PREDICTIVE 3D SEARCH ALGORITHM
FOR MULTI-FRAME MOTION
ESTIMATION
Lim Hong Yin, Ashraf A. Kassim, Peter H.N de With
IEEE Transaction on Consumer Electronics,2008
Outline





Overview of FME Algorithm
Multi-Directional Hexagon
Proposed 3D Search Scheme
High-motion & Low-motion
Simulation Results & Conclusion
Overview of FME Algorithm


In FME, the block-matching cost function increases monotonically
as the search moves away from the position of the best
match/global minimum
There is also a risk of the search getting trapped in a local
minima  the same rate-distortion performance as the full
search (FS) cannot be achieved
Multi-Directional Hexagon

Next best position in relation to that of the current
best position
All points within a range of ± 2 from T1 are then
evaluated and the direction of the best point from T1
(Next Best Position) is recorded.

Multi-Directional Hexagon pattern
Multi-Directional Hexagon

Fixed Hexagon pattern
3
Step1.
Initial best point obtain
using LDP(Large Diamond
Pattern)
2
Step2.
use fixed hexagon pattern
1
Step3.
In the final refinement serach,
the Small hexagon/diamond is
used
3
4
3 4
2
4
4
2
1
1
1
2
1
1
2
1
1
1
Multi-Directional Hexagon

Multi-Directional Hexagon pattern
3
3
4
3
4
2
4
Initial best point obtain using
LDP(Large Diamond Pattern)
Step2.
use multi-directional hexagon
pattern
Step3.
In the final refinement serach,
the Small hexagon/diamond is
used
2
1
4
1
2
Step1.
1
1
1
1
1
1
1
Results in lower number of search points !!
3D Search Pattern Scheme

In a 3D search, the search can proceed directly to points at
nearby frames and there is no necessity to perform a new
search from the window center for each reference frame
 the computational time can be reduced!
Proposed 3D Search Scheme


In a video sequence, a moving object is likely to
keep a similar appearance within the adjacent
frames.
The proposed 3D search pattern aims to perform
the MV search by searching along the trajectory of
object.
Proposed 3D Search Scheme

3D Multi-Directional Hexagon Search patterns(3D MD-HEXS)
Search Center
Flatted Hexagon
Small Hexagon
Proposed 3D Search Scheme

3D Diamond Search patterns
Proposed 3D Search Scheme
3D-LDP is used for the initial search

Step.1
Step.2

Step.3
3D-SDP is used to find best MV

The position with the minimum cost function, Jmin in
the 3D-LDP is then used to select one of the four
directional hexagon search patterns
(repeated until Jmin lies in the 3D search center)
Multi-Hexagon-Grid Search For HighMotion


Most center-biased search algorithms such as DS and HEXBS
perform poorly when used on high-motion sequences and when
the search range is large
To overcome this problem, we propose the use of a large search
pattern, specifically the Multi-Hexagon-Grid Search pattern for
high-motion activity blocks.
Multi-Hexagon-Grid Search For HighMotion

Use minimum cost functions (Jmin) to determine the motion
activity  High-motion activity can be inferred when the Jmin
obtained from the 3D search is significantly higher than the
Jmin of adjacent blocks

Base on TABLE III , we use a threshold
for determine the motion activity :
TH    MIN ( J1 , J 2 , J 3 , J 4 )
J1
J2
J4
J min
J3
M 2  MIN ( J1 , J 2 , J 3 , J 4 )
If the Jmin obtained from the 3D
search is more than TH , we use the
Multi-Hexagon-Grid Search pattern
to perform a large motion search
Simplifying Search for Low-Motion
Blocks

The table shows the percentage of optimal MV
obtained using FS, which is ±1 from the origin(%MV)
If the spatially adjacent MVs is within ±1
from the origin, then the current block is
determined to be of low-motion activity.
Low motion

High motion
When the block is determined to be low-motion through
the proposed criterion, the 3D search strategy is
simplified to using only the 3D Small Diamond Search
Simulation Results
low

RD Optimization, CABAC encoding

Search Range :±16 & ±32 for QCIF & CIF

Referenced frame :5
high
Simulation Results
Conclusion



The 3D MV predictors are used to obtain a more accurate
search center while the 3D search patterns are designed to
track the motion trajectories along the reference frames. This
reduces the computational complexity while maintaining the
search accuracy.
Implement a large motion search method utilizing the MultiHexagon Grid search pattern and saves on the search points
for low-motion blocks by simplifying the 3D search
On average, the PSNR loss for our algorithm is less than 0.2 dB
while a savings of more than 96% in computational time is
achieved compared to FS.