# New Adaptive Algorithms for GOP Size Control with Return Channel Suppression in Wyner-Ziv Video Coding

Authors: Charles Yaacoub; Joumana Farah; Béatrice Pesquet-Popescu
Journal: International Journal of Digital Multimedia Broadcasting
 (2009)

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## Abstract

We present novel algorithms for adaptive GOP size control in distributed Wyner-Ziv video coding, where an H.264 video codec is used for intracoding of key frames. The proposed algorithms rely on theoretical calculations to estimate the bit rate necessary for the successful decoding of Wyner-Ziv frames without the need for a feedback channel, which makes the system suitable for broadcasting applications. Additionally, in regions where H.264 intracoding outperforms Wyner-Ziv coding, the system automatically switches to intracoding mode in order to improve the overall performance. Simulations results show a significant gain in the average PSNR that can reach 3 dB compared to pure H.264 intracoding, and 0.8 dB compared to fixed-GOP Wyner-Ziv coding.


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## Body

## 1. Introduction

Distributed source coding [1–20] has recently
become a topic of great interest  for the research community, especially in the  world of video communications. In traditional video coding techniques, such as
MPEG or H.26x, motion estimation is performed at the encoder side, which yields
very complex encoders, but simple decoders. This is suitable for applications
where a video sequence is encoded once and decoded several times, such as video
broadcasting or video streaming on demand. A simple decoder is desired in this
case to allow low-cost receivers for the end users.On the other hand, some
applications require simple encoders. Distributed Video Coding (DVC) was
introduced [7, 8] to permit low-complexity encoding for small power-limited and
memory-limited devices, such as camera-equipped mobile phones or wireless video
sensors, by moving the computation burden from the encoder side to the decoder.
Increased decoding complexity can be tolerated in this case since, in such
applications, the decoder is usually located in a base station with sufficient
resources.It is known from information
theory that, given two statistically dependent sourcesX and Y, each source can
be independently compressed to its entropy limit, H(X) and H(Y), respectively. However,
by exploiting the correlation statistics between these sources, X and Y can be
jointly compressed to the joint entropy H(X,Y). This results in a more
efficient compression since H(X,Y)≤H(X)+H(Y). The idea behind DVC goes back to the 1970s when Slepian
and Wolf ‎[21] proved that, if the source Y is compressed to its
entropy limit H(Y), X can be transmitted at a rate very close to the conditional
entropy H(X|Y), provided that Y is available at the receiver as side
information for decoding X. Since H(X,Y)=H(Y)+H(X|Y), X and Y can be
independently encoded and jointly decoded without any loss in the compression
efficiency, compared to the case where both sources are jointly encoded and
decoded. The application of this concept to lossy source coding is known as the
Wyner-Ziv coding ‎[22].In practical DVC systems, a
subset of frames, known as key frames, is usually compressed using traditional
intracoding techniques. One or more frames following each key frame, known as
Wyner-Ziv (WZ) frames, are then compressed by appropriate puncturing of the
parity bits at the output of a channel coder. At the receiver side, previously
decoded (key or WZ) frames are interpolated to generate the necessary side
information for the decoding process.The first practical DVC systems
appeared in 2002, when Puri and Ramchandran‎[7] proposed a block-based codec using syndromes, and Aaron
et al. ‎[8] proposed a frame-based codec using turbo codes. The
frame-based approach has gained a greater interest in the research community. However,
it still suffers from several weaknesses that limit its use in real-life
applications.One of the main drawbacks of
current DVC systems is the use of a feedback channel (FC)‎[11] to allow flexible rate control and to ensure
successful decoding of WZ frames. The FC is not suitable for real-time systems
(e.g., broadcasting applications) due to transmission delay constraints.
Additionally, in multiuser applications with rate constraints, the application
of WZ coding becomes impractical because of the difficulty of implementing
appropriate rate allocation algorithms. Furthermore, since several decoding
runs are required to successfully recover a WZ frame, the FC imposes
