With the rapid development of network and media technology, there are also many security problems, and the existing media security theories and technologies have limitations in solving massive image and video security problems. Based on the application background of massive media security and confidentiality, this chapter studies the security mechanism of image and video encoding and decoding from the perspective of quantum computing around the perceptual attributes of image and video media, which provides a new way for media processing and security confidentiality. This chapter mainly introduces the significance of quantum image and video processing research, the related concepts of quantum image and video processing, and the research status and analysis of the current situation.
With the rapid development of network and media technology, there are also many security problems, and the existing media security theories and technologies have limitations in solving massive image and video security problems. Based on the application background of massive media security and confidentiality, this chapter studies the security mechanism of image and video encoding and decoding from the perspective of quantum computing around the perceptual attributes of image and video media, which provides a new way for media processing and security confidentiality. This chapter mainly introduces the significance of quantum image and video processing research, the related concepts of quantum image and video processing, and the research status and analysis of the current situation.
Quantum image processing uses quantum computing, which implements the concepts of entanglement and superposition to increase efficiency, scalability, and revolutionary promise in demanding image processing ap plications. In this research paper, a comprehensive frame work for quantum image processing (QIP) is presented, which includes innovative quantum algorithms for pattern recognition, image improvement, and representation. The system presents advanced quantum encoding models for effective image data manipulation and storage. In addition, it discusses the quantum image-based algorithms that give better results than classical techniques in terms of speed and accuracy for image enhancement tasks such as edge recognition, contrast adjustment, and noise reduction. Researchers have developed quantum-based feature ex traction and classification methods for pattern recognition, which demonstrate enhanced scalability and accuracy for complex visual data. It has various applications in satellite data processing, secure visual data transmission, and medical imaging highlighted by simulations and theoretical analysis that confirm the proposed approaches.
Quantum computing is increasingly being viewed as a promising direction for the future of image processing. Conventional approaches to image classification, enhancement, compression, and restoration, although highly developed, often struggle with scalability, computational complexity, and data security—especially when processing high-dimensional or sensitive visual information. This survey explores how quantum computing can help address these limitations through quantum image representation models such as FRQI and NEQR, variational quantum circuits, and hybrid quantum–classical architectures. The primary objective of this review is to present a clear and structured overview of the current state of quantum image processing (QIP). It discusses foundational representation models, emerging hybrid learning techniques, available software frameworks and simulators, benchmarking practices, and practical applications in areas such as medical imaging, remote sensing, and biometric security. Particular attention is given to solutions that are feasible on today’s NISQ-era hardware, including hybrid architectures and quantum-secured image processing schemes. The survey concludes by identifying existing challenges and outlining realistic future research directions. Overall, it emphasizes the practical importance of hybrid models, the need for standardized quantum-oriented benchmarks, and the growing potential of quantum-based security mechanisms for safeguarding visual data.
Quantum video information steganography holds significant research value in the era of quantum computing. Compared to image-based media, videos possess greater storage capacity and redundant information, making them more suitable as carriers for secret information. This chapter introduces a least significant qubit (LSQb) steganography algorithm for quantum videos in the Quantum Video based on Novel Enhanced Quantum Representation (QVNEQR) representation. Based on LSQb and the motion vectors of quantum videos, a quantum video motion vector steganography method is proposed. This algorithm embeds secret information into the motion vectors of quantum videos using the LSQb algorithm, encoding the secret information into the quantum bitstreams of the motion vectors. This approach enhances the security of quantum video steganography.
Image processing is popular in our daily life because of the need to extract essential information from our 3D world, including a variety of applications in widely separated fields like bio-medicine, economics, entertainment, and industry. The nature of visual information, algorithm complexity, and the representation of 3D scenes in 2D spaces are all popular research topics. In particular, the rapidly increasing volume of image data as well as increasingly challenging computational tasks have become important driving forces for further improving the efficiency of image processing and analysis. Since the concept of quantum computing was proposed by Feynman in 1982, many achievements have shown that quantum computing has dramatically improved computational efficiency [1]. Quantum information processing exploit quantum mechanical properties, such as quantum superposition, entanglement and parallelism, and effectively accelerate many classical problems like factoring large numbers, searching an unsorted database, Boson sampling, quantum simulation, solving linear systems of equations, and machine learning. These unique quantum properties may also be used to speed up signal and data processing. In quantum image processing, quantum image representation plays a key role, which substantively determines the kinds of processing tasks and how well they can be performed.
Nowadays, image analysis constitutes a key function of modern smart devices and computer applications. In recent years, image analysis has made significant progress due to the availability of a large volume of data as well as due to the development and promotion of deep learning. GPU graphics cards, as well as big data analysis frameworks (e.g. MapReduce and Spark), mainly meets the computing needs of modern image analysis applications. In this context, recently, quantum computing has emerged as an alternative that can meet the increasing computing needs. This chapter explores the degree of penetration of quantum computing into the field of image analysis, through the recording, editing, and presentation of publications made by the scientific community. The purpose of this chapter is not only to capture the current situation in the field of quantum image analysis (QIA) but also to raise open issues that pose challenges for further research in this research field. This chapter will be a useful guide to the early-stage researchers working in the field of quantum computing, providing the information gathered in a comprehensive and structured way.
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.