Quantum walk inspired JPEG compression of images
arXiv:2602.12306v1 Announce Type: cross 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 scie
arXiv:2602.12306v1 Announce Type: cross 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.
Executive Summary
The article introduces a novel quantum-inspired adaptive quantization framework designed to enhance classical JPEG compression. By employing a Quantum Walk Inspired Optimization (QWIO) search strategy, the framework generates an optimized quantization table (Qtable) that improves both reconstruction fidelity and compression efficiency. Evaluated on datasets such as MNIST, CIFAR10, and subsets of ImageNet, the method demonstrates significant improvements in Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM), while maintaining compatibility with existing JPEG decoders. The approach leverages accessible scientific packages, making it practical for deployment and further research.
Key Points
- ▸ Introduction of a quantum-inspired optimization strategy for JPEG compression.
- ▸ Significant improvements in PSNR and SSIM metrics.
- ▸ Compatibility with existing JPEG decoders.
- ▸ Evaluation on diverse datasets including MNIST, CIFAR10, and ImageNet.
- ▸ Practical deployment potential due to accessibility of scientific packages.
Merits
Innovative Optimization Strategy
The use of Quantum Walk Inspired Optimization (QWIO) represents a novel approach to optimizing quantization tables, potentially offering superior performance compared to classical methods.
Improved Image Quality
The framework achieves notable gains in PSNR and SSIM, indicating better reconstruction fidelity and structural preservation of images.
Compatibility and Accessibility
The method maintains compatibility with existing JPEG decoders and utilizes accessible scientific packages, facilitating easy adoption and further research.
Demerits
Limited Dataset Evaluation
While the evaluation includes diverse datasets, the subsets of ImageNet used may not fully represent the complexity and variability of real-world images.
Computational Complexity
The computational overhead of the QWIO strategy compared to classical methods is not thoroughly addressed, which could impact its practicality in resource-constrained environments.
Generalization to Other Formats
The focus on JPEG compression limits the immediate applicability of the findings to other image compression formats.
Expert Commentary
The article presents a compelling advancement in the field of image compression by introducing a quantum-inspired optimization strategy. The significant improvements in PSNR and SSIM metrics, coupled with the framework's compatibility with existing JPEG decoders, underscore its practical potential. However, the computational complexity and the limited evaluation on real-world datasets warrant further investigation. The method's accessibility and the potential for integration into current image processing pipelines make it a valuable contribution to both academic research and industrial applications. Future work should focus on addressing the computational overhead and expanding the evaluation to more diverse and complex datasets to ensure the robustness and generalizability of the proposed framework.
Recommendations
- ✓ Conduct a more comprehensive evaluation on a wider range of real-world images to assess the robustness and generalizability of the proposed framework.
- ✓ Investigate the computational complexity of the QWIO strategy and explore potential optimizations to reduce overhead, making it more suitable for resource-constrained environments.
