In this paper, we describe the integration of quantum entropy and classical IQA (Image Quality Assessment) metrics based on the 8-qubit NEQR (Novel Enhanced Quantum Representation) quantum image coding method. NEQR technology allows encoding image intensities through quantum computation, which helps in efficient storage and processing of high-resolution images. Quantum entropy blocks allow for the estimation of the information density of quantum images, while comparing image quality and accuracy using classical IQA metrics (ACMO, BREN, CONT, GRAE, HISE, LAPD, Contrast, Sharpness) and reference metrics (MSE, PSNR, SSIM). The developed pipeline visualization is presented in a diagram and shows the connections between the quantum encoding process and classical assessment.
In this paper, we describe the integration of quantum entropy and classical IQA (Image Quality Assessment) metrics based on the 8-qubit NEQR (Novel Enhanced Quantum Representation) quantum image coding method. NEQR technology allows encoding image intensities through quantum computation, which helps in efficient storage and processing of high-resolution images. Quantum entropy blocks allow for the estimation of the information density of quantum images, while comparing image quality and accuracy using classical IQA metrics (ACMO, BREN, CONT, GRAE, HISE, LAPD, Contrast, Sharpness) and reference metrics (MSE, PSNR, SSIM). The developed pipeline visualization is presented in a diagram and shows the connections between the quantum encoding process and classical assessment.
This paper examines Quantum Image Representation Algorithms (QIRA), which have significantly advanced the encoding and manipulation of visual data using quantum computing. It explores key developments in image representation techniques, focusing on Flexible Representation of Quantum Images (FRQI), Novel Enhanced Quantum Representation (NEQR), Generalized Quantum Image Representation (GQIR), Bit Plane Representation of Quantum Images (BRQI), and Order Encoding Quantum Image Model (OQIM). Using the Qiskit framework, these methods are implemented to encode classical images into quantum states and perform various quantum operations. The paper provides a comprehensive understanding of QIRA by outlining the background of these representation techniques and rigorously evaluating their performance in image reconstruction. The evaluation is based on metrics such as Mean Squared Error (MSE), Cosine Similarity, Normalized Cross-Correlation (NCC), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR).
Quantum image processing represents a transformative approach to visual data analysis, leveraging the principles of quantum computing to overcome classical limitations. This work explores two prominent quantum image encoding methods: FRQI (Flexible Representation of Quantum Images) and NEQR (Novel Enhanced Quantum Representation). FRQI excels in qubit efficiency, making it suitable for hardware implementation, while NEQR offers superior precision in pixel intensity representation, ideal for complex image processing tasks. We detail the implementation of these algorithms, including preprocessing, quantum circuit design, and simulation, using platforms like Qiskit. The study highlights the potential of quantum image processing in fields such as medicine, industry, and environmental monitoring, while addressing challenges like qubit limitations and noise sensitivity. This research contributes to advancing quantum computing applications, paving the way for innovative and sustainable technological solutions.
Superposition, entanglement, and quantum parallelism can be utilized in Quantum Image Processing (QIP) as a promising paradigm for expediting large-scale visual computing workloads. However, the majority of existing content-agnostic Quantum Image Representation (QIR) models allocate an equal amount of computational resources to individual pixels, irrespective of their semantic significance. This approach poses particular challenges to Noisy Intermediate-Scale Quantum (NISQ) systems, especially in noise-sensitive applications such as medical imaging and maritime surveillance, where low coherence and noisy gates significantly constrain the achievable circuit depth. In order to apply dynamically local resource assignment conditions by the complexity of the image, this paper presents EBA-QR (Entropy-Based Adaptive Quantum Representation), a content- sensitive encoding scheme, which combines a local Shannon entropy scheme and quantum circuit synthesis. EBA-QR does not process the pixels as equals but divides the image into discrete blocks, calculates the number of pixels in each block, and the block entropy, then uses a threshold, which is adjustable, below which each block is considered to be part of Interest (ROI), or the background. In contrast to low- entropy background blocks, which are coded with gate-skipping algorithms to approximate a fixed cost of a gate per block, high- entropy blocks are coded with the NEQR-like basis-state accuracy. The total cost of preparation is proportional to the size of the regions of ROI, and a formal gate-cost optimization function can also be applied to work with a reduction of up to two orders of magnitude compared to NEQR in sparse sceneries without increasing the number of qubits. These benefits are observed to be the effect of entropy-directed gate allocation, not necessarily of classical masking by an Entropy-Based Mask (EBM) control of a NEQR +Mask control baseline that retains the normal NEQR circuit. There are three datasets that were applied as SAR ship detection imaging based on SSDD, brain tumor MRI scans, and so on. Images of Doha International Airport and ICEYE SAR are benchmarked and tested on 10 performance parameters. The development of Qiskit simulators is accomplished. Circuit and all graphics are downsampled to 32 x 32 so as to look NISQ-era. The findings indicate that EBA-QR possesses the fidelity of the level of NEQR in ROIs and is not prone to common aberrations of transform-based compression schemes, and by a maximum of 78.1 percent on sparse SAR sceneries, simplifying the complexity of gates. 64.7% on brain MRI. Moreover, EBA-QR consents that it is helpful. End to-End image processing system in the NISQ era and a successful building block in future quantum pipelines, edge detection, and medical diagnostics satellite monitoring to help in basic operations on images, like geometric transform, image flipping, and Sobel edge detection.
