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Image compression and encryption algorithm with wavelet-transform-based 2D compressive sensing

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
By combining a wavelet transform with chaos scrambling, an image compression and encryption algorithm based on 2D compressive sensing is designed. The wavelet transform is employed to obtain the sparse representation of a plaintext image. The sparse image is measured in two orthogonal directions by compressive sensing. Then, the result of 2D compressive sensing is confused by the Arnold transform and the random pixel scrambling. The combination of four-dimensional chaos and logistic map is exploited to generate the first row of the key-controlled circulant matrix. The proposed algorithm not only carries out image compression and encryption simultaneously, but also reduces the consumption of the key by controlling the generation of measurement matrix. Experimental results reveal that the proposed image compression and encryption algorithm is resistant to noise attacks with good compression performance and high key sensitivity.
Czasopismo
Rocznik
Strony
461--472
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
autor
  • School of Information Engineering, Nanchang University, Nanchang 330031, China
autor
  • School of Information Engineering, Nanchang University, Nanchang 330031, China
autor
  • School of Information Engineering, Nanchang University, Nanchang 330031, China
Bibliografia
  • [1] DONOHO D.L., Compressed sensing, IEEE Transactions on Information Theory 52(4), 2006, pp. 1289 –1306, DOI: 10.1109/TIT.2006.871582.
  • [2] CANDÈS E.J., ROMBERG J., TAO T., Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory 52(2), 2006, pp. 489–509, DOI: 10.1109/TIT.2005.862083.
  • [3] BARANIUK R.G., Compressive sensing, IEEE Signal Processing Magazine 24(4), 2007, pp. 118–121, DOI: 10.1109/MSP.2007.4286571.
  • [4] ORSDEMIR A., ALTUN H.O., SHARMA G., BOCKO M.F., On the security and robustness of encryption via compressed sensing, [In] MILCOM 2008 – 2008 IEEE Military Communications Conference, 2008, pp. 1–7, DOI: 10.1109/MILCOM.2008.4753187.
  • [5] DAN-HUA LIU, GUANG-MING SHI, DA-HUA GAO, MIN GAO, A robust image encryption scheme over wireless channels, [In] 2009 International Conference on Wireless Communications & Signal Processing, 2009, pp. 1–6, DOI: 10.1109/WCSP.2009.5371631.
  • [6] GESEN ZHANG, SHUHONG JIAO, XIAOLI XU, Application of compressed sensing for secure image coding, [In] Wireless Algorithms, Systems, and Applications, WASA 2010, Lecture Notes in Computer Science, Pandurangan G., Anil Kumar V.S., Ming G., Liu Y., Li Y. [Eds.], Vol. 6221, Springer, Berlin, Heidelberg, 2010, pp. 220–224, DOI: 10.1007/978-3-642-14654-1_27.
  • [7] XINPENG ZHANG, YANLI REN, GUORUI FENG, ZHENXING QIAN, Compressing encrypted image using compressive sensing, [In] 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2011, pp. 222–225, DOI: 10.1109/IIHMSP.2011.12.
  • [8] AIDI ZHANG, NANRUN ZHOU, LIHUA GONG, Color image encryption algorithm combining compressive sensing with Arnold transform, Journal of Computers 8(11), 2013, pp. 2857–2863, DOI: 10.4304/ jcp.8.11.2857-2863.
  • [9] NANRUN ZHOU, AIDI ZHANG, FEN ZHENG, LIHUA GONG, Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing, Optics and Laser Technology 62, 2014, pp. 152–160, DOI: 10.1016/j.optlastec.2014.02.015.
  • [10] LIANSHENG SUI, MINJIE XU, AILING TIAN, Optical noise-free image encryption based on quick response code and high dimension chaotic system in gyrator transform domain, Optics and Lasers in Engineering 91, 2017, pp. 106–114, DOI: 10.1016/j.optlaseng.2016.11.017.
  • [11] LIANSHENG SUI, BEI ZHOU, ZHANMIN WANG, AILING TIAN, An optical color image watermarking scheme by using compressive sensing with human visual characteristics in gyrator domain, Optics and Lasers in Engineering 92, 2017, pp. 85–93, DOI: 10.1016/j.optlaseng.2017.01.003.
  • [12] LIANSHENG SUI, XIAO ZHANG, AILING TIAN, Optical multiple-image authentication scheme based on the phase retrieval algorithm in gyrator domain, Journal of Optics 19(5), 2017, article ID 055702, DOI: 10.1088/2040-8986/aa6506.
