Significance-linked connected component analysis for high performance low bit rate wavelet coding

Recent success in wavelet image coding is mainly attributed to the recognition of importance of data organization and representation, There have been several very competitive wavelet coders developed, namely, embedded zerotree wavelets (EZW), morphological representation of wavelet data (MRWD), and set partitioning in hierarchical 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 fields. Computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT as well. For example, for the “Barbara” image, at 0.50 bpp SLCCA outperforms EZW and SPIHT by 1.71 dB and 0.85 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. This outstanding performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast