Image Representation Using Block Pattern Models and Its Image Processing Applications
An image representation scheme using a set of block pattern models (BPMs) consisting of three categories (constant, oriented, and irregular) is introduced. Algorithms for model classification, model parameter estimation, and image reconstruction from model parameters are presented, and these provide the necessary vehicles for applying the proposed representation scheme to various image processing tasks. The applications of the proposed models in image coding, image zooming, and image smoothing are described. Satisfactory coded images have been obtained at bit rates between 0.5 approximately 0.6 b.p.p. (bits per pixel) with a high-rate realization and between 0.3 approximately 0.5 b.p.p. with a low-rate realization. The high-rate realization has a simple structure suitable for real-time implementation. The methods for image zooming and smoothing are similar, where both adapt the processing for each pixel according to the model of its neighborhood. By using directional filters in oriented regions, edges and lines are rendered sharper in a smoother manner than with conventional linear filtering approaches, which leads to significant improvement in perceived image quality. >