Universal and low-complexity quantizer design for compressive sensing image coding

Compressive sensing imaging (CSI) is a new framework for image coding, which enables acquiring and compressing a scene simultaneously. The CS encoder shifts the bulk of the system complexity to the decoder efficiently. Ideally, implementation of CSI provides lossless compression in image coding. In this paper, we consider the lossy compression of the CS measurements in CSI system. We design a universal quantizer for the CS measurements of any input image. The proposed method firstly establishes a universal probability model for the CS measurements in advance, without knowing any information of the input image. Then a fast quantizer is designed based on this established model. Simulation result demonstrates that the proposed method has nearly optimal rate-distortion (R~D) performance, meanwhile, maintains a very low computational complexity at the CS encoder.