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Volume 13 | Issue 4 | Year 2026 | Article Id. IJECE-V13I4P128 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I4P128

An Entropy-Based Adaptive Quantum Image Representation (EBA-QR) for Efficient Multimodal Image Processing

Vrushali Nikam, Shirish Sane

Received Revised Accepted Published
24 Jan 2026 24 Feb 2026 25 Mar 2026 30 Apr 2026

Citation :

Vrushali Nikam, Shirish Sane, "An Entropy-Based Adaptive Quantum Image Representation (EBA-QR) for Efficient Multimodal Image Processing," International Journal of Electronics and Communication Engineering, vol. 13, no. 4, pp. 348-370, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I4P128

Abstract

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.

Keywords

Quantum Image Processing, Quantum Image Representation, EBA-QR, Entropy-Based Encoding, NEQR, NISQ Devices, SAR Ship Detection, Brain Tumor MRI, Quantum Edge Detection.

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