cient codec based on signi cance - linked connectedcomponent analysis of wavelet coe

Recent success in wavelet coding (or interchangeable, linear subband coding) is mainly attributed to the recognition of importance of data organization. There have been several very competitive wavelet codecs developed, namely, Shapiro's Embedded Zerotree Wavelets (EZW), Servetto et al.'s Morphological Representation of Wavelet Data (MRWD), and Said and Pearlman's Set Partitioning in Hierarchical Trees (SPIHT). In this paper, we propose a new image compression algorithm called Signiicance-Linked Connected Component Analysis (SLCCA) of wavelet coeecients. SLCCA exploits both within-subband clustering of signiicant coeecients and cross-subband dependency in signiicant elds. A so-called signiicance link between connected components is designed to reduce the positional overhead of MRWD. In addition, the signiicant coeecients' magnitude are encoded in bit plane order to match the probability model of the adaptive arithmetic coder. Experiments show that SLCCA outperforms both EZW and MRWD, and is tied with SPIHT. Furthermore, it is observed that SLCCA generally has the best performance on images with large portion of texture. When applied to ngerprint image compression, it outperforms FBI's wavelet scalar quantization by about 1 dB.