On exploiting geometric constraint of image wavelet coefficients

We investigate the problem of how to exploit the geometric constraints 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. Experimental results show that the proposed phase shifting and prediction scheme improves both the subjective and objective performance of wavelet-based image coders.