Variable block size segmentation for image compression using stochastic models
Chee Sun Won · 2002
In this paper, a new variable size block segmentation for image compression is proposed. The decision whether the given image block is homogeneous or not is based on the model selection criterion. More specifically, calculating the log-likelihoods for all pre-determined region segmentations with the given image data, we apply a modified AIC criterion to select a best match. If the selected pattern turns out to be a texture or an edge, we further divide the given image block to yield a variable size block segmentation. Since the proposed algorithm takes into account the contextual information as well as the block variance for the classification, it can differentiate a texture from an edge. Moreover, due to the pre-determined block segmentations, we can further differentiate vertical, horizontal, or diagonal edges.