instantaneous decoding in the receiver. For all these reasons, the introduction
of new techniques for estimating the necessary bit rate to successfully decode
each WZ frame becomes crucial. In fact, the problem of the return channel in
DVC has rarely been targeted in the literature. Artigas and Torres ‎[12] and
Morbée et al.  ‎[13]
proposed techniques that rely on performance tables used by the encoder to
predict the compression level of each particular frame. Kubasov et al. proposed
in ‎[18] an encoder rate control technique that reduces the
use of the feedback channel. Transform domain WZ rate control algorithms were
introduced in ‎[19] (for a DCT-based WZ codec) and ‎[20] (for a wavelet-based WZ codec). However, these
studies do not take into account the rate constraints in limited-bandwidth
applications. Besides, the influence of the channel impairments on the proposed
rate allocation techniques is not considered. In [6, 14, 15], we proposed a novel
technique for the removal of the feedback channel in DVC systems, using an
analytical approach based on entropy calculations. Designed for a multiuser
scenario, the proposed technique takes into account the amount of motion in the
captured video scene as well as the transmission channel conditions for every
user, in order to allocate unequal transmission rates among the different
users.Another drawback of current DVC
systems is that the quality of the generated side information greatly affects
the system’s performance. Even though the interpolation algorithm used at the
decoder strongly influences the side information quality, key frames are
essential components in the interpolation process and thus, having high-quality
key frames is crucial. Therefore, a very high peak signal-to-noise ratio (PSNR)
is desired (for the key frames) in order to allow a successful decoding of the
WZ frames at feasible WZ bit rates. This condition can result in a very high
bit rate requirement, which is not possible in limited-bandwidth applications.
Additionally, when the key frames are too distant apart, the quality of the
side information is degraded. As a result, most research on DVC considers a group
of pictures (GOP) size of 2, that is, each key frame is followed by one WZ
frame. Several attempts have been made to increase the GOP size in DVC. In‎[16], Aaron et al. impose the use of high-quality key
frames with fixed GOP sizes ranging from 2 to 5. As the GOP size increases, the
system’s performance decreases. However, lower rates could be reached with
greater GOP sizes due to the high bit rate requirements of the key frames. In ‎[17], Ascenso et al., present a content-adaptive GOP size
selection algorithm. The number of frames in a GOP is determined dynamically
depending on motion activity. However, the proposed algorithm uses four
different metrics in order to determine the size of a GOP, which results in a
significant increase of the encoder’s complexity. Furthermore, both studies rely
on a feedback channel for the decoding of WZ frames, and on H.263+ for key frame
encoding. Since H.264/AVC ‎[23] greatly outperforms H.263+, it is expected that H.264
intracoding will outperform both Wyner-Ziv systems too.In this paper, we present novel
algorithms for dynamically varying the GOP size in distributed video coding. Our
simulations are performed using a pixel-domain WZ video codec. However, the
same algorithms can be applied in a transform-domain codec, which improves the
overall performance at the expense of a slight increase in encoding and
decoding complexity. Our method relies on H.264 for the encoding of key frames,
and on our previously developed WZ rate estimation technique presented in [5, 6, 14, 15],
where quadri-binary turbo-codes are used for the compression of WZ frames. Only
one metric is required to determine the size of a GOP, and a feedback channel is
not needed for the decoding of WZ frames. Automatic mode selection allows the
system to switch to H.264 intracoding mode in regions where H.264 outperforms
WZ video coding. Furthermore, based on our study in [5, 6, 14, 15], our algorithms
can be easily extended to take into account channel impairments and multiuser scenarios.This paper is organized as
follows: in Section2, we present a brief description of the Wyner-Ziv video
codec used in this study, along with the rate estimation technique for WZ
frames. Our adaptive algorithms for GOP size control are detailed in Section 3,
and the additional complexity they incur at the encoder side is analyzed in
Section 4. Finally, simulation results are presented in Section 5.


