Signi cance-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 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 cancelinked 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 0.25 bpp SLCCA outperforms EZW, MRWD and SPIHT by 1.41 dB, 0.32 dB and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256 256 grayscale texture images compressed at 0.40 bpp, SLCCA outperforms SPIHT by 0.16 dB{0.63 dB in PSNR. This outstanding performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast. Submitted: IEEE Trans. on Image Processing EDICS category: IP 1.1 (Coding) Correspondence author: Dr. Xinhua Zhuang Address: 313 Engineering Building West Department of Computer Engineering & Computer Science University of Missouri-Columbia Columbia, MO 65211 Phone: (573) 882-2382 Fax: (573) 882-8318 E-mail: [email protected]