Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/60454
Title: Regularity scalable image coding based on wavelet singularity detection
Authors: Ho, CYF
Hsung, TC
Lun, DPK 
Ling, BWK
Tam, PKS
Siu, WC 
Keywords: Feature-based scalable coding
Regularity
Separable wavelet transform
Issue Date: 2008
Publisher: World Scientific
Source: International journal of image and graphics, 2008, v. 8, no. 1, p. 109-134 How to cite?
Journal: International journal of image and graphics 
Abstract: In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficiency.
URI: http://hdl.handle.net/10397/60454
ISSN: 0219-4678
EISSN: 1793-6756
DOI: 10.1142/S0219467808003003
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