Quantum walk inspired JPEG compression of images

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 compliant and can be implemented using accessible scientific packages making it ideal for deployment and practical research use.