Evidence map›Paper›PMID 41634231›Full record

ArticleScientific reports2026

Quantum optimized hierarchical chunk encoding with robust embedding for perceptual integrity and compression tolerant visual data protection.

G Suresh, J Arun Kumar, Vivek Karthick Perumal, S Ramkumar

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

G SureshDepartment of Artificial Intelligence and Machine Learning, Panimalar Engineering College, Chennai, India. gsuresh.aiml@panimalar.ac.in.
J Arun KumarDepartment of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600 062, India.
Vivek Karthick PerumalDepartment of Electronics and Communication Engineering, Sona College of Technology, Salem, India.
S RamkumarDepartment of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The multimedia communication and image-sharing platform development has increased exponentially, which enhances the threat of unauthorized entry, degradation through compression, and the manipulation of the visual data. The classical watermarking methods that are based on fixed-domain transforms are likely DCT and DWT are not always adaptive to different compression rates, and do not have high robustness to adversarial environments. In addition, methods that are available are limited by low levels of imperceptibility or computation, which hinder real time or large-scale application. In order to overcome such limitations, a new Quantum-Optimized Hierarchical Chunk Encoding (QHCE) model of perceptually adaptive and compression-sensitive image watermarking is proposed. The offered approach incorporates the entropy-based quadtree partitioning of the image into chunks, saliency-based region selection, and transform-domain embedding of watermark with the use of multi-layers DWT. Quantum Genetic Algorithm (QGA) is used to find the optimal location, band and strength of the embedding parameter that results in a good trade-off between the robustness and the visual fidelity. The model was trained with Python 3.10 and PyWavelets and Qiskit and tested on Kodak image dataset at different JPEG compression ratios. Experimental results demonstrate a significant improvement over baseline methods, with an average PSNR of 57.8 dB, SSIM of 0.997, 0.00% BER, and 100% extraction accuracy at QF ≥ 70. The QGA-based optimization also achieved a 19% increase in payload capacity and a 25% reduction in runtime compared to non-optimized baselines. Integrity verification reached 99.95% accuracy using SHA-256 and Hamming distance analysis. Integrating quantum-inspired optimization within a perceptually driven encoding pipeline offers a scalable, secure, and highly robust solution for next-generation visual data protection. This provides a compelling direction for future research in quantum-secure multimedia systems and watermark resilience.

Indexed as

Compression-tolerant watermarkingHierarchical chunk encodingImage protectionQuantum-optimized embeddingSecure data embeddingVisual integrity

Identifiers

PMID41634231
PMCPMC12921234

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.