Quantum walk inspired JPEG compression of images
Abhishek Verma, Sahil Tomar, Sandeep Kumar

TL;DR
This paper introduces a quantum-inspired adaptive quantization method that improves JPEG image compression by optimizing quantization tables through a quantum walk inspired search, resulting in higher image quality and structural preservation.
Contribution
It presents a novel quantum walk inspired optimization strategy for adaptive quantization in JPEG, enhancing compression quality without altering decoder compatibility.
Findings
Achieved 3-6 dB PSNR improvement on tested datasets.
Improved structural preservation of image features.
Maintained JPEG compliance and decoder compatibility.
Abstract
This work proposes a quantum inspired adaptive quantization framework that enhances the classical JPEG compression by introducing a learned, optimized Qtable derived using a Quantum Walk Inspired Optimization (QWIO) search strategy. The optimizer searches a continuous parameter space of frequency band scaling factors under a unified rate distortion objective that jointly considers reconstruction fidelity and compression efficiency. The proposed framework is evaluated on MNIST, CIFAR10, and ImageNet subsets, using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Bits Per Pixel (BPP), and error heatmap visual analysis as evaluation metrics. Experimental results show average gains ranging from 3 to 6 dB PSNR, along with better structural preservation of edges, contours, and luminance transitions, without modifying decoder compatibility. The structure remains JPEG…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum-Dot Cellular Automata
