Efficient Image Processing Through Compressive Sampling: Bridging the Gap between Data Compression and High-Quality Reconstruction
DOI:
https://doi.org/10.31272/jeasd.2643Keywords:
Compressive Sampling, Image Compression, Image Fidelity, Image Reconstruction, Sparse Transform CompressionAbstract
One of the longstanding problems in digital imaging is the compression of images with sufficient fidelity for reconstruction. Common file formats like JPEG and PNG are not well suited for higher compression, so there has been a growing interest in compressive sampling as an alternative. In this paper, the basic principles of compressive sampling are appraised, and its performance is compared with traditional methods of compression on a set of 129 animal images. The compression ratios of the Sparse Transform method on the test dataset were within the range of 9.26–93.44 (mean 45.02). Mean reconstruction error was 24.14, PSNR ranged from 18.98 to 27.07 dB (mean 23.27 dB), and SSIM from 0.68 to 0.96 (mean 0.85). The results presented here indicate that, among the compressive sampling variants tested, the Sparse Transform approach rose as the best combination of compression and reconstruction quality, whereas the conventional methods (including JPEG) have better reconstruction fidelity at each operating point. Finally, the paper raises a number of questions that need to be addressed in order to continue work in this area, particularly the applicability of the method to other more specialized imaging tasks.
References
N. A. Awad and A. Mahmoud, "Improving Reconstructed Image Quality via Hybrid Compression Techniques," Computers, Materials & Continua, vol. 66, no. 3, pp. 3151–3160, 2021, doi:https:/doi.org/10.32604/cmc.2021.014426
A. J. Hussain, A. Al-Fayadh, and N. Radi, "Image compression techniques: A survey in lossless and lossy algorithms," Neurocomputing, vol. 300, pp. 44-69, 2018, doi: https://doi.org/10.1016/J.NEUCOM.2018.02.094.
C. E. Shannon, “Communication in the Presence of Noise,” Proceedings of the IRE, vol. 37, no. 1, pp. 10–21, Jan. 1949, doi: https://doi.org/10.1109/jrproc.1949.232969.
R. Arie, A. Brand, and S. Engelberg, "Compressive sensing and sub-Nyquist sampling," IEEE Instrumentation & Measurement Magazine, vol. 23, no. 2, pp. 94-101, 2020, doi: https://doi.org/10.1109/MIM.2020.9062696.
M. M. Abo-Zahhad, A. I. Hussein, and A. M. Mohamed, "Compressive sensing algorithms for signal processing applications: A survey," International Journal of Communications, Network and System Sciences, vol. 8, no. 6, pp. 197-216, 2015, doi: https://doi.org/10.4236/ijcns.2015.86021.
A. Singh and K. G. Kirar, "Review of image compression techniques," 2017 International Conference on Recent Innovations in Signal Processing and Embedded Systems (RISE), Bhopal, India, 2017, pp. 172-174, doi: https://doi.org/10.1109/RISE.2017.8378148.
J. Chen, Y. Gao, C. Ma, and Y. Kuo, "Compressive sensing image reconstruction based on multiple regulation constraints," Circuits, Systems, and Signal Processing, vol. 36, pp. 1621-1638, 2017, doi: https://doi.org/10.1007/s00034-016-0432-2.
M. Lakshminarayana and M. Sarvagya, "Algorithm to balance compression and signal quality using novel compressive sensing in medical images," in International Conference on Information and Communication Technology for Intelligent Systems, 2016: Springer, pp. 317-327, doi: https://doi.org/10.1007/978-3-319-33622-0_29.
B. Zhang, "One-two-one networks for compression artifacts reduction in remote sensing," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 145, pp. 184-196, 2018, doi: https://doi.org/10.1016/j.isprsjprs.2018.01.003.
A. J. I. Barbhuiya, T. A. Laskar, and K. Hemachandran, "An approach for color image compression of JPEG and PNG images using DCT and DWT," in 2014 International Conference on Computational Intelligence and Communication Networks, 2014: IEEE, pp. 129-133, doi: https://doi.org/10.1109/CICN.2014.40.
S. Zhu, B. Zeng, and M. Gabbouj, "Adaptive sampling for compressed sensing based image compression," Journal of Visual Communication and Image Representation, vol. 30, pp. 94-105, 2015, doi: https://doi.org/10.1016/j.jvcir.2015.03.006.
S. Bennett, "Compressive sampling using a pushframe camera," IEEE Transactions on Computational Imaging, vol. 7, pp. 1069-1079, 2021, doi: https://doi.org/10.1109/TCI.2021.3114980.
E. J. Candès and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, vol. 25, no. 2, pp. 21-30, 2008, doi: https://doi.org/10.1109/MSP.2007.914731.
S. Chen and D. Donoho, "Basis pursuit," in Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers, 1994, vol. 1: IEEE, pp. 41-44, doi: https://doi.org/10.1109/ACSSC.1994.471413.
A. Beck and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse problems," SIAM Journal on Imaging Sciences, vol. 2, no. 1, pp. 183-202, 2009, doi: https://doi.org/10.1137/080716542.