## 2. Description of the Distributed Video Coding System

The distributed video coding system
considered in this study can be represented by the block diagram in Figure1.
Key frames are compressed using H.264 intracoding. After H.264 decoding, a key
frame is stored in a buffer in order to be used during the process of
generating the side information necessary for the decoding of WZ frames.Figure 1
Block diagram of the pixel-domain distributed video coding system.Compression of the Wyner-Ziv
frames starts by a uniform scalar quantization to obtainM-bit representations
of the eight-bit pixels, M∈{1,2,4}. The turbo encoder [24–27] consists of a
parallel concatenation of two 16-state quadri-binary convolutional encoders
separated by an internal interleaver and resulting in a minimum global coding
rate of 2/3. The generator polynomials in octal notation are
(23,35,31,37,27)8 from  ‎[27]. At the encoder output, systematic information is
discarded, while parity information is punctured and transmitted to the
decoder. Side information (SI) of a particular WZ frame is generated at
the receiver by motion-compensated interpolation of two previously reconstructed
(WZ or H.264) frames. The frame interpolation technique assumes symmetric
motion vectors as explained in [8, 16]. The interpolated frame is then quantized
and fed to the turbo decoder as a noisy version of the missing systematic data.
Turbo decoding is realized by iterative Soft Input Soft Output (SISO) decoders
based on the Max-Log-MAP (maximum a posteriori) algorithm ‎[28]. However, metric calculations are
modified in order to take into account the nonbinary nature of the turbo codec,
and the residual signal statistics between the WZ and SI frames. Finally, the
reconstruction block is used to recover an eight-bit version of the decoded WZ
frames using the available side information ‎[8]. The final output is then stored in a buffer if
needed to generate side information for another WZ frame.In a previous work‎[5], we presented an
analytical approach for estimating the compression limits of a pixel-domain
Wyner-Ziv video coding system with a transmission over error-prone channels,
without the need for a feedback channel. Simulation results showed that the
theoretical bounds can be used in a broadcasting system to predict the
compression level for each frame with a minor loss in the
decoding PSNR, compared to the classical feedback-based coding system. In the
absence of transmission errors, the theoretical compression bound of the WZ
frames for the system in Figure 1 can be expressed as (see [5, 6])
(1)H(X|Y)=−12M∑i=02M−1∑j=02M−1g[cα22Me−α|di−j|Ldi−j],
where X is a quantized WZ
frame, Y is the corresponding side information, g(a)=alog2(a), Ldi−j=2M−|i−j| is the number of possible couples (i,j) that
yield the difference
i−j=d, di−j=2M(i−j) is the difference between two quantized pixel
values, c is a scaling factor, and α is
the parameter of the Laplacian
distribution modeling the statistics of the residual error between the side
information and the WZ frame ‎[8]. Since there is
always a gap between theoretical and practical bounds, we have determined in ‎[14]that, in order to obtain a good average
performance, the ratio between the average number of transmitted bits per pixel
and the lower compression bound expressed in (1) must be not less than T4=2.4
for M=4, T2=4 for M=2, and T1=6 for M=1. As a result, the
encoder can determine the compression rate for a given WZ frame by first
determining its compression bound, then multiplying it with the corresponding
coefficient TM, depending on the value of the WZ quantization
parameter M.


