adjustable partial distortion search algorithm for fast block motion estimation chun-ho cheung and...
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Adjustable Partial Distortion Search Algorithm for Fast Block Motion EstimationChun-Ho Cheung and Lai-Man Po
Department of Electronic Engineering,
City University of Hong Kong
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,TECHNOLOGY,
VOL. 13, NO. 1, JANUARY 2003VOL. 13, NO. 1, JANUARY 2003
Outline
Introduction to partial distortion search algorithm
Progressive partial distortion Preliminaries Fast BMA and Searching Speed Limitation Formation of PPD PPDS Algorithm
Adjustable partial distortion comparison Experimental results
Partial distortion search algorithms
Alternating Subsampling Search Algorithm (ASSA)
{Pixel-decimated (4 : 1)}
Normalized Partial Distortion Search algorithm (NPDS)
{early rejection, halfway-stop}
Partial distortion search algorithms Without limitation of checking points Lack the flexible adjustability between the
prediction quality and searching speed Adjustable Partial Distortion Search algorithm
(APDS) Increase the searching speed of NPDS by introduction
of progressive partial distortions (PPD) at very early stages.
Adjustable partial distortion comparison method with enabling the quality/speed control.
Progressive Partial Distortion-
Preliminaries
The basic operations of computing SAE are absolute and addition operations,and require about (3N 2 -1) operations per BDM.
Progressive Partial Distortion - Fast BMA and Searching Speed Limitation
For one second of K-Hz I x J video sequence with search windows ±W K(I/N)(J/N)(3N2-1)(2W+1)2
3SS Minimizing the searching points in (2W+1)2
ASSA and NPDS Reduce the BDM’s (3N2-1) ASSA => 4 times speedup ASSA + subblock or subsampled motion field => 8 times sp
eedup NPDS => 12-13 times speedup
Conventional partial distance search algorithm
•Pixel-by pixel basis•Obtains the optimal solution as in FS•The basis for developing NPDS
1 9 3 13
11 5 15 7
4 14 2 10
16 8 12 6
S = 4
T = 4
P =16
NPDS
Saving of multiples of 16-pixel matching-operations
It limits the maximum possible speedup ratios to Num(block)/Num(d1) or 16 times theoretically.
PPDS are proposed to be used in the first few stages of NPDS for increasing the rejection rate.
Number of pixel in the candidate block
First partial distortion d1
•Also, wider range of quality control
Formation of PPD
•Theoretically, it is a combinational-nature problem to divided d1 into smaller partitions.•Regularity of the PPD patterns favors both hardware and software Implementations.
P = 16 P = 16 partitionspartitions
Proposed PPD patterns6 14 8 16
10 2 12 4
7 15 5 13
11 3 9 1
3 7 4 8
5 1 6 2
4 8 3 7
6 2 5 1
2 4 2 4
3 1 3 1
2 4 2 4
3 1 3 1
1 2 1 2
2 1 2 1
1 2 1 2
2 1 2 1
4 6 4 6
5 2 5 3
4 6 4 6
5 3 5 1
3 5 3 5
4 1 4 2
3 5 3 5
5 2 4 1
1 5 3 5
5 4 5 4
3 5 2 5
5 4 5 4
2 3 2 3
3 1 3 1
2 3 2 3
3 1 3 1
(a)PPDS(v1)Group of 1 pixel
(b)PPDS(v2)Group of 2 pixels
(c)PPDS(v3)Group of 4 pixels
(d)PPDS(v4)Group of 8 pixels
(h)PPDS(v8)(4,4,8)
(g)PPDS(v7)(1,1,2,4,8)
(f)PPDS(v6)(2,2,4,4,4)
(e)PPDS(v5)(1,1,2,4,4)
•Total G partial distortions G=H+P-1
H=16, G=31 H=8, G=23 H=4, G=19 H=2, G=17
H=6, G=21 H=5, G=20 H=5, G=20 H=3, G=18
An Example
2 4 2 4
3 1 3 1
2 4 2 4
3 1 3 1
(c)PPDS(v3)Group of 2 pixels
•Total G partial distortions G=H+P-1
H=4, G=19
For dg|1≤g≤4,
For dg|5≤g≤19, same as the pervious PPD Formulation.
PPDS Algorithm
nknfforDknfDN
NDDnDDwhereDD
MINg
MINMINggMINg
),(,),(
/,/,2
2
2)1(),( kNnkknf
f(n,k)=n, where n is the number of pixels cumulated in Dg
•Normalized Distortion Comparison criteria (NDC):
•Adjustable function:
Average distance from and probability of the true motion vector per block against the quality factor k
SIF, “tennis”
Average distance from and probability of the true motion vector per block against the quality factor k
CCIR601, “tennis”
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