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 $(n+4)$ or $(n+6)$ qubits to store and process a grayscale or RGB color image of $2^n$ 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.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 $(n+4)$ or $(n+6)$ qubits to store and process a grayscale or RGB color image of $2^n$ 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.

Quantum Information and ComputationDec 28, 20211 citation

Toward implementing efficient image processing algorithms on quantum computers

Fei Yan, Salvador Elías Venegas-Andraca, Kaoru Hirota

Soft ComputingJan 7, 202236 citations

A multi-channel quantum image representation model with qubit sequences for quantum-inspired image and image retrieval

Nawres A. Alwan, Suzan Jabbar Obaiys, Nadia M. G. Al-Saidi +2 more

Quantum image processing (QIP) has become one of the most significant fields in quantum computing (QC); it merges quantum mechanics with image processing to improve classical image-processing speed, which involves various operations to advance quantum image representation (QIR). Accordingly, we introduce two new QIRs: The first is based on the wavelength and bit plane, called the quantum image representation bit plane (QIRBP), and the second is based on the wavelength and adjacency pixels, which is called the quantum image representation wavelength correlation (QIRWC). The QIRBP model uses $ b+2n+6 $ quantum bit (qubits) to store a digital color image of size $ {2}^{n}\times {2}^{n} $. In contrast, the QIRWC needs $ 2b+4n+8 $ qubits to store a digital color image of size $ {2}^{n}\times {2}^{n} $ and to entangle the wavelength between two neighboring pixels. While the QIRWC approach is more complex, it is also more efficient on the basis of the transformation data. The complexity arises from the level of information being transmitted. In this work, two new representation methods (QIRBP and QIRWC) are proposed to overcome existing QIR weaknesses by enhancing storage efficiency, enabling compact high-resolution representation, improving data transformation through wavelength correlation and pixel adjacency, reducing noise, achieving greater versatility, and advancing scalable QIR. To prove the efficiency of the proposed methods, they were analyzed and compared with other efficient quantum image representations, outlining their similar and different aspects.Quantum image processing (QIP) has become one of the most significant fields in quantum computing (QC); it merges quantum mechanics with image processing to improve classical image-processing speed, which involves various operations to advance quantum image representation (QIR). Accordingly, we introduce two new QIRs: The first is based on the wavelength and bit plane, called the quantum image representation bit plane (QIRBP), and the second is based on the wavelength and adjacency pixels, which is called the quantum image representation wavelength correlation (QIRWC). The QIRBP model uses $ b+2n+6 $ quantum bit (qubits) to store a digital color image of size $ {2}^{n}\times {2}^{n} $. In contrast, the QIRWC needs $ 2b+4n+8 $ qubits to store a digital color image of size $ {2}^{n}\times {2}^{n} $ and to entangle the wavelength between two neighboring pixels. While the QIRWC approach is more complex, it is also more efficient on the basis of the transformation data. The complexity arises from the level of information being transmitted. In this work, two new representation methods (QIRBP and QIRWC) are proposed to overcome existing QIR weaknesses by enhancing storage efficiency, enabling compact high-resolution representation, improving data transformation through wavelength correlation and pixel adjacency, reducing noise, achieving greater versatility, and advancing scalable QIR. To prove the efficiency of the proposed methods, they were analyzed and compared with other efficient quantum image representations, outlining their similar and different aspects.

AIMS MathematicsJan 1, 20255 citations

Quantum Image Processing and Its Applications

J. Jennifer Ranjani

Springer eBooksJan 1, 20190 citations

An improved bit-plane representation of quantum image

Jiale Mu, Xiaofei Li, Xiang-Hua Zhang +1 more

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 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.

Jul 24, 20240 citations

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+6)

qubits to store and process a grayscale or RGB color image of

2^n

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.