Image prediction based on non-negative matrix factorization
This paper presents a novel spatial texture prediction method based on non-negative matrix factorization. As an extension of template matching, approximation based iterative texture prediction methods have recently been considered for image prediction. These approaches rely on the assumption that the given basis functions (atoms) span the signal residue space at each iteration of the algorithm. However, in the case of signal prediction with a support region approximation, the atoms may not approximate residue signals very well even though the dictionary has been well adapted in the spatial domain. The underlying main idea is to consider a factorization based algorithm in which the given atoms approximate the signal without going further into signal residue space. The proposed spatial prediction method has first been assessed against the prediction methods based on template matching and sparse approximations. It has then been assessed in a compression scheme where the prediction residue is transform encoded. Experimental results obtained show that the proposed method outperforms the template matching and sparse approximations based techniques in terms of encoding efficiency.