## 3. Adaptive Algorithms for GOP Size Control in Wyner-Ziv Video Coding

In a video sequence, when there is low
motion, consecutive frames are highly correlated. The aim of varying the GOP
size is to allow the system to better exploit this property, by reducing the
number of intracoded key frames in regions where WZ frames would yield a better
rate-distortion (R-D) performance. In regions where intracoding outperforms WZ
coding (because of high motion), the GOP structure is reduced to one (H.264
intracoded) frame per GOP. This automatic mode selection allows the WZ encoder
to make use of H.264 coding efficiency to better improve the system’s R-D
performance.LetSmax represent
the maximum allowable GOP size. For each GOP, let R0 represent the
average bit rate assigned for the first frame (intracoded key frame) in the
GOP, and PSNR0 its PSNR. Smax can be chosen depending on
the system’s delay constraints. For a GOP of size S, let F0 denote
the key frame, F1,F2,…,FS−1 the WZ frames,
and FS the key frame of the next GOP.To perform GOP length decision,
our proposed algorithm operates as follows:Initially, setS=1.WhileS≤Smaxdo:IfS==1, go to
step (e), otherwise:
(a)
Interpolate betweenF0andFS
The interpolated frame serves as an estimate of the side information
available at the decoder during the decoding process of the WZ frameF⌊S/2⌋, located at half-distance between
F0 and FS.
Since motion estimation is not allowed at the encoder for complexity
reasons, average interpolation‎[8] can be used to estimate the side information that
will be available at the decoder.(b)
Estimate
the average bit rateR⌊S/2⌋
Given the WZ frameF⌊S/2⌋ and its
corresponding side information (estimate), the encoder determines its lower
compression bound, and consequently, its compression rate, as explained in the
previous section. The computation of R⌊S/2⌋ becomes straightforward.(c)
ComputePSNR⌊S/2⌋
Given the WZ frameF⌊S/2⌋ and its
corresponding side information (estimate), the encoder can determine an
estimate F⌊S/2⌋′ of
the decoded frame at the receiver by first quantizing the WZ frame, and
then reconstructing an 8-bit version using the available side information. The
PSNR is then computed using F⌊S/2⌋ and F⌊S/2⌋′.(d)
Repeat steps (a) to (c) until rate and PSNR estimates are obtained
for all the frames of the GOP
However, instead of interpolating betweenF0 and FS in step (a), the frames F0 and F⌊S/2⌋′ are first
used to generate a side information estimate for the frame located at half distance
between the two, and the same process in steps (b) to (d) is repeated. Then, a
similar procedure is performed in the second half of the GOP, using the frames
F⌊S/2⌋′ and FS. Frame F⌊S/2⌋′ is used instead of F⌊S/2⌋ because
the former better estimates the frame that will be available at the decoder
side since the latter is not known by the decoder.(e)
Estimate the average rate and PSNR obtained with a GOP of sizeS,
respectively, defined as(2)RavS=1S∑j=0S−1Rj,PSNRavS=1S∑j=0S−1PSNRj.(f)
DetermineλavS=PSNRavS/RavS
This represents the average PSNR per average unit bit rate estimated for
a GOP of sizeS.(g)
S=S+1.Intuitively, the bestR-D performance is obtained by
maximizing the average PSNR per unit bit rate. As a result, the system decides
the GOP length L as
(3)L=argmaxk=1,2,…,Smax(λavk).
In other words, if the system determines that the average
PSNR per unit bit rate obtained by WZ coding, for different GOP lengths
(S>1), is lower than the one obtained with H.264 intracoding (S=1), the
system switches to H.264 intracoding mode (L=1) and an H.264 I-frame is then
transmitted. Otherwise (L>1), an H.264 intracoded key frame is transmitted,
followed by L−1 WZ frames. Furthermore, since motion-compensated interpolation
yields better side information compared to average interpolation, in general,
the decoder is expected to perform better than estimated at the encoder side.
This procedure is repeated at the beginning of every GOP and thus, the GOP
length is dynamically varied along the sequence, in order to optimize the
overall performance.Figure2 presents  the algorithm above as a flow chart
diagram, and Pseudocode 1 shows the  Pseudocode of the recursive procedure R_PSNR_estimations(i,len) used to estimate the rate and PSNR for all
the frames in a GOP of size S, where i is the time index of the first
frame in the GOP, and len is the time interval between the frame at
index i and the next frame used during the interpolation process
(initially, len=S).Pseudocode 1:Pseudocode of the recursive procedure R_PSNR_estimations used to estimate the rate and PSNR for all the frames in a GOP.
procedure
R_PSNR_estimations(i, len) {If len<2Return// End of the
recursive function callsElsea=i,b=i+len //a and b are the time indices//
of the frames used during the//
interpolation process.d=⌊(b−a)/2⌋// time interval from a to the//
frame at mid distance//
between a and b.SI(a+d)=Interpolate(a,b)    // Perform average interpolation// between frames at time// indices a and b.Q(a+d)=QuantizeFrame(F(a+d))R(a+d)=EstimateBitrate(Q(a+d))// estimate the bitrate as// explained in Section 2.F(a+d)′=Reconstruct(Q(a+d),SI(a+d))   // reconstruct the// quantized WZ frame// given the estimated// side information.PSNR(a+d)=ComputePSNR(F(a+d)′,F(a+d))// Compute the PSNR// of the reconstructed// frame.R_PSNR_estimations(i, d);// Recursive function call
using// the first half of the GOP.R_PSNR_estimations(i+d, len−d)   // Recursive function call using// the second half of the GOP.End If}Figure 2
Flow chart diagram of the proposed GOP size control algorithm.In general, a constant quality is desired along the
sequence, since big fluctuations in PSNR yield undesirable visual effects. For
this reason, the encoder determines the rate for
the H.264 intracoded frames in such a way to obtain a near-constant PSNR in the
GOP. This can be done by one of several techniques:(i)
Using predefined performance tables that
determine H.264 rate-distortion relationships.(ii)
Using an analytical model for H.264
rate-distortion performance. This allows the system to avoid extensive table
search as in the previous technique. However, it is important in this case to
have an accurate, generalizedR-D model [29].(iii)
Trial and error: the system tries several
coding rates for each H.264 intracoded frame, and determines the PSNR for each
case. Then, the rate that yields a PSNR closer to the one of neighboring frames
is chosen. This method is more accurate than the previous ones. However, it can
result in significant delay and increased encoder complexity.The GOP size control algorithm
can be further simplified by assuming a constant PSNR for all the frames. In
this case, instead of maximizing the average PSNR per unit bit rate, (3)
reduces to minimizing the average bit rate per frame over all possible GOP
sizes. As a result, the simplified GOP size control algorithm operates as
follows:Initially, setS=1.WhileS≤Smaxdo:IfS==1, go to
step (d), otherwise:(a)
Interpolate fromF0andFS(b)
Estimate
the average bit rateR⌊S/2⌋(c)
Repeat steps (a) and (b) until a
rate estimate is obtained for all the frames in the GOP (replace framesF0andFSin step (a) with the corresponding frames as previously
explained in step (d) of the initial algorithm).(d)
DetermineRavS=(1/S)∑j=0S−1Rj(e)
S=S+1 Finally, the system decides the GOP length L as
(4)L=argmink=1,2,…,Smax(Ravk).This allows the system to avoid estimating the PSNR for
each frame, and the average PSNR per unit bit rate for each GOP size.


