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

 

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

 PDF

Abstract:
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.

Keywords:
image encryption, wavelet coefficient, chaotic system.

Citation:
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.

References:

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

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846)332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20