Bi-directional intra prediction based measurement coding for compressive sensing images

This work proposes a bi-directional intra prediction-based measurement coding algorithm for compressive sensing images. Compressive sensing is capable of reducing the size of the sparse signals, in which the high-dimensional signals are represented by the under-determined linear measurements. In order to explore the spatial redundancy in measurements, the corresponding pixel domain information extracted using the structure of measurement matrix. Firstly, the mono-directional prediction modes (i.e. horizontal mode and vertical mode), which refer to the nearest information of neighboring pixel blocks, are obtained by the structure of the measurement matrix. Secondly, we design bi-directional intra prediction modes (i.e. Diagonal + Horizontal, Diagonal + Vertical) base on the already obtained mono-directional prediction modes. Experimental results show that this work improves 0.01 - 0.02 dB PSNR improvement and the birate reductions of on average 19%, up to 36% compared to the state-of-the-art.