Temporal filtering for restoration of wavelet-compressed motion imagery

Temporal filtering of motion imagery can alleviate the effects of noise and artifacts in the data by incorporating observations of the imagery data from several distinct frames. If the noise that is expected to occur in the data is well-modeled by independent and identically distributed (IID) Gaussian noise, then straightforward algorithms can be designed that filter along motion trajectories in an optimal fashion. This paper addresses the restoration of motion imagery that have been compressed by scalar quantization of the data's discrete wavelet transform coefficients. Noise due to compression in such situations is neither independent nor identically distributed, and thus straightforward filters designed for the IID case are suboptimal. This paper provides a statistical characterization of the quantization error and shows how the improved noise modeling can be used in temporal filtering to improve visual quality of the decompressed motion imagery. Example restoration results include the cases where the data have been compressed by quantization of two- and three-dimensional wavelet transform coefficients. Although not developed in this work, the noise model is also directly applicable to other restoration algorithms that incorporate information from other time instants, such as super-resolution. Published by Elsevier B.V.