B-splines provides tools widely used in geometric modeling. Since nested knot vectors gives rise to nested spline spaces, B-splines are well suited to be used in a muiltiresolution setting. One of the reasons why B-splines have become so popular is that by normalizing each B-spline properly, the stability constants with respect to any p norm can be estimated independent of the knot vector. An interesting question is therefore if B-wavelets has a similar property, and if so how they should be normalized.

Lossy image compression with non-uniform distribution of image quality between different regions of an image has been subject to recent research. Wavelet based compression is ideally for this, because of the local support of the wavelet basis functions. If semiorthogonal wavelets subject to the L₂ inner product are used, each decomposition step makes a least squares approximation of the input. By using a weighted inner product, the approximation will be better in the regions where the weight is higher.