A singular value decomposition based approach for the compression of encrypted images


  • Hiyam Hatem Department of Computer Science, Collage of Computer Science and Information Technology, University of Sumer
  • Raed Majeed Department of Computer Information Technology, Collage of Computer Science and Information Technology, University of Sumer
  • Jumana Waleed Department of Computer Science, College of Science, University of Diyala






Discrete Wavelet Transform (Dwt), Image Encryption, Singular Value Decomposition (SVD), Image Compression.


Image compression is a process which supplies a good solution to the current problems of data storage by reducing redundancy, and irrelevance within images. This paper provides effective encryption then compression technique applied for compressing images within the entire domain of encryption. The Singular Value Decomposition (SVD) application has been described for the results of compression from an image encrypted based on Discrete wavelet transforms (DWT). Initially, the original image has been decomposed into a pyramid of wavelet by utilizing DWT. The DWT subbands are enciphered via a pseudo random number and pseudo random permutation. Then, encrypted images are compressed evaluated by the SVD method which encompasses the corresponding singular values and singular vectors. The performance evaluated on several images and the experimental results and security evaluation is given to validate the explained goals of high security and good compression performance.





[1] Vaish, A., Gautam, S., & Kumar, M. (2017). ‘A wavelet based approach for simultaneous compression and encryption of fused images’. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2017.01.005.

[2] Satone, K. N., Deshmukh, A. S., & Ulhe, P. B. (2017, April). ‘A review of image compression techniques’. In Electronics, Communication and Aerospace Technology (ICECA), 2017 International conference of (Vol. 1, pp. 97-101). IEEE. https://doi.org/10.1109/ICECA.2017.8203651.

[3] Alfalou, A., & Brosseau, C. (2009). ‘Optical image compression and encryption methods’. Advances in Optics and Photonics, 1(3), 589-636. https://doi.org/10.1364/AOP.1.000589.

[4] Alfalou, A., Brosseau, C., Abdallah, N., & Jridi, M. (2013).’ Assessing the performance of a method of simultaneous compression and encryption of multiple images and its resistance against various attacks’. Optics express, 21(7), 8025-8043. https://doi.org/10.1364/OE.21.008025.

[5] Zhou, N., Li, H., Wang, D., Pan, S., & Zhou, Z. (2015). ‘Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform’. Optics Communications, 343, 10-21. https://doi.org/10.1016/j.optcom.2014.12.084.

[6] Johnson, M., Ishwar, P., Prabhakaran, V., Schonberg, D., & Ramchandran, K. (2004). ‘On compressing encrypted data’. IEEE Transactions on Signal Processing, 52(10), 2992-3006. https://doi.org/10.1109/TSP.2004.833860.

[7] Schonberg, D., Draper, S. C., & Ramchandran, K. (2005, September). ‘On blind compression of encrypted data approaching the source entropy rate’. In Signal Processing Conference, 2005 13th European (pp. 1-4). IEEE.

[8] Zhou, N., Zhang, A., Zheng, F., & Gong, L. (2014). ‘Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing’. Optics & Laser Technology, 62, 152-160. https://doi.org/10.1016/j.optlastec.2014.02.015.

[9] Vaish, A., & Kumar, M. (2015, September). ‘Prediction error based compression of encrypted images’. In Proceedings of the Sixth International Conference on Computer and Communication Technology 2015 (pp. 228-232). ACM.

[10] Baker, K. (2005). ‘Singular value decomposition tutorial’. The Ohio State University, 24.

[11] Ranade, A., Mahabalarao, S. S., & Kale, S. (2007). ‘A variation on SVD based image compression’. Image and Vision computing, 25(6), 771-777. https://doi.org/10.1016/j.imavis.2006.07.004.

[12] Majeed, R., Beiji, B. Z., Hiyam, H., & Jumana, W. (2015). ‘Ancient Cuneiform Text Extraction Based on Automatic Wavelet Selection’. International Journal of Multimedia and Ubiquitous Engineering, 10(6), 253-264. https://doi.org/10.14257/ijmue.2015.10.6.25.

[13] Padmavati, S., & Meshram, V. (2017, February). ‘A hardware implementation of discrete wavelet transform for compression of a natural image’. In Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), 2017 International Conference on (pp. 1-5). IEEE. https://doi.org/10.1109/ICAMMAET.2017.8186683.

[14] Singh, A. V., & Murthy, K. S. (2013). ‘Vector quantization–based neuro–wavelet model with cumulative distribution function for efficient image compression’. International Journal of Computer Applications in Technology, 48(2), 106-119. https://doi.org/10.1504/IJCAT.2013.056017.

[15] Jumana Waleed, Huang Dong Jun, and Saad Hameed., (2015), "An optimized digital image watermarking technique based on cuckoo search (CS)", ICIC Express Letters, Part B: Applications, Vol. 6, No. 10, pp. 2629-2634.

[16] Jumana Waleed, Huang Dong Jun, Sarah Saadoon, Saad Hameed, Hiyam Hatem, (2015), "An Immune Secret QR-Code Sharing based on a Twofold Zero Watermarking Scheme", International Journal of Multimedia and Ubiquitous Engineering Vol.10, No.4, pp.399-412. https://doi.org/10.14257/ijmue.2015.10.4.38.

[17] Kalman, D. (1996). ‘A singularly valuable decomposition: the SVD of a matrix’. The college mathematics journal, 27(1), 2-23. https://doi.org/10.1080/07468342.1996.11973744.

[18] Yen, J. C., & Guo, J. I. (2000). ‘Efficient hierarchical chaotic image encryption algorithm and its VLSI realization’. IEE Proceedings-vision, image and signal processing, 147(2), 167-175.


[19] Zhang, X. (2011). ‘Lossy compression and iterative reconstruction for encrypted image’. IEEE transactions on information forensics and security, 6(1), 53-58. https://doi.org/10.1109/TIFS.2010.2099114.

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