BCS-Based Encoding Schemes for Monoview and Multiview Visual Configurations in WVSN Data Gathering: A Survey
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Abstract
Wireless Visual Sensor Network (WVSN) has become a valuable tool in addressing the evolving needs of modern monitoring systems. Encoding in WVSNs is a multifaceted process that involves compressing visual data, optimizing energy consumption, ensuring error resilience, and adapting to various network and application requirements. The associated lightweight encoders and the demand for less storage space make block compressive sensing (BCS) techniques suitable for WVSN applications where energy, bandwidth, and storage resources are limited. Based on the number of visual perspectives or camera angles available within a network for data capture, there are two primary congurations: monoview and multiview. This paper provides a comprehensive survey of dierent BCS-based encoding schemes used for data-gathering in both monoview and multiview scenarios within WVSNs. A comparative study of these algorithms based on compression level, computational complexity, relative gain in encoder energy, and reconstruction quality is performed. A BCS-based joint encoding scheme for multiview conguration that ensures a relatively high compression level is also proposed in this paper.
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References
S. Eleuch, N. Khouja, S. Milani, T. Erseghe, and F. Tlili, “A distributed rate-control approach to reduce communication burdens in VSNs,” IEEE Access, vol. 11, pp. 1011–1022, 2022.
I. K. Abbood and A. K. Idrees, “Data reduction techniques for wireless multimedia sensor
networks: a systematic literature review,” The Journal of Supercomputing, pp. 1–46, 2023.
J. Koteich, C. Salim, and N. Mitton, “Image processing-based data reduction technique in WVSN for smart agriculture,” Computing, vol. 105, no. 12, pp. 2675–2698, 2023.
T. Pal and S. Das Bit, “An improved lowoverhead secure image compression over wireless multimedia sensor network,” Wireless Personal Communications, vol. 132, no. 2, pp. 1049–1081, 2023.
M. Ebrahim, S. H. Adil, T. Gul, and K. Raza, “Comparative analysis: Conventional video
codecs v/s compressive sensing video codecs,” in Proc. 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST), Karachi, Pakistan, 21-22 Dec. 2018.
R. Monika, S. Dhanalakshmi, R. Kumar, and R. Narayanamoorthi, “Coefficient permuted adaptive block compressed sensing for camera enabled underwater wireless sensor nodes,” IEEE Sensors Journal, vol. 22, no. 1, pp. 776–784, 2021.
X. Chai, J. Fu, Z. Gan, Y. Lu, and Y. Zhang, “An image encryption scheme based on multi objective optimization and block compressed sensing,” Nonlinear Dynamics, vol. 108, no. 3,
pp. 2671–2704, 2022.
B. Lal, R. Gravina, F. Spagnolo and P. Corsonello, “Compressed Sensing Approach for Physiological Signals: A Review,” in IEEE Sensors Journal, vol. 23, no. 6, pp. 5513-5534, March 2023.
S. Zhang, J. Wu, D. Chen, S. Li, B. Yu, and J. Qu, “Fast frequency-domain compressed sensing analysis for high-density super-resolution imaging using Orthogonal Matching Pursuit,” IEEE Photonics Journal, vol. 11, no. 1, pp. 1–8, 2018.
Z. Li, W. Xu, Y. Tian, Y. Wang, and J. Lin, “Compressed sensing reconstruction algorithms with prior information: Logit Weight Simultaneous Orthogonal Matching Pursuit,” in Proc. IEEE 79th Vehicular Technology Conference (VTC Spring), Seoul, Korea (South), 18-21 May 2014.
M. A. Davenport, D. Needell, and M. B. Wakin, “Signal space CoSaMP for sparse recovery with redundant dictionaries,” IEEE Transactions on Information Theory, vol. 59, no. 10, pp. 6820–6829, 2013.
S. Li, H. Wang, T. Liu, Z. Cui, J. N. Chen, and Z. Xia, “A fast Barzilai-Borwein gradient projection for sparse reconstruction algorithm based on 3D modeling: Application to ERT imaging,” IEEE Access, vol. 9, pp. 152 913–152 922, 2021.
S. Mun and J. E. Fowler, “Block compressed sensing of images using directional transforms,” in Proc. IEEE 16th international conference on image processing (ICIP), Snowbird, UT, USA, pp. 3021–3024, 24–26 March 2010.
S. Mun, “Residual reconstruction for block based compressed sensing of video,” in Proc. IEEE Data Compression Conference, Snowbird, UT, USA, pp. 183 –192, 29-31 March 2011.
M. Azghani, M. Karimi, and F. Marvasti, “Multihypothesis compressed video sensing technique,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 4, pp. 627–635, 2016.
C. Chen, C. Zhou, P. Liu, and D. Zhang, “Iterative reweighted Tikhonov-regularized multihypothesis prediction scheme for distributed compressive video sensing,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 1, pp. 1–10, 2020.
R. Banerjee and S. Das Bit, “Low-overhead video compression combining partial discrete cosine transform and compressed sensing in WMSNs,” Wireless Networks, vol. 25, no. 8, pp. 5113–5135, 2019.
H.-q. Wang, Q.-n. Tang, Y.-h. Wang, and S.-l. Ren, “Application of D-KSVD in compressed sensing-based video coding,” Optik, vol. 226, p. 165917, 2021.
V. A. Nezhad, M. Azghani, and F. Marvasti, “Compressed video sensing based on deep generative adversarial network,” Circuits, Systems, and Signal Processing, pp. 1–17, 2024.
Y. Zhong, C. Zhang, X. Yang and S. Wang, “Video Compressed Sensing Reconstruction via an Untrained Network with Low-Rank Regularization,” in IEEE Transactions on Multimedia, vol. 26, pp. 4590-4601, 2024.
