This paper proposes an Efficient Framework for Quantum Video (EFQV) in quantum computer. EFQV adds time index information into different frames on BRQI (a Quantum Image Representation based on Bitplanes, BRQI) images, and each bitplanes quantum image adds a time index. Complement of Colors (COC) operator, Reverse of Bitplanes (ROB) operator, and Shift of Frames (SOF) operator are designed for EFQV and its operators are tested on the IBM quantum computation framework qiskit. Experimental results show the effectiveness of EFQV and its operations on quantum computing environment. Compared with other frameworks, EFQV has advantages in time complexity and quantum cost.
This paper proposes an Efficient Framework for Quantum Video (EFQV) in quantum computer. EFQV adds time index information into different frames on BRQI (a Quantum Image Representation based on Bitplanes, BRQI) images, and each bitplanes quantum image adds a time index. Complement of Colors (COC) operator, Reverse of Bitplanes (ROB) operator, and Shift of Frames (SOF) operator are designed for EFQV and its operators are tested on the IBM quantum computation framework qiskit. Experimental results show the effectiveness of EFQV and its operations on quantum computing environment. Compared with other frameworks, EFQV has advantages in time complexity and quantum cost.
To enhance the efficacy of multimedia quantum processing and diminish processing overhead, an advanced multimedia quantum representation model and quantum video display framework are devised. A range of framework processing operators are also developed, including an image color compensation operator, a bit plane inversion operator, and a frame displacement operator. In addition, to address image security issues, two quantum image operations have been proposed: color transformation operation and pixel blending operation. The research results indicated that the grayscale cost of the framework designed in this study was 33.8, the color cost was 40.5, and the total cost was 574 δ. In terms of color, the distribution of image elements in the red, green, and blue (RGB) channels was more balanced. In summary, the quantum video display framework has significant advantages in processing efficiency and image security. Compared to previous studies, the model proposed in this study exhibits higher processing speed and better processing quality in the face of complex image and video data. It effectively addresses the limitations of existing processing techniques while addressing emerging image security issues.
The main goal of this paper is to present a new way of processing a video file using a combination of multiple quantum methods. The design is built upon the novel enhanced quantum representation technique, NEQR, which is then expanded using ideas such as image segmentation, implemented with the help of one or multiple comparators, binarization and cycle shift. This approach allows us to process all frames in parallel according to the desired parameters-one or more thresholds. A demonstration circuit for the proposed design, using a couple of frames, that sums together all the concepts is implemented using the Python programming language and Qiskit open-source framework, made available by IBM. The circuits are analyzed in the experimental section, using the Simulator component and configured using the noise properties of real devices, where we present different relevant metrics obtained by processing the simulation results.
A flexible representation of quantum images (FRQI) was proposed to facilitate the extension of classical (non-quantum)-like image processing applications to the quantum computing domain. The representation encodes a quantum image in the form of a normalized state, which captures information about colors and their corresponding positions in the images. Since its conception, a handful of processing transformations have been formulated, among which are the geometric transformations on quantum images (GTQI) and the CTQI that are focused on the color information of the images. In addition, extensions and applications of FRQI representation, such as multi-channel representation for quantum images (MCQI), quantum image data searching, watermarking strategies for quantum images, a framework to produce movies on quantum computers and a blueprint for quantum video encryption and decryption have also been suggested. These proposals extend classical-like image and video processing applications to the quantum computing domain and offer a significant speed-up with low computational resources in comparison to performing the same tasks on traditional computing devices. Each of the algorithms and the mathematical foundations for their execution were simulated using classical computing resources, and their results were analyzed alongside other classical computing equivalents. The work presented in this review is intended to serve as the epitome of advances made in FRQI quantum image processing over the past five years and to simulate further interest geared towards the realization of some secure and efficient image and video processing applications on quantum computers.
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.
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.
This chapter first presents the general framework structure of quantum image representation, and then introduces some research results on quantum color image representation in our group. In this study, quantum color image representation based on the HSI color space (QIRHSI) is a quantum color image representation based on the hue, saturation, and intensity (HSI) color space in the right-angle coordinate system, novel color quantum image (NCQI) and Color Quantum Image based on Phase Transform (CQIPT) are quantum color image representations based on the RGB color space in the right-angle coordinate system, and MLCPQI is a quantum color image representation based on the RGB color space in the polar coordinate system. In addition, the preparation scheme on a quantum computer and the computational complexity of the prepared image are provided for each quantum color image representation method. Ultimately, a general framework and preparation strategy for quantum video representation are proposed based on quantum image representation.