Highly Eecient Codec Based on Signiicance-linked Connected Component a N a L Y S I S O F W Avelet Coeecients

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 Signi cance-Linked Connected Component Analysis (SLCCA) of wavelet coe cients. SLCCA exploits both within-subband clustering of signi cant coe cients and cross-subband dependency in signi cant elds. A so-called signi cance link between connected components is designed to reduce the positional overhead of MRWD. In addition, the signi cant coe cients' 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.