Image Prediction: Template Matching vs. Sparse Approximation
Résumé
The paper compares a sparse approximation based spatial texture prediction method with the template matching based prediction. Template matching algorithms have been widely considered for image prediction. These approaches rely on the assumption that the predicted texture contains a similar textural structure with the template in the sense of a simple distance metric between template and candidate. However, in real images, there are more complex textured areas where template matching fails. The basic idea instead is to consider sparse approximation algorithms. The proposed sparse spatial prediction is assessed against the prediction method based on template matching with a static and optimized dynamic templates. The spatial prediction method is then assessed in a coding scheme where the prediction residue is encoded with a coding approach similar to JPEG. Experimental observations show that the proposed method outperforms the conventional template matching based prediction.