Sparse representations for spatial prediction and texture refinement
In this work, we propose a novel approach for signal prediction based on the use of sparse signal representations and Matching Pursuit (MP) techniques. The paper first focuses on spatial texture prediction in a conventional block-based hybrid coding scheme and secondly addresses inter-layer prediction in a scalable video coding (SVC) framework. For spatial prediction the signal reconstruction of the block to predict is based on basis functions selected with the MP iterative algorithm, to best match a causal neighborhood. Inter-layer MP based prediction employs base layer upsampled components additionally to the causal neighborhood in order to improve the representation of high frequencies. New solutions are proposed for efficiently deriving and exploiting the atoms dictionary through phase refinement and mono-dimensional basis functions. Experimental results indicate noticeable improvement of rate/distortion performance compared to the standard prediction methods as specified in H.264/AVC and its extension SVC.
sparse representations spatial prediction phase refinement h.264/avc extrapolation video compression scalable coding redundant dictionaries