用量子行走优化量化表，提升JPEG压缩保真度
Quantum walk inspired JPEG compression of images
- 基于量子行走搜索最优频率带缩放参数
- 平均提升3-6 dB PSNR，边缘结构更清晰
- 兼容现有JPEG解码器，适合实际部署
本文提出一种受量子启发的自适应量化框架，通过量子行走启发优化（QWIO）策略学习并优化量化表（Qtable），在统一率失真目标下搜索连续频段缩放因子空间，以兼顾重建保真度与压缩效率。在MNIST、CIFAR10和ImageNet子集上评估，采用峰值信噪比（PSNR）、结构相似性指数（SSIM）、每像素比特数（BPP）及误差热图可视化作为指标。实验显示，平均PSNR提升3至6 dB，边缘、轮廓和亮度过渡结构保留更优，且无需修改解码器兼容性。该方法保持JPEG标准兼容，可使用常见科学工具包实现，适用于实际部署与研究。
原文摘要 · Abstract (English)
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.
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