Significance-linked connected component analysis for wavelet 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 embedd ed zerotree wavelets (EZW), Servetto et al.'s morphological representation of wavelet data (MRWD), and Said and Pearlman's set partitioning in hierarc hical trees (SPIHT), In this paper, we develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fi elds. Extensive computer experiments on both natural and texture images sho w convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. Fo r example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, M RWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It i s also observed that SLCCA works extremely well for images with a large por tion of texture. For eight typical 256 x 256 grayscale texture images compr essed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR, This outstanding performance is achieved without using any optimal bit allo cation procedure, Thus both the encoding and decoding procedures are fast.