Multiscale Motion Estimation for Scalable Video Coding

by Ravi Krishnamurthy, Pierre Moulin and John W. Woods

Motion estimation is an important component of video coding systems 
because it enables us to exploit the temporal redundancy in the
sequence. The popular block-matching algorithms (BMAs) produce
unnatural, piecewise constant motion fields that do not
correspond to ``true'' motion. In contrast, our focus here is
on high-quality motion estimates that produce a 
video representation that is less dependent on the specific frame-rate
or resolution. To this end, we present an {\em iterated registration}
algorithm that extends previous work on multiscale motion models and
gradient-based  estimation for coding applications.
We obtain improved motion estimates and higher overall coding performance.
Promising applications are found in temporally-scalable video coding with
motion-compensated frame interpolation at the decoder.
We obtain excellent interpolation performance and video quality;
in contrast, BMA leads to annoying artifacts near moving image edges.