  Lossless compression of dynamic 2D+t and 3D+t medical data is challenging
regarding the huge amount of data, the characteristics of the inherent noise,
and the high bit depth. Beyond that, a scalable representation is often
required in telemedicine applications. Motion Compensated Temporal Filtering
works well for lossless compression of medical volume data and additionally
provides temporal, spatial, and quality scalability features. To achieve a high
quality lowpass subband, which shall be used as a downscaled representative of
the original data, graph-based motion compensation was recently introduced to
this framework. However, encoding the motion information, which is stored in
adjacency matrices, is not well investigated so far. This work focuses on
coding these adjacency matrices to make the graph-based motion compensation
feasible for data compression. We propose a novel coding scheme based on
constructing so-called motion maps. This allows for the first time to compare
the performance of graph-based motion compensation to traditional block- and
mesh-based approaches. For high quality lowpass subbands our method is able to
outperform the block- and mesh-based approaches by increasing the visual
quality in terms of PSNR by 0.53dB and 0.28dB for CT data, as well as 1.04dB
and 1.90dB for MR data, respectively, while the bit rate is reduced at the same
time.