Z. Gu, C. Zhou, and G. Lin, “A temporal shift reconstruction network for compressive video sensing,” IET Computer Vision, vol. 18, no. 4, pp. 448–457, 2024.
W. Shi, S. Liu, F. Jiang, and D. Zhao, “Video compressed sensing using a convolutional neural network,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 2, pp. 425–438, 2020.
Z. Wei, C. Yang, and Y. Xuan, “Efficient video compressed sensing reconstruction via exploiting spatial-temporal correlation with measurement constraint,” in IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6, 2021.
X. Yang and C. Yang, “Imrnet: an iterative motion compensation and residual reconstruction network for video compressed sensing,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2350–2354, 2021.
Y. Pei, Y. Liu, and N. Ling, “Deep learning for block-level compressive video sensing,” in IEEE international symposium on circuits and systems (ISCAS), pp. 1–5, 2020.
S. Zhang, K. Li, J. Xu, and G. Qu, “Image adaptive coding algorithm based on compressive sensing,” J. Tianjin Univ., vol. 45, no. 4, pp. 319–324, 2012.
R.-F. Wang, L.-C. Jiao, F. Liu, and S.-Y. Yang, “Block-based adaptive compressed sensing of image using texture information,” ACTA ELECTONICA SINICA, vol. 41, no. 8, p. 1506, 2013.
H. Hadizadeh and I. V. Bajic´, “Soft video multicasting using adaptive compressed sensing,”IEEE transactions on multimedia, vol. 23, pp.12– 25, 2020.
Z. Zhang, H. Bi, X. Kong, N. Li, and D. Lu, “Adaptive compressed sensing of color images
based on salient region detection,” Multimedia Tools and Applications, vol. 79, pp. 14777–14791, 2020.
R. Li, X. Duan, X. Guo, W. He, and Y. Lv, “Adaptive compressive sensing of images using
spatial entropy,” Computational Intelligence and Neuroscience, vol. 2017, 2017.
J. Zhang, C. Zhao, D. Zhao, and W. Gao, “Image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization,” Signal Processing, vol. 103, pp. 114–126, 2014.
Y. Yang, D. Zhang, and F. Ding, “Distributed compressive video sensing with adaptive measurements based on temporal correlativity,” in Proc. IEEE 9th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, 11-13 Oct. 2017.
J. Yang, H. Wang, I. Taniguchi, Y. Fan and J. Zhou, “aVCSR: Adaptive Video Compressive Sensing Using Region-of-Interest Detection in the Compressed Domain,” in IEEE MultiMedia, vol. 31, no. 1, pp. 19-32, Jan.-March 2024.
Y. Zhao, Q. Zeng, and E. Y. Lam, “Adaptive compressed sensing for real-time video compression, transmission, and reconstruction,” in IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10, 2023.
J. Zhang, Q. Xiang, Y. Yin, C. Chen, and X. Luo, “Adaptive compressed sensing for wireless image sensor networks,” Multimedia Tools and Applications, vol. 76, pp. 4227–4242, 2017.
G. L. Priya and D. Ghosh, “An effectual video compression scheme for WVSNs based on block compressive sensing,” IEEE Transactions on Network Science and Engineering, vol. 11, no. 2, pp. 1542–1552, 2024.
M. Ebrahim and W. C. Chai, “Multi-phase joint reconstruction framework for multiview video compression using block-based compressive sensing,” in Visual Communications and Image Processing (VCIP), pp. 1–4, 2015.
Y. Liu, C. Zhang, and J. Kim, “Disparitycompensated total-variation minimization for
compressed-sensed multiview image reconstruction,” in IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP), pp. 1458–1462, 2015.
N. Cen, Z. Guan, and T. Melodia, “Interview motion compensated joint decoding for compressively sampled multiview video streams,” IEEE Transactions on Multimedia, vol. 19, no. 6, pp. 1117–1126, 2017.
J. Zhu, J. Wang and Q. Zhu, “Compressively Sensed Multi-View Image Reconstruction Using Joint Optimization Modeling,” 2018 IEEE Visual Communications and Image Processing (VCIP), Taichung, Taiwan, pp. 1-4, 2018.
Y. Liu, D. A. Pados, J. Kim, and C. Zhang, “Reconstruction of compressed-sensed multiview video with disparity-and motion-compensated total variation minimization,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 6, pp. 1288–1302, 2017.
X. Fei, L. Li, H. Cao, J. Miao, and R. Yu, “View’s dependency and low-rank background guided compressed sensing for multiview image joint reconstruction,” IET Image Processing, vol. 13, no. 12, pp. 2294–2303, 2019.
Y. Song, D. Zhang, Q. Tang, S. Tang, and K. Yang, “Local and non-local constraints for compressed sensing video and multiview image recovery,” Neurocomputing, vol. 406, pp. 34–48, 2020.
N. Cen, Z. Guan, and T. Melodia, “Compressed sensing based low-power multiview video coding and transmission in wireless multi-path multihop networks,” IEEE Transactions on Mobile Computing, vol. 21, no. 9, pp. 3122–3137, 2022.
K. Q. Dinh and B. Jeon, “Iterative weighted recovery for block based compressive sensing of image/video at a low subrate,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 11, pp. 2294–2308, 2017.
C.-K. Wen, J. Zhang, K.-K. Wong, J.-C. Chen, and C. Yuen, “On sparse vector recovery performance in structurally orthogonal matrices via LASSO,” IEEE Transactions on Signal Processing, vol. 64, no. 17, pp. 4519–4533, 2016.