Quantum multidimensional color image scaling using nearest-neighbor interpolation based on the extension of FRQI

Reviewing past researches on quantum image scaling, only 2D images are studied. And, in a quantum system, the processing speed increases exponentially since parallel computation can be realized with superposition state when compared with classical computer. Consequently, this paper proposes quantum multidimensional color image scaling based on nearest-neighbor interpolation for the first time. Firstly, flexible representation of quantum images (FRQI) is extended to multidimensional color model. Meantime, the nearest-neighbor interpolation is extended to multidimensional color image and cycle translation operation is designed to perform scaling up operation. Then, the circuits are designed for quantum multidimensional color image scaling, including scaling up and scaling down, based on the extension of FRQI. In addition, complexity analysis shows that the circuits in the paper have lower complexity. Examples and simulation experiments are given to elaborate the procedure of quantum multidimensional scaling.

[1]  Nan Jiang,et al.  Quantum image scaling up based on nearest-neighbor interpolation with integer scaling ratio , 2015, Quantum Information Processing.

[2]  Yonina C. Eldar Quantum signal processing , 2002, IEEE Signal Process. Mag..

[3]  Kaoru Hirota,et al.  A Flexible Representation and Invertible Transformations for Images on Quantum Computers , 2011 .

[4]  Sougato Bose,et al.  Storing, processing, and retrieving an image using quantum mechanics , 2003, SPIE Defense + Commercial Sensing.

[5]  R. Feynman Simulating physics with computers , 1999 .

[6]  Kaoru Hirota,et al.  A flexible representation of quantum images for polynomial preparation, image compression, and processing operations , 2011, Quantum Inf. Process..

[7]  Y. Shih Quantum Imaging , 2007, IEEE Journal of Selected Topics in Quantum Electronics.

[8]  Shen Wang,et al.  Quantum realization of the nearest-neighbor interpolation method for FRQI and NEQR , 2015, Quantum Information Processing.

[9]  H. Ian,et al.  Global and Local Translation Designs of Quantum Image Based on FRQI , 2017, International Journal of Theoretical Physics.

[10]  Peter W. Shor,et al.  Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1995, SIAM Rev..

[11]  P. Benioff Quantum mechanical hamiltonian models of turing machines , 1982 .

[12]  Kaoru Hirota,et al.  Watermarking and authentication of quantum images based on restricted geometric transformations , 2012, Inf. Sci..

[13]  R. Schützhold,et al.  Pattern recognition on a quantum computer , 2003 .

[14]  Kaoru Hirota,et al.  A two-tier scheme for greyscale quantum image watermarking and recovery , 2013 .

[15]  N. Jing,et al.  Geometric transformations of multidimensional color images based on NASS , 2016, Inf. Sci..

[16]  Yajuan Sun,et al.  Quantum multidimensional color images similarity comparison , 2015, Quantum Inf. Process..

[17]  Qingxin Zhu,et al.  Multidimensional color image storage, retrieval, and compression based on quantum amplitudes and phases , 2014, Inf. Sci..

[18]  Nan Jiang,et al.  Quantum image scaling using nearest neighbor interpolation , 2015, Quantum Inf. Process..

[19]  Qingxin Zhu,et al.  Image storage, retrieval, compression and segmentation in a quantum system , 2013, Quantum Inf. Process..

[20]  Abdullah M. Iliyasu Towards Realising Secure and Efficient Image and Video Processing Applications on Quantum Computers , 2013, Entropy.

[21]  Daniel R. Simon On the Power of Quantum Computation , 1997, SIAM J. Comput..

[22]  Kai Lu,et al.  NEQR: a novel enhanced quantum representation of digital images , 2013, Quantum Information Processing.

[23]  Kaoru Hirota,et al.  A FRAMEWORK FOR REPRESENTING AND PRODUCING MOVIES ON QUANTUM COMPUTERS , 2011 .