fast and memoryless image coder for multimedia sensor networks. IEEE Sensors Journal, 16(8), 2575–2587. https://doi.org/10.1109/JSEN.2016.2519600 Kouadria, N., Mechouek, K., Harize, S., & Doghmane, N. (2019). Region-of-interest based image compression using the discrete tchebichef transform in wireless visual sensor networks. Computers & Electrical Engineering, 73, 194–208. https://doi.org/10.1016/j.compeleceng.2018.11.010 Kulalvaimozhi, V., Alex, M. G., & Peter, S. J. (2020). A novel homomorphic encryption and an enhanced DWT (NHE-EDWT) compression of crop images in agriculture field. Multidimensional Systems and Signal Processing, 31(2), 367–383. Latte, M. V., Ayachit, N. H., & Deshpande, D. (2006). Reduced memory listless speck image compression. Digital Signal Processing, 16(6), 817–824. https://doi.org/10.1016/j.dsp.2006.06.001 Lin, W. K., & Burgess, N. (1998). Listless zerotree coding for color images (Vol. 1, pp. 231–235). Loganathan, S., & Arumugam, J. (2020). Energy centroid clustering algorithm to enhance the network lifetime of wireless sensor networks. Multidimensional Systems and Signal Processing, 31(3), 829–856. Mahdavinejad, M. S., et al. (2018). Machine learning for internet of things data analysis: A survey. Digital Communications and Networks, 4(3), 161–175. https://doi.org/10.1016/j.dcan.2017.10.002 Matheen, M., & Sundar, S. (2022). IoT multimedia sensors for energy efficiency and security: A review of QoS aware and methods in wireless multimedia sensor networks. International Journal of Wireless Information Networks, 1–12. Meraj, Y., & Khan, E. (2021). Modified ZM-SPECK: A low complexity and low memory wavelet image coder for VS/IoT nodes (pp. 494–500). Moin, A., Thielens, A., Araujo, A., Sangiovanni-Vincentelli, A., & Rabaey, J. M. (2021). Adaptive body area networks using kinematics and biosignals. IEEE Journal of Biomedical and Health Informatics, 25(3), 623–633. https://doi.org/10.1109/JBHI.2020.3003924 Moinuddin, A. A., Khan, E., & Ghanbari, M. (2008). Efficient algorithm for very low bit rate embedded image coding. IET Image Processing, 2(2), 59–71. https://doi.org/10.1049/iet-ipr:20070162 Oliver, J., & Malumbres, M. P. (2008). On the design of fast wavelet transform algorithms with low memory requirements. IEEE Transactions on Circuits and Systems for Video Technology, 18(2), 237–248. https://doi.org/10.1109/TCSVT.2007.913962 Pan, H., Siu, W. C., & Law, N. F. (2008). A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT. Signal Processing: Image Communication, 23(3), 146–161. https://doi.org/10.1016/j.image.2008.01.004 Pearlman, W. A., Islam, A., Nagaraj, N., & Said, A. (2004). Efficient, low-complexity image coding with a set-partitioning embedded block coder. IEEE Transactions on Circuits and Systems for Video Technology, 14(11), 1219–1235. https://doi.org/10.1109/TCSVT.2004.835150 Rao, K. R., & Yip, P. (2014). Discrete cosine transform: Algorithms, advantages, applications. San Diego: Academic Press. Ratnakar, V. (1999). TROBIC: Two-row buffer image compression (Vol. 6, pp. 3133–3136). Rein, S. A., Fitzek, F. H. P., Gühmann, C., & Sikora, T. (2015). Evaluation of the wavelet image two-line coder: A low complexity scheme for image compression. Signal Processing: Image Communication, 37, 58–74. https://doi.org/10.1016/j.image.2015.07.010 Rein, S., & Reisslein, M. (2011). Performance evaluation of the fractional wavelet filter: A low-memory image wavelet transform for multimedia sensor networks. Ad Hoc Networks, 9(4), 482–496. https://doi.org/10.1016/j.adhoc.2010.08.004 