Multihypothesis motion-compensating predictors combine several
motion-compensated signals to predict the current frame signal.
More than one motion-compensated signal, or hypothesis, is
selected for transmission.
Long-term memory motion-compensated prediction is a further concept
for efficient video compression and is an example for
forward-adaptive hypothesis switching.
One motion-compensated signal is selected from multiple reference
frames for transmission.
This paper extends the theory of multihypothesis motion-compensated
prediction to forward-adaptive hypothesis switching.
Assume, that we combine N hypotheses.
Each hypothesis that is used for the combination is selected from
a set of motion-compensated signals of size M.
We study the influence of the hypothesis set size M on both
the accuracy of motion compensation of forward-adaptive hypothesis
switching and the efficiency of multihypothesis motion-compensated
prediction.
In both cases, we examine the noise-free limiting case.
That is, we neglect signal components that are not predictable
by motion compensation.
Selecting one hypothesis from a set of motion-compensated signals
of size M, that is, switching among M hypotheses, will reduce
the displacement error variance by factor M when we assume
statistically independent displacement errors.
Integrating forward-adaptive hypothesis switching into
multihypothesis motion-compensated prediction, that is, allowing
a combination of switched hypotheses,
increases the gain of multihypothesis motion-compensated
prediction over the single hypothesis case for growing hypothesis
set size M.
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