Mostly existing quantum image models represent image in Cartesian coor- dinates. Some complicated affine transformation can not be easily handled in Cartesian coordinates. In this paper, a new representation of Multi-Channel log-polar quantum im- age (MCLPQI) sampled in log-polar coordinates is proposed. This form contains the im- age RGB color information and transparency of the corresponding pixel. Then we give the polynomial preparation process which proves that our quantum image representation state can be made in a feasible time complexity. Finally we discussed color transfor- mation among different channels and single channel color transformations, transparency adjustments. Analysis demonstrates that the MCLPQI is a efficient quantum log-polar color image representation.
Mostly existing quantum image models represent image in Cartesian coor- dinates. Some complicated affine transformation can not be easily handled in Cartesian coordinates. In this paper, a new representation of Multi-Channel log-polar quantum im- age (MCLPQI) sampled in log-polar coordinates is proposed. This form contains the im- age RGB color information and transparency of the corresponding pixel. Then we give the polynomial preparation process which proves that our quantum image representation state can be made in a feasible time complexity. Finally we discussed color transfor- mation among different channels and single channel color transformations, transparency adjustments. Analysis demonstrates that the MCLPQI is a efficient quantum log-polar color image representation.
To address the limitations of existing models that predominantly represent data in Cartesian coordinate systems and RGB color representations, this paper proposes YCrCbQPI (A Quantum Polar Coordinate Color Image Representation Model Based on YCrCb Color Space, YCrCb-QPI). This model integrates luminance and chrominance separation properties of YCrCb with the geometric invariance of logarithmic polar coordinates. Through a differentiated quantum encoding strategy, more qubits are allocated to critical luminance information while less sensitive chrominance information is assigned simplified encoding. Corresponding quantum state preparation circuits are also designed. Theoretical analysis indicates that compared to traditional RGB polar coordinate models, this model achieves significant savings in color information encoding resources while maintaining comparable visual fidelity. Furthermore, the model inherently possesses the rotation and scaling invariance of polar coordinates, establishing a robust foundation for developing efficient quantum image processing algorithms.
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.
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 2n 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 processing is a new and exciting subdiscipline of quantum computation that aims to extend classical image processing applications on a quantum computer.The research starts with a proposal of a representation of images on a quantum computer, named Multi-Channel Representation for Quantum Images (MCQI).This representation encodes the positions and their corresponding colors in the images as a normalized quantum state.For an n by n sized image, 2n qubits and 3 qubits are used to carry the position and color respectively.The preparation procedure of a quantum image is discussed by the multi-channel polynomial preparation, which turns the initial quantum states to the MC-QI quantum states.The image retrieval procedure by quantum measurement is proposed which converts the quantum states to classical images.Furthermore, based on MCQI, some categories of possible processing transformations are suggested.By considering the structure of MCQI representation, the Channel of Interest (CoI), the Channel Swapping (CS), the Color Space Transformation (CST), and Alpha Blending (αB) quantum image processing operators are discussed using unitary transformations with low complexity.All the 4 operators are used to process the information about color in the images.Their complexity are analyzed in terms of the number of basic quantum gates which are needed to build the transformations, and they can be built by 9, 3, 287, 28 basic quantum gates, respectively.Some strategies for designing new transformations from known ones are proposed as well.These strategies can be used to design further algorithms for image processing.Based on the MCQI representation and the four image processing operators, a quantum measurement based double-key double-domain watermarking strategy for multichannel quantum images (MC-WaQI) is developed.In these application, a revised of the original MCQI representation and some restricted versions of the transformations are considered.The research provides the foundation to design other quantum image processing algorithms and can be extend to the following areas:• MCQI representation can be used to build quantum image database,• new frequency operations on quantum images based on quantum Fourier quantum discrete cosine, and quantum wavelet transformations,• building the special effect on color quantum movie,• image data transmission by quantum communication.
A Multi-Channel Representation for Quantum Image (MCRQI) is proposed to facilitate the further image processing tasks based on the Flexible Representation for Quantum Image (FRQI). Channel Swapping Operation, One Channel Operation, are proposed as basic image processing operations on MCRQI representation. The simulation experiment results on classical computer show that the channel information of R G B (color) and α (transparency) can be carried easily on quantum computer by employing three qubits to represent the color space, and also indicate that this MCRQI is very flexible to realize some classic-like operations. The MCRQI provides a foundation not only express image in RGBα color space, but also to explore theoretical and practical aspects of image processing on quantum computer.
Quantum image representation (QIR) is a necessary part of quantum image processing (QIP) and plays an important role in quantum information processing. To address the problems that NCQI cannot handle images with inconsistent horizontal and vertical position sizes and multi-channel image processing, an improved color digital image quantum representation (INCQI) model based on NCQI is proposed in this paper. The INCQI model can process color images and facilitate multi-channel quantum image transformations and transparency information processing of images using auxiliary quantum bits. In addition, the quantum image control circuit was designed based on INCQI. And quantum image preparation experiments were conducted on IBM Quantum Experience (IBMQ) to verify the feasibility and effectiveness of INCQI quantum image preparation. The prepared image information was obtained by quantum measurement in the experiment, and the visualization of quantum information was successfully realized. The research in this paper has some reference value for the research related to QIP.