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Flexible Fast Block Matching Algorithm Design based on Complexity-Distortion Optimization. Pol Lin Tai, Chii Tung Liu, Shih Yu Huang * , Jia Shung Wang Department of Computer Science National Tsing Hua University, Taiwan, R.O.C. *Department of Information Management - PowerPoint PPT Presentation
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Flexible Fast Block Matching Flexible Fast Block Matching Algorithm Design based on Algorithm Design based on
Complexity-Distortion Complexity-Distortion OptimizationOptimization
Pol Lin Tai, Chii Tung Liu, Shih Yu Huang*, Jia Shung Wang
Department of Computer ScienceNational Tsing Hua University, Taiwan, R.O.C.
*Department of Information ManagementMing Chuan University, Taiwan, R.O.C.
OutlineOutline
Introduction - Block Motion Estimation
Traditional Fast Block Motion Estimation
Adaptive Complexity-Distortion Block Motion Estimation
Experimental Results Conclusions
Full-Search Block Full-Search Block MatchingMatching
Searcharea
Current block
motion vector = ),(minarg),(
vuMAEvu
N
i
N
jPvjPuibjia
NNvuMAE
1 1),(),(
1),(
vector MAE
(-3,-3) 50(-2,-3) 70
(-1,-3) 45
(-2,2) 0
(3,3) 120
Frame n-1 Frame n
x
y
0 1 2 3-1-2-31
2
3
-1
-2
-3
Motion vectorMotion vector
Three-Step Search
Fast Block Matching Fast Block Matching AlgorithmAlgorithm
4
2
x xxxx
x xx
1
motion vector = Pvu,P- and ),,(minarg),(
vuMAEvu
Current frame
1616
Referenced frame
Search area
Motion vector
Current block
fast block matching- three-step search, four-step search- new three-step search reduce check point (u,v)
Problems of the Problems of the Traditional BMTraditional BM
(1) Fixed Computational Complexity
10log(check point)
400500600700800900
10001100
4 9 14 19 24 29
FSBMTSSNew-TSSFSS
MSE
Problems of the Traditional Problems of the Traditional BM (cont.)BM (cont.)
(2) Complexity-Distortion Optimization
254=100
:1+9+1+25 = 36
Block 1 Block 2 block3 Block 4
The search path for TSS
50
50
50
50
60
40
40
40
30
30
30
30
80
70
60
50
Initial mv (0,0)
Step one(8 check points)
Step two(8 check points)
Step three(8 check points)
Complexity-Distortion Complexity-Distortion Optimization BMOptimization BM
10log(check point)
400500600700800900
10001100
4 9 14 19 24 29
CDOBM
FSBM
TSS
New-TSS
FSS
MSE
flexible computational complexity better complexity-distortion performance
Complexity-Distortion Complexity-Distortion OptimizationOptimization
50
50
50
50
60
40
40
40
30
30
30
30
80
70
60
50
ComplexityTarget Complexity subject to
min
i iDistortion
In Rate-Distortion Optimization
trellis system- dynamic programming Viterbi algorithm
In Complexity-Distortion Optimization
MSE Benefit ? Non-Reusable Heuristic search algorithm
0 20
How to Define MSE How to Define MSE benefitbenefit
In each checking step, select the block that could achieve the maximum improved distortion benefit to apply searching procedure
Block 1 Block 2 block3 Block 4
50
50
50
50
60
40
34
30
30
20
20
20
80
65
60
50
Initial mv (0,0)
Step one(8 check points)
Step two(8 check points)
Step three(8 check points)
1 23
4
Predictive Complexity-Distortion Predictive Complexity-Distortion Benefit ListBenefit List
300
Block1
500
Block2
200
Block3
400
Block4
100
Block5
MSE:
300
Block1
500
Block2
200
Block3
400
Block4
100
Block5
benefit:
Assign benefit
Sort500
Block2
400
Block4
300
Block1
200
Block3
100
Block5
Predictive Complexity-Distortion Benefit List
Updating PCDB ListUpdating PCDB List
Select Block2 to check candidate blocks (MSEcheck=250)
500
Block2
400
Block4
300
Block1
200
Block3
100
Block5
PCDB List
Update PCDB List
400
Block4
350
Block2
300
Block1
200
Block3
100
Block5
Benefit2= 0.7Benefit2
MSEcheck<MSE2 MSEcheck>MSE2
Update PCDB List
400
Block4 Block1
250
Block2
200
Block3
100
Block5
Benefit2=MSEcheck
300
Flexible Fast Block Matching Flexible Fast Block Matching Algorithm DesignAlgorithm Design
Select the first blockfrom PDCB list
Checking candidate blocks
Yes
No
End
InitializePCDB list
StartUpdating PCDB list Total complexity >
target complexity ?
Checking Candidate Motion Checking Candidate Motion VectorVector
Select Block2 to check candidate motion vector
500
Block2
400
Block4
300
Block1
200
Block3
100
Block5
PCDB List
Search patternSearch patternFSBM 3-Step
Experimental ResultsExperimental Results
QCIF: “Miss America”, “Suzie” SIF: “Football” full-search block matching, 3-step
search, new 3-step search, 4-step search
frame rate: 15Hz and 7.5Hz search area: SIF (-15,15), QCIF (-7,7) is set as 0.8
Experimental Results Experimental Results (cont.)(cont.)
The complexity-distortion performance comparison for the QCIF "Miss America" with (a) frame rate 15Hz
7
9
11
13
15
17
19
0 10 20 30 40 50
flexible-FSBM
flexible-TSS
flexible-New-TSS
flexible-FSSFSBM
TSSNew-TSSFSS
Complexity (10ln(check point))
Distortion (MSE)
Experimental Results Experimental Results (cont.)(cont.)
The complexity-distortion performance comparison for the QCIF "Miss America" with (a) frame rate 7.5Hz
11
16
21
26
31
36
41
0 10 20 30 40 50
flexible-FSBM
flexible-TSS
flexible-New-TSS
flexible-FSS
FSBMTSS
New-TSSFSS
Complexity (10ln(check point))
Distortion (MSE)
Experimental Results Experimental Results (cont.)(cont.)
video frame rate Low High Low High
15 FSBM New-TSS TSS FSS7.5 FSS New-TSS TSS FSS
15 FSBM New-TSS TSS FSS7.5 FSS New-TSS TSS FSS
15 FSS New-TSS FSBM FSS7.5 TSS New-TSS FSBM FSS
Football
The Best Algorithm The Worst Algorithm
Miss America
Suzie
complexity
The performance summary of the flexible BMAs
ConclusionsConclusions
Discuss how to utilize the limited computational complexity of the block matching algorithm to achieve the maximum quality of the compensated image under a target computational complexity
Propose predictive complexity-distortion benefit list technique
The flexible block matching algorithm design not only improves the efficiency of the traditional BMAs, but also provides a flexible motion estimation tool that allows user to terminate it at any computational complexity