Prediction-based incremental refinement for binomially-factorized discrete wavelet transforms
IEEE Transactions on Signal Processing
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Two basic approaches for implementing the 9/7 filtering unit, used in the discrete wavelet transform, are addressed. The first is the lifting scheme approach and the second is the conventional, convolutional filter approach. Two architectures are examined for each approach, a simple straightforward one and an optimized one, substituting the multipliers used for scaling with shift-add operations. The quantization of the constants used in the calculations is thoroughly explored and the selection of the data-path bit-width is addressed. Experimental results based on hardware implementation, for several quantizations and for the different hardware architectures of the 9/7 filtering units are given.