Abstract
An enhanced quantum-based image fidelity metric, the QIFM metric, is proposed as a tool to assess the “congruity” between two or more quantum images. The often confounding contrariety that distinguishes between classical and quantum information processing makes the widely accepted peak-signal-to-noise-ratio (PSNR) ill-suited for use in the quantum computing framework, whereas the prohibitive cost of the probability-based similarity score makes it imprudent for use as an effective image quality metric. Unlike the aforementioned image quality measures, the proposed QIFM metric is calibrated as a pixel difference-based image quality measure that is sensitive to the intricacies inherent to quantum image processing (QIP). As proposed, the QIFM is configured with in-built non-destructive measurement units that preserve the coherence necessary for quantum computation. This design moderates the cost of executing the QIFM in order to estimate congruity between two or more quantum images. A statistical analysis also shows that our proposed QIFM metric has a better correlation with digital expectation of likeness between images than other available quantum image quality measures. Therefore, the QIFM offers a competent substitute for the PSNR as an image quality measure in the quantum computing framework thereby providing a tool to effectively assess fidelity between images in quantum watermarking, quantum movie aggregation and other applications in QIP.
Highlights
Quantum image processing (QIP), which is an emerging sub-discipline that seeks to extend traditional image processing tasks to the quantum computing realm [1], has attracted a lot of attention from practitioners that come from divergent backgrounds, notably those from the quantum computing, computer science, and engineering fields [2].The first published material relating quantum computers to image processing can be traced to the work [3] by Vlasov in 1996, wherein the use of “analogue” quantum computing hardware to describe image analysis was mooted
QIPsub-discipline sub-disciplineover overthe thelast lastfew fewyears, years,itit has still continued to rely on classical techniques, notations, and formulations, notably the has still continued to rely on classical techniques, notations, and formulations, notably the PSNR
Contending that such a metric does not conform with the intricacies inherent to quantum computing and quantum image processing (QIP) technologies, in this study, we have appraised the need for, and formulated a wholly quantum-based metric, the quantum image fidelity metric (QIFM), which could be used to efficiently assess the congruity between two or more quantum images
Summary
Quantum image processing (QIP), which is an emerging sub-discipline that seeks to extend traditional (i.e., classical or digital) image processing tasks to (or using resources from) the quantum computing realm [1], has attracted a lot of attention from practitioners that come from divergent backgrounds, notably those from the quantum computing, computer science, and engineering fields [2]. In addition to the complexity of execution, all the measurement operations in [23] are performed on the strip wire, which, as observed earlier, disrupts the “quantumness” of the system and the resulting read outs lead to a complete collapse of the system with which comes the loss of the hitherto quantum information encoded in the system In many instances, such as quantum movie production or database search [15], these losses could be too expensive to bear. Most QIP literature assumes operability (or execution) based on the circuit-model of quantum computation In such models, classical versions of the images are used to prepare a compact unit in a or database search [15], these losses could be too expensive to bear.
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