FAST MULTI

REFERENCE MOTION
ESTIMATION VIA STATISTICAL
LEARNING
FOR H.264/AVC
Chen

Kuo
Chiang and Shang

Hong Lai
Department of Computer Science, National
Tsing
Hua
University, Taiwan
Presenter:
Yeh
, Ta

Li
2009/10/14
1
Introduction_1
•
H.264/AVC
–
Video coding standard of Joint Video Team (JVT)
–
Efficiency and quality
•
Variable block size motion compensation
•
Multiple reference frames
•
Directional spatial intra prediction
•
In

loop
deblocking
filtering
–
Computational complexity
2009/10/14
2
Introduction_2
•
Multiple reference frames
–
High video coding quality
–
Not every reference frame is useful
•
Two methods to solve the problems
–
Rule

based approach
•
Criteria to eliminate unnecessary reference frames
•
Whether it is necessary to search more frames?
–
Inter SATD, intra SATD and motion vector compactness are examined
(
Y.

W. Huang,2003)
•
Check temporal and spatial content information in
macroblock
(MB level)
–
Speed up the search process
(Q. Sun, 2007;
T.

Y.
Kuo
, 2008)
–
Semi

statistical learning approach
•
Appropriate number of reference frames (statistical)
(P. Wu, 2003)
2009/10/14
3
Purpose
•
Statistical learning approach
–
Decide the best reference frame number
1.
Choose representative feature
2.
Train SVM (Support vector machine) for
classification
3.
Off

line pre

classification approach
•
Complete machine learning approach
2009/10/14
4
Analysis of multi

reference motion
estimation (MRME)
•
MRME have higher coding efficiency, but not
all the sequences
•
Search more reference frames is helpful when:
–
A smaller block partition is chosen (variable

block

size motion estimation)
–
Occlusion or uncovering occurs
–
Marcoblock
is across object boundaries
–
Marcoblock
contains complicated texture
2009/10/14
5
(
Y.

W. Huang,2003;
Y. P. Su ,2006)
Feature selection_1
•
Representative features for each
macroblock
:
–
Block partition
–
Best inter

SAD
•
lower indicates higher probability of using only one
reference frame
–
Motion vector difference (MVD) magnitude (MVM)
•
MVD:
–
Smoothness
–
Small MVD = similar motion and unlikely to cross object
boundaries
•
MVM:
–
Small MVM= unlikely to cross object boundaries
2009/10/14
6
Feature selection_2
–
Best intra

SAD and gradient magnitude
•
Intra

SAD:
–
Minimum SAD value after intra prediction of an
macroblock
–
Large SAD = complicated texture
•
Gradient magnitude
–
Summation of gradient magnitudes of all pixels inside the
macroblock
–
Reflect whether the texture is strong
2009/10/14
7
Fig. 1. The probability of reference frame 1~5 with
respect
to (
a) best inter

SAD,
(
b) gradient
magnitude
(a)&(
b) indicate the
dropping of
the probability
of reference frame 1 as the amount of
the specified
features increase to some levels.
2009/10/14
8
Fig. 1. The probability of reference frame 1~5 with
respect
to (c
) MVD, (
d) MVM
(c)&(d) show
that
the decreasing probability
of reference frame
1
and the
increasing probability
of reference 4 and 5
as the
amount of features increases
2009/10/14
9
Fig. 1. The probability of reference frame 1~5 with
respect
(
e) best intra

SAD and (f) block partition.
(
e)&(f),
the probability
of reference 1 is rather high in all
conditions.
The
best intra

SAD
and
Block Partition
features
may
NOT
be so
effective.
2009/10/14
10
Fast multi

reference motion
estimation vs. statistical learning
•
Problem of multi

reference motion estimation
(ME)
–
Classification problem
•
Solution:
–
ME on the first reference frame
–
Predict the number of necessary frames based on
the 6 features
2009/10/14
11
Formulation of reference frame
selection
•
In ME
–
The number of reference frames is set to 5
–
Up to 16
–
Each MB
define 5 classes (use 1

5 ref. frames)
–
Reference 2,3,4,5 are similar probability
distribution than 1. (fig 1)
–
Two binary classifiers
2009/10/14
12
Training and pre

classification
•
Support vector machine (SVM)
–
Use for solving limited training samples
•
Training data
–
Obtained by applying H.264 ref. code JM 11.0
–
To 3 video sequences
•
News, container and coastguard videos
•
Pre

classification
–
Run time classification spend too much time
–
Generate all possible combinations features
–
Training and storing the result
–
Search look

up table for the corresponding result
2009/10/14
13
Flow chart of the proposed algorithm
Fig. 2. Flow chart of the proposed reference
frame
prediction
algorithm for motion
estimation.
2009/10/14
14
Experimental results_1
2009/10/14
15
Experimental results_2
2009/10/14
16
Conclusion
•
Present a multi

reference ME algorithm based on
statistical learning
•
To decide the best reference frame number
–
The feature analysis shows :provide good discriminating
feature
•
Execution time is 3 times faster than the existing fast
ME method
•
Future work:
–
Investigate more reliable features to improve classification
rate
–
Variety of videos of different motion patterns (fast, median
and slow) can be included into training data
2009/10/14
17
Reference
[1] Y.

W. Huang, et al., “Analysis and reduction of
reference frames
for motion
estimation in MPEG

4 AVC/JVT/H.264,”
in Proc
. IEEE ICASSP, Apr. 2003.
[2] Q. Sun, X. H. Chen, X. Wu, and L. Yu, “A
content

adaptive fast
multiple reference
frames motion estimation in H.264,”
in Proc
. IEEE ISCAS, pp. 3651

3654, May 2007.
[3] T.

Y.
Kuo
and H.

J. Lu, “Efficient reference frame selector
for H.264
,” IEEE Trans. on
Circuits & Systems for Video
Technology, vol
. 18, no. 3, March 2008.
[4] P. Wu, C.

B. Xiao, “An adaptive fast multiple reference
frames selection
algorithm
for H.264/AVC,” in Proc. IEEE Int. Conf.
on Acoustics
, Speech, and Signal Processing,
Apr. 2008
[5] Y. P. Su and M.

T. Sun, “Fast multiple reference frame
motion estimation
for
H.264/AVC,” IEEE Trans. on Circuits &
Systems for
Video Technology, vol. 16, pp.
447
–
452, Mar 2006
[6]
Corinna
Cortes and V.
Vapnik
, “Support

Vector Networks
,” Machine
Learning,
pp.273

297, 1995.
[7] T. M. Cover, Information Theory, Wiley

Interscience
, 1991
2009/10/14
18
Thanks for your
attention!!
2009/10/14
19
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