图像分块压缩感知观测值的DPCM非均匀量化方法

在实际的信号处理中，有必要对采集到的信号进行量化处理。量化是信号数字化、实现数字信号高效传输的必要步骤。图像分块压缩感知(Block Compressed Sensing, BCS)观测模型中,测量域上图像相邻块的观测值之间存在较强的相关性。根据这一特点，本文应用差分脉冲编码调制(Differential pulse-code modulation, DPCM)系统减小相邻块之间的冗余，并结合非均匀标量量化，对分块压缩感知图像的观测值进行量化处理。文中分析了DPCM系统的预测误差概率分布，发现在统计意义上这一分布与非均匀量化特性的变化趋势具有一致性，并以此作为所提出的量化方法的理论基础。仿真实验表明，本文提出的量化方案有效地提高了压缩感知观测值的量化信噪比(quantized signal to noise ratio, quantized SNR)，同时图像的重构质量得到了提升。 

Nonuniform Quantization For Block-based Compressed Sensing of Images In DPCM system

In practical signal processing, it is necessary to quantize the sampled signals. Quantization is considered a necessary step to digitalize signals and realize the high-efficient transmission of digital signals. There exists high correlation from one block to the next in the measurement domain for block-based compressed sensing of images. According to this feature, this paper employed differential pulse-code modulation to reduce the redundancy between two blocks, and which was coupled with nonuniform scalar quantization to provide quantized block-based compressed sensing of images. This paper analyzed the distribution of prediction errors in DPCM system and drew a conclusion that in statistical sense such distribution has a similar varying tendency with the characteristics of nonuniform scalar quantization. This discovery provided a theoretical basis for the proposed quantization method in this paper. Experimental results show that the proposed quantization scheme effectively increases the quantized signal to noise ratio, meanwhile improves the quality of image reconstruction.