Transcript CONCEALMENT2.PPT

From Error Control to Error
Concealment
Dr Farokh Marvasti
Multimedia Lab
King’s College London
Outline
Philosophical Issues
 Previous Work on Forward Error
Control/Concealment
 Error Concealment in the Source
Encoded Coefficient Domain
 Error Concealment in the Time
(Speech) or Space (Image & Video)
Domain
 Suggestions for UMTS and Future Work

Philosophical Issues
•
•
•
•
The Better the Source Encoder, the Worse
the Error Concealment (EC)
Trade off Between FEC and EC: The More
Powerful the FEC, the Less Frequent the
EC
Measure of Performance: 1-FEC-Prob of
Error or SNR. 2-EC- Subjective Evaluation
FEC is a science and EC is an art!
C Shannon
•
“However, any redundancy in the
source will usually help if it is utilized
at the receiving point. In particular, if
the source has already redundancy and
no attempt is made to eliminate it in
matching to the channel, this
redundancy will help combat noise”
J Hagenauer
•
•
“We claim that whenever some form of
concealment of the decoded source signal is
applicable, which means that some
redundancy is left in the source signal, “The
Source-Controlled Channel Decoding” can
also be applied. It is better to avoid errors
rather than to conceal them.”
“One can use more sophisticated error
concealment by using the channel decoder
soft output…i.e., hard decision and its
Previous Work: Vary, et al
•
•
MAP Estimation: C= Max P(Ci |C
recived
Min-MSE:
 i
 i
C P C
givenCreceived
CMS
i
hard decision
Ch
) over all i
A Posteriori
soft output:
Reliability
P(Ci|Crec)
Estimator
Cestimate
My Previous Work in FEC/EC
FEC Using GF of Real Numbers
 Oversampling and Nonuniform
Sampling Th for Error Correction
 Works for Erasure Channels and
Impulsive Channels
 Extension to Error Concealment
 Brief Explanation and Examples of
Multimedia Signals

Summary of the EC Work at
Lucent
Image & Video EC: Presentation by Two
of my Ph.D... Students on July 23rd at
1:30pm
 1- EC for compressed Video (H.263 &
MPEG 4) by Hasan
 2- Robust Video Codecs for UMTS by
Nick

EC for FR GSM
Direct FFT Approach does not work too
well for prediction. However, it does
show correlation of coefficients within
frames
 The Best Linear Prediction is derived
from Yule Walker equations… (Alexis)
 Ad-Hoc Methods: averaging the
previous and the future frame
coefficients works better than simple
substitution

EC for FR GSM
Combining the previous N frame and
the future M Frame Coefficients with a
weight factor works best
 Mathcad Examples:

Comparison of Yule-Walker with
Substitution
p 0
p 5
coefficient

33
n 3
coefficient
 22
n 2
coefficient
2
n 3
coefficient

2
n 2
coefficient

27
n 1
coefficient
 13
n 1
yulewakern  26.977
yulewakern  10.563
p 4
p 15
coefficient
7
n 3
coefficient
7
n 2
coefficient

4
n 3
coefficient
4
n 2
coefficient
7
n 1
coefficient
3
n 1
yulewakern  5.652
yulewakern  3.039
Enhanced GSM, G.729, etc.
It is not always a good idea to do
interpolation/prediction in the source
encoded domain (e.g., the 76 GSM
coefficients).
 If previous frames are stored in time
domain or its frequency transform (FFT,
DCT, Wavelet, etc.), extrapolation
seems to be easier to perform. See next
slide.
 *This approach does not depend on

Prediction of 18 future samples
from the past 54 samples of a real
female speech signal
17.277689
Y
k
Y1
2.752105 10
20
10
3
0
0
10
1
20
30
40
k
50
60
70
80
72
trace 1
4
1.5 10
n
( e)
4
1 10
j
( e1 )
q
j
5000
0
0
10
20
trace 1
trace 2
30
j
40
50
60
70
80
72
 2
n
Problems with FFT
High accuracy of computation is
needed. Otherwise, the prediction
becomes unstable, see next slide
 Research on other exponential or other
kernels are under study.

Algorithm for Extrapolation
2
XXj
ttj
m=
exp
j
 ii
m
1
q
DD XX
hh
1
s
90
1.05
120
hh
tt
60
120
0.79
150
mag ( s )
ii
30
0.53
180
150
mag ( s )
0
0
210
240
180
0
1  2  
300
330
240
Er
j ( q )
trace 1
Er
r

t h2 
t
Er
n  inn
2

1  1
2 
t
E( t
h2 
300
270
( q )
1
0
210
trace 1
t
30
0.53
270
lk
60
0.26
j
330
ii
90
1.05
0.79
0.26
lk
CFFT ( XX)
Err
rr
n  inn
2

rr ) h2 
t
h 2 
t

E( rr
1 
n  inn
2  )
A different Kernel: exp(j)
120
90
1.05
1.05469
60
0.79
150
mag ( s )
ii
30
0.53
0.26
lk
180
0
0
210
1
330
240
300
270
ii ( q )
lk
trace 1
The above kernel was implemented on a DSP.
It is good for burst error correction but not for EC.
Frequency Domain Approach
Take Karhunen Loeve Transform of
Previous Speech Frames and Try to do
Prediction in the Transform Domain.
DCT, FFT, etc are Suboptimal Solutions.
 The Advantage is that Both Types of
Correlation (Intra and Inter Frame) are
Taken into Account

Future Work
• Complete the Yule
Walker Prediction
• Develop the Time
Domain Extrapolation
• Study the Frequency
Domain Approach
• Joint Optimization of
Channel Coding,
Source Coding and
EC.
Conclusion: Lessons for UMTS
VAD Should Send A Code for Silence
Frames Immediately. This is Essential
for EC.
 Smaller Frame Sizes are Better for EC
at the Expense of Less Compression.
 More Frame Delay Should be Accepted
in the Standards for Better EC.
 Silence Frames Can be Used for the
Retransmission of Previous Frames if
