Phase refinement for image prediction based on sparse representation

In this work, we propose the use of sparse signal representation techniques to solve the problem of closed-loop spatial image prediction. The reconstruction of signal in the block to predict is based on basis functions selected with the Matching Pursuit (MP) iterative algorithm, to best match a causal neighborhood. We evaluate this new method in terms of PSNR and bitrate in a H.264 / AVC encoder. Experimental results indicate an improvement of rate-distortion performance. In this paper, we also present results concerning the use of phase correlation to improve the reconstruction trough shifted-basis functions.