Significance-Link Connected Component Analysis for Low Bit Rate Image Coding

Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. There have been several very competitive wavelet coders 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 develop a novel wavelet image coder called signi cance-linked connected component analysis (SLCCA) of wavelet coe cients that extends MRWD by exploiting both within-subband clustering of signi cant coe cients and cross-subband dependency in signi cant elds. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the \Barbara" image, at 0.5 bpp SLCCA outperforms EZW and SPIHT by 1.75 dB and 0.89 dB in PSNR, respectively. This outstanding performance is achieved without using any optimal bit allocation procedure, thus both the encoding and decoding procedures are fast.