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IEEE Access

A Quantum Image Representation Based on Bitplanes

Hai-Sheng Li, Xiao Chen, Haiying Xia, Yan Liang, Zuoshan Zhou

Published Jan 1, 2018• 91 citations
Publisher

Abstract

Quantum image representation plays an important role in quantum image processing. In this paper, a bitplane representation of quantum images (BRQI) is proposed, which uses (n+4) or (n+6) qubits to store a grayscale or RGB color image of 2npixels. Compared with a novel enhanced quantum representation (NEQR), and a novel quantum representation of color digital images (NCQI), the storage capacity of BRQI improves 16 times and 218times, respectively. Next, some quantum operations based on BRQI are proposed, these operations including complement of colors, reverse of bitplanes, and translation of bitplanes. Analyzed the implementation circuits of these operations, the result indicates that these operations based on BRQI have lower quantum cost than NEQR and NCQI. Furthermore, an image scrambling algorithm based on BRQI is presented by combining the above operations. Simulation experiments and performance analysis show that proposed scrambling algorithm is effective and efficient.

DOI
10.1109/access.2018.2871691

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Quantum image representation plays an important role in quantum image processing. In this paper, a bitplane representation of quantum images (BRQI) is proposed, which uses (n+4) or (n+6) qubits to store a grayscale or RGB color image of 2npixels. Compared with a novel enhanced quantum representation (NEQR), and a novel quantum representation of color digital images (NCQI), the storage capacity of BRQI improves 16 times and 218times, respectively. Next, some quantum operations based on BRQI are proposed, these operations including complement of colors, reverse of bitplanes, and translation of bitplanes. Analyzed the implementation circuits of these operations, the result indicates that these operations based on BRQI have lower quantum cost than NEQR and NCQI. Furthermore, an image scrambling algorithm based on BRQI is presented by combining the above operations. Simulation experiments and performance analysis show that proposed scrambling algorithm is effective and efficient.
Quantum image representation has a significant impact in quantum image processing. In this paper, a bit-plane representation for log-polar quantum images (BRLQI) is proposed, which utilizes (n+4)(n+4)(n+4) or (n+6)(n+6)(n+6) qubits to store and process a grayscale or RGB color image of 2n2^n2n pixels. Compared to a quantum log-polar image (QUALPI), the storage capacity of BRLQI improves 16 times. Moreover, several quantum operations based on BRLQI are proposed, including color information complement operation, bit-planes reversing operation, bit-planes translation operation and conditional exchange operations between bit-planes. Combining the above operations, we designed an image scrambling circuit suitable for the BRLQI model. Furthermore, comparison results of the scrambling circuits indicate that those operations based on BRLQI have a lower quantum cost than QUALPI. In addition, simulation experiments illustrate that the proposed scrambling algorithm is effective and efficient.
Abstract The quantum image representation based on bit-planes (BRQI) is a widely used model in quantum image processing. It utilizes both the superposition and entanglement properties to store the position and color information of an image effectively, but the time complexity is still high(O(2qn22n)). Here, we propose an improved BRQI model, which still uses log2 q + 1 qubits to represent the color information, and has a low time complexity(O(qn22n)). When storing the value of bit-plane, it can use only one controlled operation to store the values of two adjacent bit-planes at the same time, makes the time and space complexity significantly reduced. Moreover, we design a corresponding quantum image compression algorithm. Simulation experimental results show that the compression efficiency of this model is also better than that of the widely used models NEQR and BRQI, and the time complexity of quantum image representation is further reduced. Hence, our findings will be helpful for further developing the quantum image representation and compression algorithm.
Abstract Data encryption is one of the main factors in their transmission. In this paper, we have described an algorithm for color image encryption, some quantum encryption and image scrambling operations will be performed based on bitplane representation of quantum images (BRQI). Next, the image created using the logistic map (key image) is blended with the output of the first step, which was possible with the help of a quantum comparator circuit. Finally we create a secure image as the output of the encryption algorithm. Experiments with simulation and analysis of parameters such as entropy and correlation coefficient of adjacent pixels in three main positions show that the proposed algorithm has been very efficient and effective in securing and encrypting color images.
RGB multi channel representation is proposed for images on quantum computers (MCQI) that captures information about colors (RGB channels) and their corresponding positions in an image in a normalized quantum state. The proposed representation makes it possible to store the RGB information about an image simultaneously by using 2n+3 qubits for encoding 2n× 2npixel images, whereas pixel-wise processing is necessary in many other quantum image representations, e.g., qubit lattice, grid qubit, and quantum lattice. Simulation of storage and retrieval of MCQI images using human facial images demonstrated that 15 qubits are required for encoding 64 × 64 colored images, and encoded information is retrieved by measurement. Perspectives of designing quantum image operators are also discussed based onMCQI representation, e.g., channel of interest, channel swapping, and restrict version of color transformation.
Image scrambling on classical computers has been widely studied, however, the corresponding quantum algorithms are rare. This paper proposes a block-based image scrambling scheme for the generalized model of novel enhanced quantum representation (GNEQR). It is not only suit for grayscale images and color images, but also can be used in 2n× 2mimages (i.e., rectangular images). To realize the image scrambling method, we use geometric transformations and an operation of bitplane scrambling to perform position and pixel scrambling, respectively. Furthermore, the complexity of the implementation circuit of the proposed image scrambling is only O(n2) for a 2n× 2nimage. Therefore, it shows our proposed quantum image scrambling is efficient. Then, the result analysis of simulation experiments demonstrates that our proposed image scrambling is effective. In addition, security analysis shows that the proposed scrambling algorithm is secure.
Techniques for exploiting visual information play an important and topical role. Image processing is an area of Artificial Intelligence, dealing with how to represent, reconstruct, classify, recognition and analysis of images using computers. The idea behind this study is to follow how the quantum properties of superposition and entanglement are used by the Bit-plane Representation of Quantum Images BRQI model, grayscale image, to map and store the image as qubits. For this, we used the IBM Quantum Experience platform that provides the opportunity to implement and run programs with quantum applications.