Implementation driven selection of wavelet filters for still image coding based on bitrange expansion

The wavelet transform is a popular filter-based decomposition algorithm used in novel multimedia standards and applications. Due to its intrinsic use of filtering operations, the choice of an appropriate filter and the normalisation of its coefficients determines the number of bits needed to represent the transformed image. We show that depending on the normalisation of the filters, the wavelet coefficients either saturate or grow exponentially for successive transformation levels. A procedure to create images that excite worst case maxima in the transform domain is described. The bit-range expansion for natural images is computed and follows the trend of the worst case results