Adaptive predictor based on maximally flat halfband filter in lifting scheme
IEEE Transactions on Signal Processing
For the complex short time-varying signals, a highorder predictor does not always yield good performance. For this, we investigate the use of a short-order adaptive predictor. Since the maximally flat filters are the optimal predictors for polynomial signal prediction, the adaptation is based on the combination of a set of maximally flat filters. For compression efficiency, the dynamic ranges of the weighting variables are specially considered. For this, based on the Bernstein filters, another
... n filters, another form to represent the weighting variables is used. These two sets of weighting coefficients can be transformed into each other with a simple linear transform. Thus, the adaptation can be made in both the time domain and the frequency domain. For block-based image coding, the least square criterion is used to derive the weighting coefficients. Experimental results show that the adaptive predictor performs better than the S+P transform, the median edge detector (MED), and the gradient adjusted predictor (GAP). Index Terms-Bernstein polynomial, filter bank, lifting scheme, maximally flat filter.