## 4. Complexity Analysis

While the aim of
DVC is mainly permitting the design of low-complexity encoders, our GOP size
selection
algorithms incur additional encoding complexity. In this
section, we present an analysis of the proposed algorithms computational load,
and we compare our dynamic algorithms with the one presented in‎[17].Table1 presents
an estimation of the necessary number of additional operations (OPs) incurred
by each iteration (for each frame in the GOP, for every GOP size k, 1≤k≤Smax) in the initial (nonsimplified) GOP size control
algorithm. P×Q represent the frame dimensions, and M the quantization parameter
for WZ frames.Table 1
Number of additional operations per frame for each GOP sizek, 1≤k≤Smax, incurred by the proposed GOP size control algorithm.
OperationAverage InterpolationReconstructionQuantization (WZ and SI)Estimation of theα parameterPSNR estimationRate (R) estimationAdditionsPQPQ2PQ−12PQ−11×22M−1MultiplicationsPQPQ+1PQ+45×22M+1Log or Exp12×22MComparisonsPQLogical AND2PQTOTAL2PQ2PQ2PQ3PQ3PQ+38×22MConsider for
example the number of operations performed to compute the PSNR. The calculation
of the PSNR consists of first computing the mean square error (MSE), taking its
inverse, multiplying it with a constant, computing the log of the result, and
finally multiplying it by 10. The square
error between two pixel values requires one addition operation and one
multiplication. To compute the MSE,PQ square errors are first computed (which
results in PQ additions and PQ multiplications) and summed  together (PQ−1
additions). The final result
is then divided by PQ. As a result, 2PQ−1 additions, PQ+4 multiplications, and
a log operation are performed in order to obtain the PSNR. A similar analysis
was performed on the other operations involved in the GOP size control
algorithm, and the results are summarized in Table 1.The total number
of operations is roughly obtained by summing the elements of the last row in
the table, which results in12PQ+(8×22M)+3 operations. Since, in our codec, the
maximum value for M is 4, and by assuming QCIF video sequences (P×Q=144×176),
the total number of operations becomes 306,179 OPs. In the simplified
algorithm, the computation of the PSNR is not performed. Thus, a reduction of
3PQ+3 operations is obtained, which yields a total number of 230,144 OPs.A similar study was performed on
the algorithm presented in‎[17], where four different metrics were used: the
difference of histograms (DH), the histogram of difference (HD), the block
histogram difference (BHD) and the block variance difference (BVD). Given the
parameters specified in ‎[17], we obtain 50,784 OPs for DH, 50,736 OPs for HD,
60,191 OPs for BHD, and 161,567 OPs for BVD, which results in a total of
323,278 OPs.Even though the
computational load of our initial algorithm and the one of the algorithm in‎[17] are almost similar, it is
important to note that our algorithm presents the additional property of
estimating the necessary bit rate without the need for a feedback channel, and
the possibility to take into account channel impairments based on our study in
[5, 6, 14, 15]. On the other hand, a reduced complexity of approximately 25% can
be obtained with our simplified algorithm, without a significant loss in R-D
performance as will be shown in the next section.


