Locally linear embedding based texture synthesis for image prediction and error concealment
The template matching algorithm is a simple extension to exemplar-based texture synthesis. Average of template matching predictors or non-local means based approaches can be seen as heuristic extensions to template matching. These methods which linearly combine several texture patches have been shown to be more robust in synthesis and to give better results when compared to simple template matching. However, they do not search to minimize an approximation error on the known pixel values in the template. They are rather heuristic methods for calculating the linear weighting coefficients. This paper proposes a neighbor embedding based texture synthesis method by formulating the problem as a least-squares optimization using locally linear embedding. By this means, one calculates the linear weighting coefficients by solving a constrained optimization for approximating the template. The proposed texture synthesis framework has first been applied to the image prediction (predictive coding) problem. It has then been extended to a loss concealment application for transmission errors. Experimental results demonstrate the effectiveness of the proposed method for both image compression and error concealment.