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A Comprehensive Analysis on Image Encryption and Compression Techniques with the Assessment of Performance Evaluation Metrics

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Abstract

The explosive generation of data in the digital era directs to the requirements of effective approaches to transmit and store the data. The necessities of security and limited resources have led to the development of image encryption and compression approaches, respectively. The digital contents are transmitted over the internet which may subject to security threats. To overcome these limitations, image encryption approaches are developed. The image compression approaches result in the efficient usage of available transmission bandwidth and storage area. Image encryption and compression approach play an important role in the multimedia application that authenticates and secures the digital information. This paper covers the various approaches of image encryption and compression. To validate the performance of the approaches, evaluation metrics are projected and its significance is also discussed.

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References

  1. Tayal N, Bansal R, Gupta S, Dhall S. Analysis of various cryptography techniques: a survey. Int J Secur Appl. 2016;10(8):59–92.

    Google Scholar 

  2. Ghebleh M, Kanso A, Noura H. An image encryption scheme based on irregularly decimated chaotic maps. Signal Process Image Commun. 2014;29(5):618–27.

    Article  Google Scholar 

  3. Kumar P, Parmar A. Versatile approaches for medical image compression: a review. Proc Comput Sci. 2020;167:1380–9.

    Article  Google Scholar 

  4. Pankajavalli PB, Vignesh V, Karthick GS. Implementation of haar cascade classifier for vehicle security system based on face authentication using wireless networks. In: Smys S, Bestak R, Chen JZ, Kotuliak I (editors) International conference on computer networks and communication technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 15. Singapore: Springer; 2019. https://doi.org/10.1007/978-981-10-8681-6_58.

  5. Li XW, Kim ST. Optical 3D watermark based digital image watermarking for telemedicine. Opt Lasers Eng. 2013;51(12):1310–20.

    Article  Google Scholar 

  6. Zhang Y, Zhang LY, Zhou J, Liu L, Chen F, He X. A review of compressive sensing in information security field. IEEE Access. 2016;4:2507–19.

    Article  Google Scholar 

  7. Zhao H, Liu J, Jia J, Zhu N, Xie J, Wang Y. Multiple-image encryption based on position multiplexing of Fresnel phase. Optics Commun. 2013;286:85–90.

    Article  Google Scholar 

  8. Yu SS, Zhou NR, Gong LH, Nie Z. Optical image encryption algorithm based on phase-truncated short-time fractional Fourier transform and hyper-chaotic system. Opt Lasers Eng. 2020;124:105816.

    Article  Google Scholar 

  9. Qin Y, Gong Q. Multiple-image encryption in an interference-based scheme by lateral shift multiplexing. Optics Commun. 2014;315:220–5.

    Article  Google Scholar 

  10. Wang X, Dai C, Chen J. Optical image encryption via reverse engineering of a modified amplitude-phase retrieval-based attack. Optics Commun. 2014;328:67–72.

    Article  Google Scholar 

  11. Enayatifar R, Abdullah AH, Isnin IF. Chaos-based image encryption using a hybrid genetic algorithm and a DNA sequence. Opt Lasers Eng. 2014;56:83–93.

    Article  Google Scholar 

  12. Wang Q, Guo Q, Lei L, Zhou J. Linear exchanging operation and random phase encoding in gyrator transform domain for double image encryption. Optik. 2013;124(24):6707–12.

    Article  Google Scholar 

  13. Kadir A, Hamdulla A, Guo WQ. Color image encryption using skew tent map and hyper chaotic system of 6th-order CNN. Optik. 2014;125(5):1671–5.

    Article  Google Scholar 

  14. Abd El-Latif AA, Niu X. A hybrid chaotic system and cyclic elliptic curve for image encryption. AEU Int J Electron Commun. 2013;67(2):136–43.

    Article  Google Scholar 

  15. Chai X, Gan Z, Yang K, Chen Y, Liu X. An image encryption algorithm based on the memristive hyperchaotic system, cellular automata and DNA sequence operations. Signal Process Image Commun. 2017;52:6–19.

