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:

Experimental resultsCOMPUTATIONS AND PSNR

(dB) PERFORMANCE COMPARISON

COMPUTATIONS AND PSNR (dB) PERFORMANCE COMPARISON

COMPUTATIONS AND PSNR (dB) PERFORMANCE COMPARISON

Performance comparison of APDS(k) at several quality factor k

PPDS(v3)

Performance comparison of APDS(k) at several quality factor k

PPDS(v3)

Performance comparison of APDS(k) at several quality factor k

PPDS(v3)

Average PSNR performance and speedup ratios

SIF, “tennis”

Average distance from and probability of the true motion vector per block against the quality factor k

SIF, “tennis”

Average PSNR performance and speedup ratios

CCIR601, “tennis”

Average distance from and probability of the true motion vector per block against the quality factor k

CCIR601, “tennis”

MSE performance

Operations

Distance per block

Probability per block

Operations Translation in RHS

Conclusions

NPDS + different PPD Computational reduction up to 61.54 times with le

ss than 0.94-db degradation on PSNR compared to FS’s.

The APDS is very suitable for a wide range of video applications such as low-bit-rate video conferencing and high-quality video coding.