  An efficient scalable data representation is an important task especially in
the medical area, e.g. for volumes from Computed Tomography (CT) or Magnetic
Resonance Tomography (MRT), when a downscaled version of the original signal is
needed. Image and video coders based on wavelet transforms provide an adequate
way to naturally achieve scalability. This paper presents a new approach for
improving the visual quality of the lowpass band by using a novel graph-based
method for motion compensation, which is an important step considering data
compression. We compare different kinds of neighborhoods for graph construction
and demonstrate that a higher amount of referenced nodes increases the quality
of the lowpass band while the mean energy of the highpass band decreases. We
show that for cardiac CT data the proposed method outperforms a traditional
mesh-based approach of motion compensation by approximately 11 dB in terms of
PSNR of the lowpass band. Also the mean energy of the highpass band decreases
by around 30%.
