Research PaperNo Access

Multiscale transform-based secured joint efficient medical image compression-encryption using symmetric key cryptography and ebcot encoding technique

Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India

E-mail Address: avemariaraja@gmail.com

Search for more papers by this author

https://doi.org/10.1142/S0219691319500346Cited by:15 (Source: Crossref)

Abstract

Due to the huge advancement in technology, digitizing the multimedia content like text, images and videos has become easier. Everyday huge amounts of multimedia content are shared through the social networks using internet. Sometimes this multimedia content can be hacked by the hackers. This will lead to the misuse of the data. On the other hand, the medical content needs high security and privacy. Motivated by this, joint secured medical image compression–encryption mechanisms are proposed in this paper using multiscale transforms and symmetric key encryption techniques. The multiscale transforms involved in this paper are wavelet transform, bandelet transform and curvelet transform. The encryption techniques involved in this paper are international data encryption algorithm (IDEA), Rivest Cipher (RC5) and Blowfish. The encoding technique used in this paper is embedded block coding with truncation (EBCOT). Experimental results are done for the proposed works and evaluated by using various parameters like Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Image Quality Index (IQI) and Structural Similarity Index (SSIM), Average Difference (AD), Normalized Cross-correlation (NK), Structural Content (SC), Maximum difference (MD), Laplacian Mean Squared Error (LMSE) and Normalized Absolute Error (NAE). It is justified that the proposed approaches in this paper yield good results.

Keywords:

