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the development of HEVC, the initial AMVP design was significantly simplified
to provide a good trade-off between coding efficiency and an implementation
friendly design. Section
5.2.1.1
describes in detail how the list of potential MVPs
is constructed in HEVC. Section
5.2.1.2
describes the signaling of all motion data,
including the index to the AMVP list, when AMVP is used for MV coding.
5.2.1.1
AMVP Candidate List Construction
The initial design of AMVP included five MVPs from three different classes
of predictors: three motion vectors from spatial neighbors, the median of the
three spatial predictors and a scaled motion vector from a co-located, temporally
neighboring block. Furthermore, the list of predictors was modified by reordering
to place the most probable motion predictor in the first position and by removing
redundant candidates to assure minimal signaling overhead. Exhaustive experiments
throughout the standardization process investigated how the complexity of this
motion vector prediction and signaling scheme could be reduced without sacrificing
too much coding efficiency [
7
,
8
,
14
]. This led to significant simplifications of the
AMVP design such as removing the median predictor, reducing the number of
candidates in the list from five to two, fixing the candidate order in the list and
reducing the number of redundancy checks. The final design of the AMVP candidate
list construction includes the following two MVP candidates:
uptotwo
spatial candidate
MVPs that are derived from five spatial neighboring
blocks
one
temporal candidate
MVPs derived from two temporal, co-located blocks
when both spatial candidate MVPs are not available or they are identical
zero motion vectors when the spatial, the temporal or both candidates are not
available
Spatial Candidates
As already mentioned, two spatial MVP candidates A and B are derived from five
spatially neighboring blocks which are shown in Fig.
5.4
b. The locations of the
spatial candidate blocks are the same for both AMVP and inter-prediction block
merging that will be presented in Sect.
5.2.2
.
The derivation process flow for the two spatial candidates A and B is depicted
in Fig.
5.5
. For candidate A, motion data from the two blocks A0 and A1 at the
bottom left corner is taken into account in a two pass approach. In the first pass,
it is checked whether any of the candidate blocks contain a reference index that is
equal to the reference index of the current block. The first motion vector found will
be taken as candidate A. When all reference indices from A0 and A1 are pointing
to a different reference picture than the reference index of the current block, the
associated motion vector cannot be used as is. Therefore, in a second pass, the
