Sparse approximation with adaptive dictionary for image prediction

The paper presents a dictionary construction method for spatial texture prediction based on sparse approximations. Sparse approximations have been recently considered for image prediction using static dictionaries such as a DCT or DFT dictionary. These approaches rely on the assumption that the texture is periodic, hence the use of a static dictionary formed by pre-defined waveforms. However, in real images, there are more complex and non-periodic textures. The main idea underlying the proposed spatial prediction technique is instead to consider a locally adaptive dictionary, A, formed by atoms derived from texture patches present in a causal neighborhood of the block to be predicted. The sparse spatial prediction method is assessed against the sparse prediction method based on a static DCT dictionary. The spatial prediction method is then assessed in a complete image coding scheme where the prediction residue is encoded using a coding approach similar to JPEG.