Image Compression Based On Near Lossless Predictive Measurement Coding for Block Based Compressive Sensing

Abstract

Smart devices for image/video sensing are needed to work within the constraints of limited bandwidth and low computing capabilities. In this context, Block based Compressive Sensing (BCS) emerged as the most viable method for balancing image/video quality and transmission bandwidth computing overheads. However, in comparison with conventional image and video acquisition systems, BCS cannot reduce the bitrate due to its straightforward nature of system of linear equations, which still incurs high transmission and storage overhead. To address this shortcoming, in this paper we propose a novel Near Lossless Predictive Coding (NLPC) approach to compress BCS measurements. The NLPC method encodes the prediction error measurement between the target and current measurement, resulting in lower data size. We designed and implemented a complete BCS integrated with NLPC with scalar quantization (BCS-NLPC-SQ) and studied the image quality at different compression ratios with varying block sizes. The BCS-NLPC-SQ method can improve roughly on an average PSNR of +3.06 dB and the average SSIM gain of +0.11 with respect to the existing works. The synthesis results shows that, BCS-NLPC-SQ requires 83.01%, 69.03%, 53.26%, and 14.45% less area, power, ADP and PDP over JPEG compression and we have achieved an additional compression of up to 56.25% in the best case. Our proposed BCS-NLPC-SQ method outperformed the existing methods in terms of PSNR, SSIM, and bpp. IEEE