AMSC: 68U10, 92C55, 94A60, 68P30, 94A08, 44A05, 44A30

References

1. S. Agarwal, E. E. Regentova, P. Kachroo and H. Verma, Multidimensional compression of ITS data using wavelet-based compression techniques, IEEE Trans. Intell. Transp. Syst. 18(7) (2017) 1907–1917. Web of Science, Google Scholar
2. M. Antonini, M. Barland, P. Mathien and I. Daubechies, Image coding using wavelet transform, IEEE Trans. Image Process. 1(1) (1992) 205–220. Web of Science, Google Scholar
3. A. Z. Averbuch, V. A. Zheludev, M. Guttmann and D. D. Kosloff, LCT-wavelet based algorithms for data compression, J. Circ. Syst. Comput. 11(5) (2013) 1–11. Google Scholar
4. E. Cand‘es and D. Donoho, Curvelets — A surprisingly effective nonadaptive representation for objects with edges, in Curves and Surface Fitting: Saint-Malo, eds. A. Cohen, C. Rabut and L. Schumaker (Vanderbilt University Press, Nashville, 2000), pp. 105–120. Google Scholar
5. S. R. Deans, The Radom Transform and Some of its Applications (Wiley, New York, 1983). Google Scholar
6. E. Guariglia, Entropy and fractal antennas, Entropy, 18(3) (2016) 84. Web of Science, Google Scholar
7. E. Guariglia, Harmonic Sierpinski gasket and applications, Entropy 20(9) (2018) 714. Web of Science, Google Scholar
8. R. C. Guido, ZCR-aided neurocomputing: A study with applications, Knowl. Based Syst. 105 (2016) 248–269. Web of Science, Google Scholar
9. R. C. Guido, S. Barbon Jr, L. S. Vieira et al., Introduction to the discrete shapelet transform and a new paradigm: Joint time-frequency-shape analysis, in Proc. IEEE Int. Symp. Circuits and Systems (IEEE ISCAS 2008), Seattle, WA, USA, Vol. 1, 2008, pp. 2893–2896. Google Scholar
10. A. K. Gupta, S. Nooshabadi and D. Taubman, Efficient interfacing of DWT and EBCOT in JPEG2000, IEEE Trans. Circ. Syst. Video Technol. 18(5) (2008) 687–693. Web of Science, Google Scholar
11. G. Kasana, K. Singh and S. S. Bhatia, EMD-based steganography techniques for JPEG2000 encoded images, Int. J. Wavelets Multiresolution Inf. Process. 15(3) (2017) 1–12. Link, Web of Science, Google Scholar
12. X. Lai and J. Massey, A proposal for a new block encryptions standard, in Proc. Workshop on the Theory and Application of Cryptographic Techniques on Advances in Cryptology (Aarhus, Denmark, 1991), pp. 389–404. Google Scholar
13. F. Lang, J. Zhou, Y. Y. Tang, H. Yu, S. Cang and Z. Shang, Characteristics analysis of wavelet coefficients and its applications in image compression, Int. J. Wavelets Multiresolution Inf. Process. 12(3) (2014) 1–16. Link, Web of Science, Google Scholar
14. P. Li and K.-T. Lo, A content-adaptive joint image compression and encryption scheme, IEEE Trans. Multimed. 20(8) (2018) 1960–1972. Web of Science, Google Scholar
15. H. Liu and K.-K. Huang, Zerotree wavelet image compression with weighted sub-block-trees and adaptive coding order, Int. J. Wavelets Multiresolution Inf. Process. 14(4) (2016) 1–19. Link, Web of Science, Google Scholar
16. N. A. Loan, N. N. Hurrah, S. A. Parah, J. Weon Lee, J. A. Sheikh and G. M. Bhat, Secure and robust digital image watermarking using coefficient differencing and chaotic encryption, IEEE Access, Spec. Sec. Inf. Secur. Sol. Telemed. Appl. 6 (2018) 19876–19897. Google Scholar
17. S. Mallat, A Wavelet Tour of Signal Processing (Academic Press, NewYork, 1998). Google Scholar
18. A. Masmoudi and W. Puech, Lossless chaos-based crypto-compression scheme for image protection, IET Image Process. 8(12) (2014) 671–686. Web of Science, Google Scholar
19. D. C. Mishra and R. K. Sharma, Application of algebra and discrete wavelet transform in two-dimensional data (RGB-images) security, Int. J. Wavelets Multiresolution Inf. Process. 12(6) (2014) 1–16. Link, Web of Science, Google Scholar
20. K. Naik and A. K. Pal, A cryptosystem for lossless/lossy grayscale images in IWT domain using chaotic map-based generated key matrices, Int. J. Wavelets Multiresolution Inf. Process. 16(4) (2018) 1–17. Link, Web of Science, Google Scholar
21. T. Ning, C. H. Huang, J. A. Jensen, V. Wong and H. Chan, Optical emission spectrum processing using wavelet compression during wafer fabrication, IEEE Trans. Semicond. Manuf. 30(4) (2017) 380–387. Web of Science, Google Scholar
22. E. Le Pennec and S. Mallat, Sparse geometric image representations with bandelets, IEEE Trans. Image Process. 14(4) (2005) 423–438. Web of Science, Google Scholar
23. R. L. Rivest, The RC5 encryption algorithm, in Proc. Second Int. Workshop on Fast Software Encryption (FSE) (Haifa, 1994), pp. 86–96. Google Scholar
24. A. Said and W. A. Pearlman, Image compression using the spatial-orientation tree, in IEEE Int. Symp. Circ. Syst. (Chicago, 1993), pp. 279–282. Google Scholar
25. A. Said and W. A. Pearlman, A new, fast, and efficient image codec based on set partitioning in hierarchical trees, IEEE Trans. Circ. Syst. Video Technol. 6(3) (1996) 243–250. Web of Science, Google Scholar
26. B. Schneier, Description of a New Variable-Length Key, 64-Bit Block Cipher (Blowfish), Fast Software Encryption, Cambridge Security Workshop Proc. (Springer-Verlag, 1993), pp. 191–204. Google Scholar
27. J. M. Shapiro, Embedded image coding using zerotrees of wavelet coefficients, IEEE Trans. Signal Proc. 41(12) (1993) 3445–3462. Web of Science, Google Scholar
28. X. Song, Q. Huang, S. Chang, J. He and H. Wang, Three-dimensional separate descendant-based SPIHT algorithm for fast compression of high-resolution medical image sequences, IET Image Process. 11(1) 80–87. Web of Science, Google Scholar
29. Y. Sun, R. Xu, L. Chen and X. Hu, Image compression and encryption scheme using fractal dictionary and Julia set, IET Image Process. 9(3) (2015) 173–183. Web of Science, Google Scholar
30. D. Taubman, High performance scalable image compression with EBCOT, IEEE Trans. Image Process. 9(7) (2000) 1158–1170. Web of Science, Google Scholar
31. J. S. Walker, Wavelet-based image compression, in Transforms and Data Compression Handbook (CRC Press LLC, Boca Raton, 2001). Google Scholar
32. C. Wang, J. Ni, X. Zhang and Q. Huang, Efficient compression of encrypted binary images using the Markov random field, IEEE Trans. Inf. Forensics Sec. 13(5) (2018) 1271–1285. Web of Science, Google Scholar
33. J. Wang, Q.-H. Wang and Y. Hu, Image encryption using compressive sensing and detour cylindrical diffraction, IEEE Photonics J. 10(3) (2018) 1–15. Web of Science, Google Scholar
34. A. Wang, G. Zhao and Y.-D. Li, Wavelet based local fairing algorithm for surfaces with data compression, Int. J. Wavelets Multiresolution Inf. Process. 13(5) (2015) 1–22. Link, Web of Science, Google Scholar
35. X. Zhang, Y. Ren, L. Shen, Z. Qian and G. Feng, Compressing encrypted images with auxiliary information, IEEE Trans. Multimed. 16(5) (2014) 1327–1336. Web of Science, Google Scholar
36. Y. Zhang and L. Y. Zhang, Exploiting random convolution and random subsampling for image encryption and compression, Electron. Lett. 51(20) (2015) 1572–1574. Web of Science, Google Scholar
37. J. Zhou, O. C. Au, G. Zhai, Y. Y. Tang and X. Liu, Scalable compression of stream cipher encrypted images through context-adaptive sampling, IEEE Trans. Inf. Forensics Sec. 9(11) (2014) 1857–1868. Web of Science, Google Scholar
38. J. Zhou, X. Liu, O. C. Au and Y. Y. Tang, Designing an efficient image encryption-then-compression system via prediction error clustering and random permutation, IEEE Trans. Inf. Forensics Sec. 9(1) (2014) 39–50. Web of Science, Google Scholar
39. S. Zope-Chaudhari, P. Venkatachalam and K. M. Buddhiraju, Secure dissemination and protection of multispectral images using crypto-watermarking, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(11) (2015) 5388–5394. Web of Science, Google Scholar