Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, School of Electronic Engineering, Xidian University, Xi’an, China.
IEEE Trans Image Process. 2012 Jun;21(6):2980-90. doi: 10.1109/TIP.2012.2188810. Epub 2012 Feb 24.
Compressive sensing (CS) has been recently and enthusiastically promoted as a joint sampling and compression approach. The advantages of CS over conventional signal compression techniques are architectural: the CS encoder is made signal independent and computationally inexpensive by shifting the bulk of system complexity to the decoder. While these properties of CS allow signal acquisition and communication in some severely resource-deprived conditions that render conventional sampling and coding impossible, they are accompanied by rather disappointing rate-distortion performance. In this paper, we propose a novel coding technique that rectifies, to a certain extent, the problem of poor compression performance of CS and, at the same time, maintains the simplicity and universality of the current CS encoder design. The main innovation is a scheme of progressive fixed-rate scalar quantization with binning that enables the CS decoder to exploit hidden correlations between CS measurements, which was overlooked in the existing literature. Experimental results are presented to demonstrate the efficacy of the new CS coding technique. Encouragingly, on some test images, the new CS technique matches or even slightly outperforms JPEG.
压缩感知 (CS) 最近受到了热烈的推崇，被认为是一种联合采样和压缩的方法。与传统信号压缩技术相比，CS 的优势在于架构：CS 编码器通过将系统复杂性的大部分转移到解码器，使得信号独立且计算成本低廉。虽然 CS 的这些特性允许在一些使传统采样和编码变得不可能的严重资源匮乏的情况下进行信号采集和通信，但它们伴随着相当令人失望的率失真性能。在本文中，我们提出了一种新的编码技术，在一定程度上纠正了 CS 压缩性能不佳的问题，同时保持了当前 CS 编码器设计的简单性和通用性。主要创新是一种具有分箱的渐进固定率标量量化方案，使 CS 解码器能够利用 CS 测量之间隐藏的相关性，这在现有文献中被忽视了。实验结果表明了新的 CS 编码技术的有效性。令人鼓舞的是，在一些测试图像上，新的 CS 技术与 JPEG 匹配甚至略有优势。