This study presents a comparative evaluation of three quantum image representation techniques: FRQI, QPIE, and NEQR. The Baboon image is used as a benchmark, and performance is assessed through PSNR, MSE, SSIM, and execution time. Simulations were conducted using a noiseless quantum simulator to ensure fair comparison across methods. Results indicate that QPIE achieves the best reconstruction quality, with PSNR = 117.97, MSE = 1.037, and SSIM = 0.975 for the 8 x 8 image, while maintaining strong performance for larger sizes. In contrast, FRQI shows lower quality and high execution time. NEQR is computationally faster (4.85 s) but delivers moderate quality (PSNR = 79.38, SSIM = 0.859). Additionally, QPIE requires fewer qubits, making it more resource-efficient. Overall, QPIE emerges as the most effective representation, striking a balance between image quality, computational cost, and qubit utilization. These findings highlight QPIE as a promising candidate for future quantum image processing tasks such as compression, edge detection, and negative transformation.
In the era of big data and artificial intelligence, the increasing volume of data and the demand to solve more and more complex computational challenges are two driving forces for improving the efficiency of data storage, processing, and analysis. Quantum image processing is an interdisciplinary field between quantum information science and image processing, which has the potential to alleviate some of these challenges by leveraging the power of quantum computing. In this work, we compare and examine the compression properties of four different quantum image representations (QImRs): namely, tensor network representation (TNR), flexible representation of quantum image (FRQI), novel enhanced quantum representation (NEQR), and quantum probability image encoding (QPIE). Our simulations show that FRQI and QPIE perform a higher compression of image information than TNR and NEQR. Furthermore, we investigate the trade-off between accuracy and memory in binary classification problems, evaluating the performance of quantum kernels based on QImRs compared to the classical linear kernel. Our results indicate that quantum kernels provide comparable classification average accuracy but require exponentially fewer resources for image storage.
In quantum image processing, a fundamental step is encoding classical image data into quantum states. This can be achieved using methods such as Flexible Representation of Quantum Images (FRQI), Quantum Probability Image Encoding (QPIE), and Novel Enhanced Quantum Representation (NEQR). However, on real quantum hardware, these encodings can quickly lead to circuits with many gates, large circuit depth, and high qubit usage, which is a problem for Noisy Intermediate-Scale Quantum (NISQ) devices. In this work, we investigate whether low-rank state approximation, formulated via Schmidt decomposition, can help reduce this complexity. The method keeps only the most significant parts of a quantum state's entanglement structure, making state preparation more efficient while preserving most of the image information. We compare the three encoding techniques in their original form and with low-rank approximation, evaluating metrics such as circuit depth, CNOT count, MSE, and visual quality of reconstructed images. The results reveal meaningful trade-offs between accuracy and resource efficiency, with the FRQI model achieving a 97 percent reduction in circuit depth while maintaining a near-perfect reconstruction (MSE of about 0.27). This demonstrates the potential of low-rank techniques for advancing practical quantum image processing on near-term hardware.