feature-based intra-/intercoding mode selection for h.264/avc c. kim and c.-c. jay kuo csvt, april...
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Feature-Based Intra-/InterCoding Mode Selection for H.264/AVC
C. Kim and C.-C. Jay Kuo
CSVT, April 2007
Outline
Introduction Overview of Proposed Algorithm Feature Selection Feature Space Partitioning Coding Mode Prediction Experimental Results
Introduction
Inter/Intra Mode Decision in H.264 Skip mode, direct mode, intra modes, and inter
modes Full mode decision
Testing all possible modes and then choosing the best mode with smallest cost
Fast algorithms Selection of optimal inter-prediction mode Selection of optimal intra-prediction mode Binary decision of intra/inter mode
Overview of Proposed Algorithm
Motion activity
f1, Residual of intra prediction
f0, Residual of inter prediction
MB
Risk-Free
Risk-Tolerable
Risk-Intolerable
Choose min(f0,f1)
Compute risk- minimizing mode
Full mode decision
Feature Selection (1/4)
Intra mode feature Calculate SATD for 5 modes
DC, vertical, horizontal, diagonal down-left, and diagonal down-right
Let f1 or fIntra be the SATD of the MB of the chosen modes
Feature Selection (2/4)
Inter mode feature MV is obtained by
MVFAST + Two more candidates Residual of every visited point is remembered
in the memory Search points of a MB < 512
Let f0 or fInter be SATD of MB residual of the chosen MV
(i,j)
(i-1,j-1)
nn-1
Feature Selection (3/4)
Motion activity classification
Motion activity, decision error, and skipped frames Decision metric
df = f1 – f0 Intra (Inter): df < 0 (df > 0)
Decision error probability P(df<0inter)+P(df>0intra)
22yx vvMV
Feature Selection (4/4)
Motion activity, RD cost difference dc, and feature difference df dc = (D1 +1R1) - (D0+ 0R0) Positive (Negative) if inter (intra) is better
Low motion High motionmedium motion
Best intra mode Best inter mode
Feature Space Partitioning
The 3-D feature space is partitioned into three regions (off-line)
Lp: normalized RD cost between the best mode and the wrongly selected mode
pp
pp
p
LLR
LLLR
LLR
MVffF*
eintolerabl
*freetolerable
freefree
10 |]|,,[
Inter mode featureIntra mode feature
Motion activity
Threshold
TT
TT
DR
RDRRLp
)ˆ()ˆ(
Feature Space Partitioning
Let every cell has about equal training data |MV|
f0
f1
Feature Space Partitioning
Getting training data from Akiyo, Hall Monitor, Foreman, Coastguard, Stefan, Table Tennis, and Mobile.
Coding Mode Prediction (1/4)
Risk-Free region Distribution of f0 and f1 in a given motion
classbased on feature difference
Risk free
Coding Mode Prediction (2/4)
Risk-tolerable/-intolerable region
Risk-tolerable and Risk-intolerable
Coding Mode Prediction (3/4)
Risk-tolerable region Risk function
1
0
1
0
)|~(~
i jjiij mmPCR
),~( jiij mmPC
)(
~
j
ij
mP
C
i
dFFfFmP j)()|(
)~()~|( iij mPmmP
1
0
)()(i
ii
dFFfFR
1
0
)|()(j
jiji FmPCFFor simplicity, let stands for cost instead of R
mj is the best modem0: intram1: inter
The chosen mode
Cost of deciding ~mi under mj
Coding Mode Prediction (4/4)
Risk-minimizing mode selection Mode selection rule
)|()|( 0001010 FmPCFmPC )|()|( 0101111 FmPCFmPC 0 0
Likelihood ratio1.Parametric2.Semi-parametric3.Nonparametric
Experimental Results (1/6)
Environments JM7.3a 32 x 32 motion search range Fast full search with 5 reference frames No B-frame QP= {10, 16, 22, 28, 34} 5 skipped frames
Experimental Results (2/6)
QCIF Table Tennis
Experimental Results (3/6)
QCIF Table Tennis
Computation complexity Saving time
Experimental Results (4/6)
QCIF Foreman 5 skipped frames
0 skipped frames
Experimental Results (5/6)
QCIF Stefan
Experimental Results (6/6)