    Article  Google Scholar 

  16. Zhang Q, Liu L, Wei X. Improved algorithm for image encryption based on DNA encoding and multi-chaotic maps. AEU Int J Electron Commun. 2014;68(3):186–92.

    Article  Google Scholar 

  17. Liu H, Wang X, Kadir A. Color image encryption using Choquet fuzzy integral and hyper chaotic system. Opt Int J Light Electron Opt. 2013;124(18):3527–33.

    Article  Google Scholar 

  18. Khan MA, Ahmad J, Javaid Q, Saqib NA. An efficient and secure partial image encryption for wireless multimedia sensor networks using discrete wavelet transform, chaotic maps and substitution box. J Mod Opt. 2017;64(5):531–40.

    Article  Google Scholar 

  19. Singh N, Sinha A. Gyrator transform-based optical image encryption, using chaos. Opt Lasers Eng. 2009;47(5):539–46.

    Article  Google Scholar 

  20. Ozaktas HM, Kutay MA. The fractional Fourier transform. In: 2001 European control conference (ECC), IEEE; 2001. pp. 1477–1483.

  21. Chai X, Chen Y, Broyde L. A novel chaos-based image encryption algorithm using DNA sequence operations. Opt Lasers Eng. 2017;88:197–213.

    Article  Google Scholar 

  22. Zhao G, Chen G, Fang J, Xu G. Block cipher design: generalized single-use-algorithm based on chaos. Tsinghua Sci Technol. 2011;16(2):194–206.

    Article  MATH  Google Scholar 

  23. Belazi A, El-Latif AAA, Belghith S. A novel image encryption scheme based on substitution-permutation network and chaos. Signal Process. 2016;128:155–70.

    Article  Google Scholar 

  24. Abd El-Samie FE, Ahmed HEH, Elashry IF, Shahieen MH, Faragallah OS, El-Rabaie ESM, Alshebeili SA. Image encryption: a communication perspective. Boca Raton: CRC Press; 2013.

    Book  Google Scholar 

  25. Karthick GS, Pankajavalli PB. A review on human healthcare Internet of Things: a technical perspective. SN Comput. Sci. 2020;1:198. https://doi.org/10.1007/s42979-020-00205-z.

  26. Zhang W, Wong KW, Yu H, Zhu ZL. An image encryption scheme using reverse 2-dimensional chaotic map and dependent diffusion. Commun Nonlinear Sci Numer Simul. 2013;18(8):2066–80.

    Article  MathSciNet  MATH  Google Scholar 

  27. Li XW, Cho SJ, Kim ST. A 3D image encryption technique using computer-generated integral imaging and cellular automata transform. Optik. 2014;125(13):2983–90.

    Article  Google Scholar 

  28. Mehra I, Nishchal NK. Optical asymmetric image encryption using gyrator wavelet transform. Opt Commun. 2015;354:344–52.

    Article  Google Scholar 

  29. Rawat N, Kim B, Kumar R. Fast digital image encryption based on compressive sensing using structurally random matrices and Arnold transform technique. Optik. 2016;127(4):2282–6.

    Article  Google Scholar 

  30. Abbas NA. Image encryption based on independent component analysis and arnold’s cat map. Egypt Inform J. 2016;17(1):139–46.

    Article  Google Scholar 

  31. Cao X, Wei X, Guo R, Wang C. No embedding: a novel image cryptosystem for meaningful encryption. J Vis Commun Image Represent. 2017;44:236–49.

    Article  Google Scholar 

  32. Zhang Y, Xu B, Zhou N. A novel image compression–encryption hybrid algorithm based on the analysis sparse representation. Opt Commun. 2017;392:223–33.

    Article  Google Scholar 

  33. Khan M, Shah T. A novel statistical analysis of chaotic S-box in image encryption. 3D Res. 2014;5(3):16.

    Article  Google Scholar 

  34. Ahmad J, Ahmed F. Efficiency analysis and security evaluation of image encryption schemes. Computing. 2010;23:25.

    Google Scholar 

  35. Smith CA. A survey of various data compression techniques. Int J pf Recent Technol Eng. 2010;2(1):1–20.

    Google Scholar 

  36. Hosseini M. A survey of data compression algorithms and their applications. Network Systems Laboratory, School of Computing Science, Simon Fraser University, BC, Canada; 2012.

