Compressive-Sensed Image Coding via Multi-layer Closed-Loop Prediction

Abstract

These years have seen the advance of compressive sensing (CS), but the CS-based image coding scheme still has a poor rate-distortion (R-D) performance compared with the traditional image coding techniques. In this paper, we propose an image coding scheme based on the CS paradigm via multi-layer closed-loop prediction. In the scheme, we divide CS measurements into multi-layers and predict a particular layer's measurements with all its preceding layers' measurements, which can reduce the redundancies between CS measurements efficiently. The produced measurement residuals are then quantized into binary codes, which are tremendously reduced compared to quantizing the CS measurements directly. Furthermore, We provide a non-local low-rank CS reconstruction algorithm corresponding to our multi-layer closed-loop prediction scheme. Experimental results verify that the proposed scheme can significantly outperform JPEG2000, and the reconstruction quality of our scheme is no worse or even better than that of HEVC-Intra.