Image compression based on transform coding appears to be approaching an asymptotic bit rate limit for application-specific distortion levels. However, a new compression technology, called object-based compression (OBC) promises improved rate-distortion performance at higher compression ratios. OBC involves segmentation of image regions, followed by efficient encoding of each region’s content and boundary. Advantages of OBC include efficient representation of commonly occurring textures and shapes in terms of pointers into a compact codebook of region contents and boundary primitives. This facilitates fast decompression via substitution, at the cost of codebook search in the compression step.
Segmentation cose and error are significant disadvantages in current OBC implementations. Several innovative techniques have been developed for region segmentation, including (a) moment-based analysis, (b) texture representation in terms of a syntactic grammar, and (c) transform coding approaches such as wavelet based compression used in MPEG-7 or JPEG-2000. Region-based characterization with variance templates is better understood, but lacks the locality of wavelet representations. In practice, tradeoffs are made between representational fidelity, computational cost, and storage requirement. This paper overviews current techniques for automatic region segmentation and representation, especially those that employ wavelet classification and region growing techniques. Implementational discussion focuses on complexity measures and performance metrics such as segmentation error and computational cost.