Image compression and encryption based on wavelet transform and chaos
H. Gao, W. Zeng

College of Information Science & Engineering, Hunan International Economics University, Changsha 410205, China

Аннотация:
With the rapid development of network technology, more and more digital images are transmitted on the network, and gradually become one important means for people to access the information. The security problem of the image information data increasingly highlights and has become one problem to be attended. The current image encryption algorithm basically focuses on the simple encryption in the frequency domain or airspace domain, and related methods also have some shortcomings. Based on the characteristics of wavelet transform, this paper puts forward the image compression and encryption based on the wavelet transform and chaos by combining the advantages of chaotic mapping. This method introduces the chaos and wavelet transform into the digital image encryption algorithm, and transforms the image from the spatial domain to the frequency domain of wavelet transform, and adds the hybrid noise to the high frequency part of the wavelet transform, thus achieving the purpose of the image degradation and improving the encryption security by combining the encryption approaches in the spatial domain and frequency domain based on the chaotic sequence and the excellent characteristics of wavelet transform. Testing experiments show that such algorithm reduces the memory consumption and implements the complexity, not only can decrease the key spending and compress the time spending, but also can improve the quality of decoded and reconstructed image, thus showing good encryption features with better encryption effect.

Ключевые слова:
image encryption, wavelet coefficient, chaotic system.

Цитирование:
Gao H, Zeng W. Image compression and encryption based on wavelet transform and chaos. Computer Optics 2019; 43(2): 258-263. DOI: 10.18287/2412-6179-2019-43-2-258-263.

Литература:

  1. Tong, X. A joint image lossless compression and encryption method based on chaotic map / X. Tong, P. Chen, M. Zhang // Multimedia Tools and Applications. – 2017. – Vol. 76, Issue 12. – P. 13995-14020.
  2. Zhu, H. A novel image encryption-compression scheme using hyper-chaos and Chinese remainder theorem / H. Zhu, Ch. Zhao, X. Zhang // Signal Processing: Image Communication. – 2013. – Vol. 28, Issue 6. – P. 670-680.
  3. Alfalou, A. Assessing the performance of a method of simultaneous compression and encryption of multiple images and its resistance against various attacks / A. Alfalou, C. Brosseau, N. Abdallah // Optics Express. – 2013. – Vol. 21, Issue 7. – P. 8025-8043.
  4. Kong, Y. Time-varying neural networks for dynamical systems modeling with application to image compression / Y. Kong, H-j. Lu // International Journal of Security and Its Applications. – 2016. – Vol. 10, Issue 12. – P. 323-334.
  5. Tang, J. Critical algorithm for graph and image compression and transmission research / J. Tang // International Journal of Future Generation Communication and Networking. – 2016. – Vol. 9, Issue 12. – P. 387-394.
  6. Jaferzadeh, K. Lossless and lossy compression of quantitative phase images of red blood cells obtained by digital holographic imaging / Jaferzadeh Keyvan, Gholami Samaneh, and Moon Inkyu // Applied Optics. – 2016. – Vol. 55, Issue 36. – P. 10409-10416.
  7. Alfalou, A. Assessing the performance of a method of simultaneous compression and encryption of multiple images and its resistance against various attacks / A. Alfalou, C. Brosseau, N. Abdallah, M. Jridi // Optics Express. – 2013. – Vol. 21, Issue 7. – P. 8025-8043.
  8. Zhou. J. Designing an efficient image encryption-then-compression system via prediction error clustering and random permutation / J. Zhou, X. Liu, O.C. Au, Y.Y. Tang // IEEE Transactions on Information Forensics and Security. – 2014. – Vol. 9, Issue 1. – P. 39-50.
  9. Babu, R.N. Improving forecast accuracy of wind speed using wavelet transform and neural networks / R.N. Babu, P. Arulmozhivarman // Journal of Electrical Engineering and Technology. – 2013. – Vol. 8, Issue 3. – P. 559-564.
  10. Khalili, M. Colour spaces effects on improved discrete wavelet transform-based digital image watermarking using Arnold transform map / M. Khalili, D. Asatryan // IET Signal Processing. – 2013. – Vol. 7, Issue 3. – P. 177-187.
  11. Mikherskii, R.M. Application of an artificial immune system for visual pattern recognition / R.M. Mikherskii // Computer Optics. – 2018. – Vol. 42, Issue 1. – P. 113-117. – DOI: 10.18287/2412-6179-2018-42-1-113-117.

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