<title>Image partition boundary coding</title>

Abstract

This paper introduces two image partition boundary coding models that are composed solely of binary decisions. Because of their simplified decision structure, the models can take advantage of various accelerating schemes for binary arithmetic coding. The number of decisions necessary to describe a partition using either model varies between one and two per pixel location and is proportional to partition complexity. The first model is a binary decomposition of Steve Tate's neighboring edge model. The decomposition employs boundary connectivity constraints to reduce the number of model parameters. The constraints also reduce the number of descriptive decisions to just over one per pixel for typical partitions. A theoretical zero order entropy bound of 1.6 bits per pixel also results. The second model represents a partition as a sequence of strokes. A stroke consists of one or two three-way chains. Chain termination is accomplished without redundant boundary traversal by using a special termination decision at encounters with previously drawn chains. Chain initiation decisions are also conditioned on previously drawn edge patterns. Chain direction decisions are conditioned via a boundary state machine. The paper compares object based boundary coding and pixel based coding, placing the new coders into the latter category. A technique for determining the appropriate application domain of pixel based codes is developed. The new coding models are placed into context with previous pixel based work by the development a new categorization of image partition representations. Four representations are defined, the map coloring, the edge map, the outline map, and the perimeter map. Experiments compare the new methods with other pixel based methods and with a canonical object based method.