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.
Suresh et al. (2026) studied this question.