Compressed Sensing by Using Measurement Completion for Robust Image Coding

Compressed sensing (CS) has been demonstrated to be an effective method for robust image coding. However, for existing CS-based image coding schemes, recovery performance drops rapidly at high packet loss rates (PLRs) because the received CS measurements are insufficient for stable recovery. To solve this problem, we propose a novel robust image coding scheme by using CS with measurement completion in this paper. By dividing the original image into a lot of down-sampled images with interweaving permutation (IP) and then sampling them with scrambled 2D CS (2DCS), we can obtain the CS measurement vectors of the down-sampled images. Since the CS measurement vectors preserve the correlation of the down-sampled images, they are also highly-correlated with each other. By exploring the correlation among the CS measurement vectors, a measurement completion strategy is proposed, which can recover many lost CS measurements due to packet loss at the decoder side. Simulation results show that the proposed scheme can significantly outperform previous CS-based image coding schemes at high PLRs in terms of rate distortion (R-D) performance. This advantage makes the proposed scheme a good candidate for those image communication systems which need to provide reliable transmission for the image data via channels with high PLRs.