  37. Reddy MP, Reddy BVR, Bindu CS. Lossy image compression using exponential growth equation and encryption by natural exponential function. J Image Process Pattern Recognit Prog. 2018;4(3):46–55.

    Google Scholar 

  38. Hussain M, Wahab AWA, Idris YIB, Ho AT, Jung KH. Image steganography in spatial domain: a survey. Signal Process Image Commun. 2018;65:46–66.

    Article  Google Scholar 

  39. Huffman DA. A method for the construction of minimum-redundancy codes. Proc IRE. 1952;40(9):1098–101.

    Article  MATH  Google Scholar 

  40. Langdon GG. An introduction to arithmetic coding. IBM J Res Dev. 1984;28(2):135–49.

    Article  MathSciNet  MATH  Google Scholar 

  41. Ziv J, Lempel A. A universal algorithm for sequential data compression. IEEE Trans Inf Theory. 1977;23(3):337–43.

    Article  MathSciNet  MATH  Google Scholar 

  42. Saupe D, Hamzaoui R. A review of the fractal image compression literature. ACM SIGGRAPH Comput Graph. 1994;28(4):268–76.

    Article  Google Scholar 

  43. Arnavut Z, Magliveras SS. Block sorting and compression. In: Proceedings DCC ’97, Data Compression Conference, Snowbird, UT, USA. 1997. p. 181–90. https://doi.org/10.1109/DCC.1997.582009.

  44. Capon J. A probabilistic model for run-length coding of pictures. IRE Trans Inf Theory. 1959;5(4):157–63.

    Article  MathSciNet  Google Scholar 

  45. Schmid M, Steinlein C, Bogart JP, Feichtinger W, Haaf T, Nanda I, et al. The hemiphractid frogs. Phylogeny, embryology, life history, and cytogenetics. Cytogenet Genome Res. 2012;138(2–4):69–83.

    Article  Google Scholar 

  46. Mahmud S. An improved data compression method for general data. Int J Sci Eng Res. 2012;3(3):2.

    Google Scholar 

  47. Platoš J, Snášel V, El-Qawasmeh E. Compression of small text files. Adv Eng Inform. 2008;22(3):410–7.

    Article  Google Scholar 

  48. Kalajdzic K, Ali SH, Patel A. Rapid lossless compression of short text messages. Comput Stand Interfaces. 2015;37:53–9.

    Article  Google Scholar 

  49. De Agostino S. The greedy approach to dictionary-based static text compression on a distributed system. J Discrete Algorithms. 2015;34:54–61.

    Article  MathSciNet  MATH  Google Scholar 

  50. Che W, Zhao Y, Guo H, Su Z, Liu T. Sentence compression for aspect-based sentiment analysis. IEEE ACM Trans Audio Speech Lang Process. 2015;23(12):2111–24.

    Article  Google Scholar 

  51. Oswald C, Ghosh AI, Sivaselvan B. Knowledge engineering perspective of text compression. In 2015 Annual IEEE India conference (INDICON), IEEE; 2015. pp. 1–6.

  52. Oswald C, Sivaselvan B. An optimal text compression algorithm based on frequent pattern mining. J Ambient Intell Humaniz Comput. 2018;9(3):803–22.

    Article  Google Scholar 

  53. Rao YR, Eswaran C. New bit rate reduction techniques for block truncation coding. IEEE Trans Commun. 1996;44(10):1247–50.

    Article  Google Scholar 

  54. Sanchez-Cruz H, Rodriguez-Dagnino RM. Compressing bilevel images by means of a three-bit chain code. Opt Eng. 2005;44(9):097004.

    Article  Google Scholar 

  55. Khan A, Khan A, Khan M, Uzair M. Lossless image compression: application of Bi-level Burrows Wheeler Compression Algorithm (BBWCA) to 2-D data. Multimed Tools Appl. 2017;76(10):12391–416.

    Article  Google Scholar 

  56. Kumar M, Vaish A. An efficient encryption-then-compression technique for encrypted images using SVD. Digit Signal Process. 2017;60:81–9.

