A Novel Data Representation Strategy for Wavelet Image Compression

Recent success in wavelet image coding is mainly attributed to recognition of the importance of data organization and representation. Several very competitive wavelet coders have been 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 exploits both within-subband clustering of signi cant coe cients and cross-subband dependency in signi cant elds. Extensive computer experiments show that the proposed SLCCA outperforms all three aforementioned wavelet coders. For example, for the \Barbara" image, at 0.50 bpp SLCCA outperforms EZW and SPIHT by 1.75 dB and 0.89 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with large texture regions. For eight typical 256 256 grayscale texture images compressed at 0.40 bpp, SLCCA outperforms SPIHT by 0.32 dB{ 0.70 dB. This outstanding performance is achieved without any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.