, 44:28 | Cite as

Joint medical image compression–encryption in the cloud using multiscale transform-based image compression encoding techniques

  • S P RajaEmail author


The recent years have witnessed rapid strides in the use of cloud computing and its countless applications. A cloud can contain massive volumes of multimedia data in the form of images, video and audio. Cloud computing platforms confront challenges in terms of data confidentiality, message integrity, user authentication and compression. Multimedia data needs plenty of storage capacity. Consequently, there is a need for multimedia data compression to reduce data size. Compression techniques are quite reliable, offering benefits to organizations dealing with metasized data in the cloud. Compressing large quanta of data leads to superior utilization of cloud storage. Compression techniques can compress data used for storage and transmission, yet compression alone is inadequate because multimedia data shared should, of necessity, be secure. Therefore, both multimedia compression and security are mandatory in the cloud. The chief goal of this paper is to propose a new framework, comprising multiscale transforms, public key cryptography and appropriate encoding techniques, that performs joint medical image compression and image encryption in the cloud. Multiscale transforms play a lead role in image compression, and the ones discussed in this paper include wavelet, bandelet, curvelet, ridgelet and contourlet transforms. Wavelet transforms offer robust localization both in terms of time and frequency domains. Bandelet transforms offer natural images geometric regularity to help improve the efficiency of representation. Curvelet transforms handle curve discontinuities well, with ridgelet transforms being the core idea behind curvelets. Contourlet transforms capture smooth contours and edges at any orientation. The Rivest-Shamir-Adleman (RSA) algorithm is used to encrypt images to provide maximum security when they are being transferred. Encoding techniques involved in this paper comprise the Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), Wavelet Difference Reduction (WDR), and Adaptively Scanned Wavelet Difference Reduction (ASWDR). Performance parameters such as peak signal to noise ratio (PSNR), mean square error (MSE), image quality index and structural similarity index (SSIM) are used for evaluation. It is justified that the proposed framework compresses images securely in the cloud.


Cloud computing RSA bandelet wavelet curvelet countourlet ridgelet SPIHT EZW WDR ASWDR 


