Neighborhood coding for bilevel image compression and shape recognition

Neighborhood coding was proposed to encode binary images. Previously, this coding scheme presented good results in the problem of handwritten character recognition. In this article, we extended this coding scheme so that it can be applied as an image shape descriptor and in a bilevel image compression method. An algorithm to reduce the number of codes needed to reconstruct the image without loss of information is presented. Using the exactly same set of reduced codes, a lossless compression method and a shape recognition system are proposed. The reduced codes are used with Huffman coding and RLE (Run-Length Encoding) to obtain a compression rate comparable to well-known image compression algorithms such as LZW and CCITT Group 4. For the shape recognition task we applied a template matching algorithm to the set of strings generated by the coding reduction procedure. We tested this method in the MPEG-7 Core Experiment Shape 1 part A2 and the binary image compression challenge database.