On exploiting geometric constraint of image wavelet coefficients

In this paper, we investigate the problem of how to exploit geometric constraint of edges in wavelet-based image coding. The value of studying this problem is the potential coding gain brought by improved probabilistic models of wavelet high-band coefficients. Novel phase shifting and prediction algorithms are derived in the wavelet space. It is demonstrated that after resolving the phase uncertainty, high-band wavelet coefficients can be better modeled by biased-mean probability models rather than the existing zero-mean ones. In lossy coding, the coding gain brought by the biased-mean model is quantitatively analyzed within the conventional DPCM coding framework. Experiment results have shown the proposed phase shifting and prediction scheme improves both subjective and objective performance of wavelet-based image coders.