  1. 1.
    Armbrust M, Fox A, Griffith R, Joseph A D, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I and Zaharia M 2010 A view of cloud computing. Commun. ACM. 53: 50–58CrossRefGoogle Scholar
  2. 2.
  3. 3.
    Antonini M, Barlaud M, Mathieu P and Daubchies I 1992 Image coding using wavelet transform. IEEE Trans. Image Process. 1(4): 205–220CrossRefGoogle Scholar
  4. 4.
    Daubechies I 1990 The wavelet transform, time frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(9): 961–1005MathSciNetCrossRefGoogle Scholar
  5. 5.
    Mallat S 1998 A wavelet tour of signal processing. New York: Academic PresszbMATHGoogle Scholar
  6. 6.
    Ram I, Cohen I and Elad M 2014 Facial image compression using patch-ordering-based adaptive wavelet transform. IEEE Signal Process. Lett. 21(10): 1270–1274CrossRefGoogle Scholar
  7. 7.
    Suruliandi A and Raja S P 2015 Empirical evaluation of EZW and other encoding techniques in the wavelet based image compression domain. Int. J. Wavelets Multiresolution Inf. Process. 13: 2MathSciNetCrossRefGoogle Scholar
  8. 8.
    Candes E J and Donoho D 1999 Curvelets—a surprisingly effective nonadaptive representation for objects with edges. In: A Cohen, C Rabut and L Schumaker (Eds) Curves and Surface Fitting: Saint-Malo. Vanderbilt University Press, Nashville, pp. 105–120Google Scholar
  9. 9.
    Elaiwat S, Bennamoun M, Boussaid F and El-Sallam A 2014 3-D face recognition using curvelet local features. IEEE Signal Process. Lett. 21(2): 172–175CrossRefGoogle Scholar
  10. 10.
    Do M N and Vetterli M 2009 The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14(12): 2091–2106CrossRefGoogle Scholar
  11. 11.
    Ashouri Z and Shirani S 2013 Video super resolution using contourlet transform and bilateral total variation filter. IEEE Trans. Consum. Electron. 59(3): 604–609CrossRefGoogle Scholar
  12. 12.
    El-Mezouar M C, Kpalma K, Taleb N and Ronsin J 2014 A pan-sharpening based on the non-subsampled contourlet transform: application to Worldview-2 imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(5): 1806–1815CrossRefGoogle Scholar
  13. 13.
    Kishor Upla P, Manjunath Joshi V and Prakash Gajjar P 2015 An edge-preserving multiresolution fusion: use of contourlet transform and MRF prior. IEEE Trans. Geosci. Remote Sens. 53(6): 3210–3220CrossRefGoogle Scholar
  14. 14.
    Candes E J and Donoho D L 1999 Ridgelets: a key to higher-dimensional intermittency? Phil. Trans. R. Soc. Lond. A. 357(1760): 2495–2509Google Scholar
  15. 15.
    Le Pennec E and Mallat S 2005 Sparse geometric image representations with bandelets. IEEE Trans. Image Process. 14(4): 423–438MathSciNetCrossRefGoogle Scholar
  16. 16.
    Rivest R, Shamir A and Adleman L 1978 A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM 21(2): 120–126MathSciNetCrossRefGoogle Scholar
  17. 17.
    Huang X and Wang W 2015 A novel and efficient design for an RSA cryptosystem with a very large key size. IEEE Trans. Circuits Syst. II Express Briefs 6(10): 972–976CrossRefGoogle Scholar
  18. 18.
    Sun H M, Wu M E, Ting W C and Jason Hinek M 2007 Dual RSA and its security analysis. IEEE Trans. Inf. Theory 53(8): 2922–2933MathSciNetCrossRefGoogle Scholar
  19. 19.
    Aaron Cohen E and Keshab Parhi K 2011 Architecture optimizations for the RSA public key cryptosystem: a tutorial. IEEE Circuits Syst. Mag. 11(4): 24–34CrossRefGoogle Scholar
  20. 20.
    Shapiro J M 1993 Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Proc. 41(12): 3445–3462CrossRefGoogle Scholar
  21. 21.
    Said A and Pearlman W A 1993 Image compression using the spatial-orientation tree. In: IEEE Int. Symp. on Circuits and Systems, Chicago, IL, pp. 279–282Google Scholar
  22. 22.
    Said A and Pearlman W A 1996 A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuits Syst. Video Technol. 6(3): 243–250CrossRefGoogle Scholar
  23. 23.
    Tian J and Wells R O 1998 Embedded image coding using wavelet difference reduction. In: P Topiwala (Ed.) Wavelet Image and Video Compression, vol. 450. Norwell, MA: Kluwer, pp. 289–302Google Scholar
  24. 24.
    Walker J S and Nguyen T Q 2000 Lossy image codec based on adaptively scanned wavelet difference reduction. Opt. Eng. 39: 1891–1897CrossRefGoogle Scholar
  25. 25.
    Walker J S 2001 Wavelet-Based Image Compression. Transforms and Data Compression Handbook. Boca Raton: CRC Press LLCGoogle Scholar
  26. 26.
    Prasad B and Mishra K 2013 A combined encryption compression scheme using chaotic maps. Cybern. Inf. Technol. 13(2): 75–81MathSciNetGoogle Scholar
  27. 27.
    Shi Z, Sun X and Wu F 2014 Photo album compression for cloud storage using local features. IEEE J. Emerg. Sel. Top. Circuits Syst. 4(1): 17–28CrossRefGoogle Scholar
  28. 28.
    Yue H, Sun X, Yang J and Wu F 2013 Cloud-based image coding for mobile devices—toward thousands to one compression. IEEE Trans. Multimed. 15(4): 845–857CrossRefGoogle Scholar
  29. 29.
    Li C and Li L Y 2015 Hierarchical scheduling optimization scheme in hybrid cloud computing environments. J. Circuits Syst. Comput. 24: 8Google Scholar
  30. 30.
    Mallet S and Peyre G 2007 A review of bandelet methods for geometrical image representation. Numer. Algorithms 44(3): 205–234MathSciNetCrossRefGoogle Scholar
  31. 31.
    Javidan R, Masnadi-Shirazi M A, Azimifar Z and Sadreddini M H 2008 A comparative study between wavelet and contourlet transform features for textural image classification. In: 3rd International Conference on Information and Communication Technologies: From Theory to Applications, pp. 1–8Google Scholar
  32. 32.
    Li Y, Zhang S and Hu J 2010 Combining curvelet transform and wavelet transform for image denoising. In: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence Lecture Notes in Computer Science 62(16), pp. 317–324Google Scholar
  33. 33.
    Chang V and Ramachandran M 2015 Towards achieving data security with the cloud computing adoption framework. IEEE Trans. Serv. Comput. 9(1): 138–151CrossRefGoogle Scholar
  34. 34.
    Tari Z, Yi X, Premarathne U S and Bertok P 2015 Security and privacy in cloud computing: vision, trends, and challenges. IEEE Cloud Comput. 2(2): 30–38CrossRefGoogle Scholar
  35. 35.
    Guan Z T and Yang T T 2015 Research on challenges and security of access control models for mobile cloud computing. In: Proceedings of 2015 International Workshop on Wireless Communication and Network Google Scholar
  36. 36.
    Xiao Z and Xiao Y 2013 Security and privacy in cloud computing. IEEE Commun. Surv. Tutor. 15(2): 843–859CrossRefGoogle Scholar
  37. 37.
    Shabir M Y, Iqbal A, Mahmood Z and Ghafoor A 2016 Analysis of classical encryption techniques in cloud computing. Tsinghua Sci. Technol. 21(1): 102–113CrossRefGoogle Scholar
  38. 38.
    Wang S, Zhou J, Liu J K and Yu J 2016 An efficient file hierarchy attribute-based encryption scheme in cloud computing. IEEE Trans. Inf. Forensics Secur. 11(6): 1265–1277CrossRefGoogle Scholar
  39. 39.
    Zhang Y, Zhang L Y, Zhou J, Liu L, Chen F and He X 2016 A review of compressive sensing in information security field. IEEE Access Green Commun. Netw. 5G Wirel. 4: 2507–2519Google Scholar
  40. 40.
    Chikkannan E and Ramakrishnan K 2017 Feed-forward neural network-based predictive image coding for medical image compression. Arab. J. Sci. Eng. S13369-017-2837-z: 2191–4281Google Scholar
  41. 41.
    Sadh R, Mishra N and Sharma S 2016 Dual plane multiple spatial watermarking with self encryption. Sadhana 41(1): 1–14CrossRefGoogle Scholar
  42. 42.
    Peyré G and Mallat S 2005 Surface compression with geometric bandelets. ACM transactions on graphics. Proc. ACM Siggraph 24(3): 601–608CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyAvadi, ChennaiIndia

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