Design of Low-Complexity Quantized Compressive Sensing Using Measurement Predictive Coding

Abstract

Block-based compressive sensing (BCS) has evolved as a promising method for smart devices with limited bandwidth and computing capabilities, striking a balance between image/video quality and transmission efficiency. Despite its advantages, BCS falls short in reducing bitrate compared with traditional acquisition systems, because it increases the number of bits per measurement, which leads to high storage and transmission costs. In this context, we propose a measurement predictive coding (MPC) along with the quantization method in integration with BCS named BCS-MPC

here, we have performed the quantization with bit shifts only instead of binary division. The proposed method reduces the number of bits per compressive sensing (CS) measurement as well as the transmission of the quantization step size. Furthermore, it reduces the latency and hardware resources. The proposed method improved on average +3.44 to +8.28 dB in PSNR over the current works. From the synthesis results, the proposed BCS-MPC method requires 26.11%, 18.89%, and 82.53% less area, power, and delay over the existing work. We have achieved a reduction in delay with bit-shift operations. © 1993-2012 IEEE.