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.

翻译：本研究提出一种量子启发的自适应量化框架，通过引入基于量子行走启发优化搜索策略学习得到的优化量化表，以增强经典JPEG压缩性能。该优化器在统一率失真目标下搜索频带缩放因子的连续参数空间，该目标同时考虑重建保真度与压缩效率。所提框架在MNIST、CIFAR10及ImageNet子集上进行评估，采用峰值信噪比、结构相似性指数、每像素比特数及误差热力图可视化分析作为评价指标。实验结果表明，在保持解码器兼容性的前提下，平均可获得3至6 dB的PSNR提升，同时能更好地保持边缘、轮廓和亮度过渡的结构完整性。该结构完全兼容JPEG标准，可通过易获取的科学计算包实现，非常适合实际部署与应用研究。