H. S. Goklani, J. N. Sarvaiya, and A. Fahad, "Image reconstruction using orthogonal matching pursuit (OMP) algorithm," in 2014 2nd International Conference on Emerging Technology Trends in Electronics, Communication and Networking, 2014: IEEE, pp. 1-5, doi: https://doi.org/10.1109/ET2ECN.2014.7044960.
D. Needell and J. A. Tropp, "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples," Applied and Computational Harmonic Analysis, vol. 26, no. 3, pp. 301–321, 2009, doi: https://doi.org/10.1016/j.acha.2008.07.002
J. Zhang, C. Zhao, and W. Gao, "Optimization-inspired compact deep compressive sensing," IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 4, pp. 765-774, 2020, doi: https://doi.org/10.1109/JSTSP.2020.2977507.
E. J. Candès and T. Tao, "Decoding by linear programming," IEEE Transactions on Information Theory, vol. 51, no. 12, pp. 4203–4215, 2005, doi: https://doi.org/10.1109/TIT.2005.858979.
T. Saha, S. Srivastava, S. Khare, P. S. Stanimirović, and M. D. Petković, "An improved algorithm for basis pursuit problem and its applications," Applied Mathematics and Computation, vol. 355, pp. 385-398, 2019, doi: https://doi.org/10.1016/j.amc.2019.02.073.
Z. Hu, D. Liang, D. Xia, and H. Zheng, "Compressive sampling in computed tomography: Method and application," Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 748, pp. 26-32, 2014, doi: https://doi.org/10.1016/j.nima.2014.02.026.
S. Dhawan, "A review of image compression and comparison of its algorithms," International Journal of Electronics & Communication Technology, vol. 2, no. 1, pp. 22-26, 2011. Retrieved from https://ceng460.cankaya.edu.tr/uploads/files/file/Review%20paper.pdf.
K. Sayood, Introduction to Data Compression, 5th ed. Morgan Kaufmann, 2017.
M. Weeks and M. Bayoumi, "Discrete wavelet transform: architectures, design and performance issues," Journal of VLSI signal processing systems for signal, image and video technology, vol. 35, pp. 155-178, 2003, doi: https://doi.org/10.1023/A:1023648531542.
M. Dadkhah, M. J. Deen, and S. Shirani, "Compressive sensing image sensors-hardware implementation," Sensors, vol. 13, no. 4, pp. 4961-4978, 2013, doi: https://doi.org/10.3390/s130404961.
A. S. Abd-Alzhra and M. S. Al-Tamimi, "Image compression using deep learning: methods and techniques," Iraqi Journal of Science, pp. 1299-1312, 2022, doi: https://doi.org/10.24996/ijs.2022.63.3.34.
I. A. Kougioumtzoglou, I. Petromichelakis, and A. F. Psaros, "Sparse representations and compressive sampling approaches in engineering mechanics: A review of theoretical concepts and diverse applications," Probabilistic Engineering Mechanics, vol. 61, p. 103082, 2020, doi: https://doi.org/10.1016/j.probengmech.2020.103082.
H. Gan, Y. Gao, C. Liu, H. Chen, T. Zhang, and F. Liu, "AutoBCS: Block-based image compressive sensing with data-driven acquisition and noniterative reconstruction," IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2558-2571, 2021, doi: https://doi.org/10.1109/TCYB.2021.3127657.
G. K. Wallace, "The JPEG still picture compression standard," IEEE Transactions on Consumer Electronics, vol. 38, no. 1, pp. xviii–xxxiv, 1992, doi: https://doi.org/10.1109/30.125072
E. Öztürk and A. Mesut, "Performance evaluation of JPEG standards, Webp and PNG in terms of compression ratio and time for lossless encoding," in 2021 6th International Conference on Computer Science and Engineering (UBMK), 2021: IEEE, pp. 15-20, doi: https://doi.org/10.1109/UBMK52708.2021.9558922.
C. Christopoulos, A. Skodras, and T. Ebrahimi, "The JPEG2000 still image coding system: an overview," IEEE Transactions on Consumer Electronics, vol. 46, no. 4, pp. 1103–1127, 2000, doi: https://doi.org/10.1109/30.920468
P. Khobragade and S. Thakare, "Image compression techniques-a review," Int. J. Comput. Sci. Inf. Technol, vol. 5, no. 1, pp. 272-275, 2014. Retrieved from: https://www.ijcsit.com/docs/Volume%205/vol5issue01/ijcsit2014050157.pdf
Q. Huynh-Thu and M. Ghanbari, "Scope of validity of PSNR in image/video quality assessment," Electronics Letters, vol. 44, no. 13, pp. 800–801, 2008, doi: https://doi.org/10.1049/el:20080522
D. R. I. M. Setiadi, "PSNR vs SSIM: imperceptibility quality assessment for image steganography," Multimedia Tools and Applications, vol. 80, no. 6, pp. 8423-8444, 2021, doi: https://doi.org/10.1007/s11042-020-10035-z.
J.-H. Xue and D. M. Titterington, "t-tests, F -tests and Otsu’s Methods for Image Thresholding," IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2392-2396, 2011, doi: https://doi.org/10.1109/TIP.2011.2114358.
Downloads
Key Dates
Received
Revised
Accepted
Published Online First
Published
Issue
Section
License
Copyright (c) 2026 Ghassan Ahmad Ismaeel (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