  • [13] YUSHU ZHANG, JIANTAO ZHOU, FEI CHEN, LEO YU ZHANG, DI XIAO, BIN CHEN, XIAOFENG LIAO, A block compressive sensing based scalable encryption framework for protecting significant image regions, International Journal of Bifurcation and Chaos 26(11), 2016, article ID 1650191, DOI: 10.1142/ S0218127416501911.
  • [14] GEORGE S.N., PATTATHIL D.P., A secure LFSR based random measurement matrix for compressive sensing, Sensing and Imaging 15(1), 2014, article ID 85, DOI: 10.1007/s11220-014-0085-9.
  • [15] PEI LU, ZHIYONG XU, XI LU, XIAOYONG LIU, Digital image information encryption based on compressive sensing and double random-phase encoding technique, Optik 124(16), 2013, pp. 2514–2518, DOI: 10.1016/j.ijleo.2012.08.017.
  • [16] NANRUN ZHOU, SHUMIN PAN, SHAN CHENG, ZHIHONG ZHOU, Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing, Optics and Laser Technology 82, 2016, pp. 121–133, DOI: 10.1016/j.optlastec.2016.02.018.
  • [17] LIHUA GONG, CHENGZHI DENG, SHUMIN PAN, NANRUN ZHOU, Image compression-encryption algorithms by combining hyper-chaotic system with discrete fractional random transform, Optics and Laser Technology 103, 2018, pp. 48–58, DOI: 10.1016/j.optlastec.2018.01.007.
  • [18] XINGBIN LIU, WENBO MEI, HUIQIAN DU, Simultaneous image compression, fusion and encryption algorithm based on compressive sensing and chaos, Optics Communications 366, 2016, pp. 22–32, DOI: 10.1016/j.optcom.2015.12.024.
  • [19] GEORGE S.N., PATTATHIL D.P., A novel approach for secure compressive sensing of images using multiple chaotic maps, Journal of Optics 43(1), 2014, pp. 1–17, DOI: 10.1007/s12596-013-0147-8.
  • [20] YUSHU ZHANG, LEO YU ZHANG, JIANTAO ZHOU, LICHENG LIU, FEI CHEN, XING HE, A review of compressive sensing in information security field, IEEE Access 4, 2016, pp. 2507–2519, DOI: 10.1109/ ACCESS.2016.2569421.
  • [21] MALLAT S.G., ZHANG Z.F., Matching pursuits with time-frequency dictionaries, IEEE Transactions Signal Processing 41(12), 1993, pp. 3397–3415, DOI: 10.1109/78.258082.
  • [22] BAJWA W.U., HAUPT J.D., RAZ G.M., WRIGHT S.J., NOWAK R.D., Toeplitz-structured compressed sensing matrices, [In] 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, 2007, pp. 294–298, DOI: 10.1109/SSP.2007.4301266.
  • [23] GUOYUAN QI, GUANRONG CHEN, Analysis and circuit implementation of a new 4D chaotic system, Physics Letters A 352(4–5), 2006, pp. 386–397, DOI: 10.1016/j.physleta.2005.12.030.
  • [24] LIANSHENG SUI, KUAIKUAI DUAN, JUNLI LIANG, A secure double-image sharing scheme based on Shamir’s three-pass protocol and 2D sine logistic modulation map in discrete multiple-parameter fractional angular transform domain, Optics and Lasers in Engineering 80, 2016, pp. 52–62, DOI: 10.1016/j.optlaseng.2015.12.016.
  • [25] ZHAN YU, CHANGLUN ZHANG, HENGYOU WANG, NAN NING, Digital image multiple encryption algorithm based on compressive sensing, [In] Proceedings of the 2016 4th International Conference on Sensors, Mechatronics and Automation (ICSMA 2016), Advances in Intelligent Systems Research, Vol. 136, 2016, pp. 657–661, DOI: 10.2991/icsma-16.2016.114.
  • [26] GUIQIANG HU, DI XIAO, YONG WANG, TAO XIANG, An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications, Journal of Visual Communication and Image Representation 44, 2017, pp. 116–127, DOI: 10.1016/j.jvcir.2017.01.022.
  • [27] YE ZHANG, BIAO XU, NANRUN ZHOU, A novel image compression–encryption hybrid algorithm based on the analysis sparse representation, Optics Communications 392, 2017, pp. 223–233, DOI: 10.1016/ j.optcom.2017.01.061.
  • [28] NANRUN ZHOU, HAO JIANG, LIHUA GONG, XINWEN XIE, Double-image compression and encryption algorithm based on co-sparse representation and random pixel exchanging, Optics and Lasers in Engineering 110, 2018, pp. 72–79, DOI: 10.1016/j.optlaseng.2018.05.014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ac2cfbbd-ab17-4e7d-be40-a6ee000655c9
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