Image Representation Using Block Pattern Models and Its Image Processing Applications

An image representation scheme using a set of block pattern models (BPM‘s) is introduced. These models consist of three categories (constant, oriented, and irregular), with the last one consisting of two subgroups: textured and mixed. They are constructed to represent three basic types of image patterns: shade, edge, and texture. Image representation using these models requires considerably fewer bits than the original pixel-wise description and yet characterizes perceptually significant features more effectively. In particular, the parameterization of the oriented model using oriented basis functions lays a mathematical foundation for designing directional operators that are desirable in many image processing applications. 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. The coding system approximates each image block by a BPM and further quantizes the model parameters. Satisfactory coded images have been obtained at bit rates between 0.5 ~ 0.6 bpp (bits per pixel) with a high-rate realization and between 0.3 ~ 0.5 bpp 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.