Said, A., & Pearlman, W. A. (1996). A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, 6(3), 243–250. https://doi.org/10.1109/76.499834 Senapati, R. K., Pati, U. C., & Mahapatra, K. K. (2012). Listless block-tree set partitioning algorithm for very low bit rate embedded image compression. AEU-International Journal of Electronics and Communications, 66(12), 985–995. https://doi.org/10.1016/j.aeue.2012.05.001 Senapati, R. K., Pati, U. C., & Mahapatra, K. K. (2012). Listless block-tree set partitioning algorithm for very low bit rate embedded image compression. AEU-International Journal of Electronics and Communications, 66(12), 985–995. https://doi.org/10.1016/j.aeue.2012.05.001 Shapiro, J. M. (1993). Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing, 41(12), 3445–3462. https://doi.org/10.1109/78.258085 Sun, H., & Shi, Y. Q. (2008). Image and video compression for multimedia engineering: Fundamentals, algorithms, and standards (2nd ed.). Boca Raton: CRC Press. Taubman, D. (2000). High performance scalable image compression with ebcot. IEEE Transactions on Image Processing, 9(7), 1158–1170. https://doi.org/10.1109/83.847830 Tausif, M., Khan, E., & Hasan, M. (2018). BFrWF: block-based FrWF for coding of high-resolution images with memory-complexity constrained-devices (pp. 1–5). Tausif, M., Khan, E., Hasan, M., & Reisslein, M. (2017). SFrWF: Segmented fractional wavelet filter based Dwt for low memory image coders (pp. 593–597). Tausif, M., Khan, E., Hasan, M., & Reisslein, M. (2019). SMFrWF: Segmented modified fractional wavelet filter: Fast low-memory discrete wavelet transform (DWT). IEEE Access, 7, 84448–84467. https://doi.org/10.1109/ACCESS.2019.2924490 Tausif, M., Kidwai, N. R., Khan, E., & Reisslein, M. (2015). FrWF-based LMBTC: Memory-efficient image coding for visual sensors. IEEE Sensors Journal, 15(11), 6218–6228. https://doi.org/10.1109/JSEN.2015.2456332 Tausif, M., Rahman Kidwai, N., & Khan, E. (2017). Low-memory image coder for wearable visual sensors. In S. Chandra Mukhopadhyay & T. Islam (Eds.), Wearable sensors: Applications, design and implementation, Ch. 10 (pp. 101–1036). Bristol: IOP Publishing. Usevitch, B. E. (2001). A tutorial on modern lossy wavelet image compression: Foundations of JPEG 2000. IEEE Signal Processing Magazine, 18(5), 22–35. https://doi.org/10.1109/79.952803 Wheeler, F. W., & Pearlman, W. A. (2000). Combined spatial and subband block coding of images (Vol. 3, pp. 861–864). Wheeler, F. W., & Pearlman, W. A. (2000). SPIHT image compression without lists (Vol. 4, pp. 2047–2050). Yarinezhad, R., & Sarabi, A. (2018). Reducing delay and energy consumption in wireless sensor networks by making virtual grid infrastructure and using mobile sink. AEU - International Journal of Electronics and Communications, 84, 144–152. https://doi.org/10.1016/j.aeue.2017.11.026 Ye, L., Guo, J., Nutter, B., & Mitra, S. (2007). Memory-efficient image codec using line-based backward coding of wavelet trees (pp. 213–222). Ye, L., Guo, J., Nutter, B., & Mitra, S. (2011). Low-memory-usage image coding with line-based wavelet transform. Optical Engineering, 50(2), 027005-1-027005–11. https://doi.org/10.1117/1.3541802 Ye, L., & Hou, Z. (2015). Memory efficient multilevel discrete wavelet transform schemes for JPEG2000. IEEE Transactions on Circuits and Systems for Video Technology, 25(11), 1773–1785. https://doi.org/10.1109/TCSVT.2015.2400776 Zemliachenko, A., Lukin, V., Ponomarenko, N., Egiazarian, K., & Astola, J. (2016). Still image/video frame lossy compression providing a desired visual quality. Multidimensional Systems and Signal Processing, 27(3), 697–718.