Progressive Content-Based Shape Compression for Retrieval of Binary Images

This paper deals with content-based compression of binary-shape images. The proposed method is based on a polygonal approximation of the shape contours. A well-known approximation algorithm, from computer vision applications such as shape analysis and boundary pattern matching, is adapted to achieve a progressive representation. The resulting various levels of shape quality are encoded, from a coarse representation for fast browsing up to a lossless representation for final rendering. In order to perform efficient compression of the progressive shape information, discrete geometrical constraints inherent to the image grid quantization are exploited. While the proposed scheme offers a content-based description (shape boundary as opposed to bitmap blocks) together with a quality scalable representation, it remains comparable, in terms of compression efficiency, with state of the art shape coding methods that do not combine such functionalities.