    Article  Google Scholar 

  57. Huang H, Shu H, Yu R. Lossless audio compression in the new IEEE Standard for Advanced Audio Coding. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP), Florence. 2014. p. 6934–8. https://doi.org/10.1109/ICASSP.2014.6854944.

  58. Hang B, Wang Y, Kang C. A scalable variable bit rate audio codec based on audio attention analysis. Revista Técnica de la Facultad de Ingeniería. 2016;39(6):114–20.

    Google Scholar 

  59. Brettle J, Skoglund J. Open-source spatial audio compression for vr content. In: SMPTE 2016 annual technical conference and exhibition, SMPTE; 2016. pp. 1–9.

  60. Kosmidou VE, Hadjileontiadis LJ. Sign language recognition using intrinsic-mode sample entropy on sEMG and accelerometer data. IEEE Trans Biomed Eng. 2009;56(12):2879–90.

    Article  Google Scholar 

  61. Marcelloni F, Vecchio M. A simple algorithm for data compression in wireless sensor networks. IEEE Commun Lett. 2008;12(6):411–3.

    Article  Google Scholar 

  62. Alsheikh MA, Lin S, Niyato D, Tan HP. Rate-distortion balanced data compression for wireless sensor networks. IEEE Sens J. 2016;16(12):5072–83.

    Article  Google Scholar 

  63. Rajakumar K, Arivoli T. Lossy image compression using multiwavelet transform for wireless transmission. Wirel Pers Commun. 2016;87(2):315–33.

    Article  Google Scholar 

  64. Drinic M, Kirovski D, Potkonjak M. Model-based compression in wireless ad hoc networks. In: Proceedings of the 1st international conference on embedded networked sensor systems; 2003. pp. 231–242.

  65. Khan TH, Wahid KA. White and narrow band image compressor based on a new color space for capsule endoscopy. Signal Process Image Commun. 2014;29(3):345–60.

    Article  Google Scholar 

  66. Venugopal D, Mohan S, Raja S. An efficient block based lossless compression of medical images. Optik. 2016;127(2):754–8.

    Article  Google Scholar 

  67. Nielsen M, Kamavuako EN, Andersen MM, Lucas MF, Farina D. Optimal wavelets for biomedical signal compression. Med Biol Eng Comput. 2006;44(7):561–8.

    Article  Google Scholar 

  68. Unnikrishnan S, Surve S, Bhoir D. Advances in computing, communication and control. In: Conference proceedings ICAC3; 2011. p. 109.

  69. Vadori V, Grisan E, Rossi M. Biomedical signal compression with time-and subject-adaptive dictionary for wearable devices. In: 2016 IEEE 26th international workshop on machine learning for signal processing (MLSP), IEEE; 2016. pp. 1–6.

  70. Lee CF, Changchien SW, Wang WT, Shen JJ. A data mining approach to database compression. Inf Syst Front. 2006;8(3):147–61.

    Article  Google Scholar 

  71. Louie H, Miguel A. Lossless compression of wind plant data. IEEE Trans Sustain Energy. 2012;3(3):598–606.

    Article  Google Scholar 

  72. Fout N, Ma KL. An adaptive prediction-based approach to lossless compression of floating-point volume data. IEEE Trans Vis Comput Graph. 2012;18(12):2295–304.

    Article  Google Scholar 

  73. Venkataraman KS, Dong G, Xie N, Zhang T. Reducing read latency of shingled magnetic recording with severe intertrack interference using transparent lossless data compression. IEEE Trans Magn. 2013;49(8):4761–7.

    Article  Google Scholar 

  74. Shannon CE. A symbolic analysis of relay and switching circuits. Electr Eng. 1938;57(12):713–23.

    Article  Google Scholar 

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Correspondence to N. Mahendiran.

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K N and M. Shivakumar.

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Mahendiran, N., Deepa, C. A Comprehensive Analysis on Image Encryption and Compression Techniques with the Assessment of Performance Evaluation Metrics. SN COMPUT. SCI. 2, 29 (2021). https://doi.org/10.1007/s42979-020-00397-4

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