Quantized Compressive Sensing Measurement Based on Improved Subspace Pursuit Algorithm

Recent research results in compressive sensing have shown that sparse signals can be recovered from a small number of random measurements. Whether quantized compressive measurements can provide an efficient representation of sparse signals in information-theoretic needs discuss. In this paper, the distortion rate functions are used as a tool to research the quantizing compressive sensing measurements bring about average distortion rate. Both uniform quantization and non-uniform quantization were considered, for quantized measurements, the improved subspace pursuit was adapted to accommodate quantization error based on the concept of consistency, and experimental results show that the improved algorithm significantly reduces the reconstruction distortion when compared to standard compressive sensing techniques. Key-Words: Compressive Sensing; Rate Distortion Function; Subspace Pursuit