Multiwavelet is a new kind of wavelets and application of multiwavelet to signal processing is also a new practice these years. Perhaps this is why we hardly find the statistical data such as mean

variance and the proportion of zero valued quantized coefficients of multiwavelet transforms in literatures. In this paper

we collect five multiwavelets from literatures and Internet and make a statistical analysis of their performance in multiwavelet transform. From our analysis we can conclude that (1) After CL multiwavelet transform

the energy of an image will converge not only to the lowest resolution subimage

but further to the first component of the subimage. Therefore

CL multiwavelet is most qualified for image coding. (2) After CARDBAL multiwavelet transform

the energy of the lowest resolution subimage of an image will spread in average among its four components. Therefore

CARDBAL multiwavelet image coding has to appeal to correlative coding between the components of subimage to improve its compression ratio. (3) After GHM multiwavelet transform

the distribution of the lowest resolution subimage's energy is not concentrated on one of its components

not averaged among its components. Therefore

GHM multiwavelet is not particularly suitable to image coding

even though it is the first multiwavelet to be discovered and now widely used in applications.