## 5. Experimental Results

In our simulations, we consider
three different QCIF video sequences with different levels of motion: Foreman,
Grandmother, and Salesman, sampled at a rate of 30 frames per second. The first
100 frames from each sequence are first encoded using a WZ codec with fixed GOP
sizes ranging from 1 to 5. For the case where the GOP size is 1, all frames are
H.264 intracoded, whereas for the other cases, only the first frame (key frame)
from each GOP is H.264 intracoded, while the remaining ones are WZ-coded. H.264 coding is performed using
JM FRExt reference software, version 13.2, with baseline profile. The results
are
then compared with the case where a WZ
codec with a dynamically varying GOP size is used. The GOP size is determined
using our proposed algorithms as explained in Section3, with Smax set to 5.In Figure3, we show the rate and PSNR variations along the Grandmother
sequence for M=4, and the quantization
parameter of the H.264 intraframes QP=25, obtained using a WZ codec with
a fixed GOP size set to 3, with and without
a feedback channel (FC). It can be noticed that the rate estimated without FC
exceeds the rate obtained using FC most of the time. As a result, WZ frames are
correctly decoded in both cases and the
reconstructed output is the same. However, in rare situations (e.g., in frame
41), the encoder underestimates the rate needed for correctly decoding a WZ
frame, which yields a degraded quality at the decoder output. For an average
bit rate of 697 kbps obtained with the feedback channel, an average rate excess
of 50 kbps is observed in the case where the feedback channel is  suppressed. In
other words, for applications where the feedback channel is not suitable (e.g.,
in video broadcasting), the cost to be paid (in terms of bit rate) for the
suppression of the return channel is approximately 7%.Rate
(a) and PSNR (b) variations along the Grandmother sequence using a WZ
codec with a GOPsize=3, M=4, and QP=25.
(a)(b)In Figure4, we show the average
R-D curves obtained for the three sequences using both the initial (I) and
simplified (S) algorithms. The rate and PSNR are averaged over all the sequence
(key and WZ frames). Different rate points are obtained by varying the
quantization parameter M for the WZ frames. As for the quantization parameter
(QP) of the H.264 intraframes, it is chosen in such a way to permit a
near-constant decoding quality in the output video sequence, using the approach
presented in ‎[17]. It can be
clearly seen that, for the Salesman and Foreman sequences, both curves overlap
(both algorithms have similar performance), whereas a negligible loss that does
not exceed 0.45 dB is observed with the Grandmother by using the simplified algorithm.Figure 4
AverageR-D curves obtained using the initial (I) and simplified (S) adaptive GOP size
control algorithms.Figures5 to 7 show the average
R-D performance for the Foreman, Grandmother, and Salesman sequences,
respectively, obtained with the initial (nonsimplified) algorithm. In Figure 5
(Foreman), we notice that for the case of a fixed GOP size, the performance
decreases as the GOP size increases. The best performance is thus obtained when
all frames are intracoded. This is due to the high motion in this sequence,
which yields less accurate side information when the key frames are further
apart. A similar effect has been noticed in ‎[16] where key frames were encoded using an H.263+ video
codec. However, when the GOP size is dynamically varied along the sequence, a
gain of 10 to 12 kbps is obtained compared to H.264 intracoding. For sequences
with lower motion levels, different results are observed. It can be seen in Figures
6 and 7 that the best system performance is obtained with a GOP of size 3 (for
the fixed-GOP case). Our proposed system outperforms both H.264 intracoding and
fixed-GOP WZ coding in most cases. For example, for the Grandmother sequence at
520 kbps, a gain of 3 dB is observed with respect to the H.264 intracodec and
0.8 dB with respect to the WZ codec with a GOP size of 3. Similarly, for the
Salesman sequence at 580 kbps, our proposed algorithm outperforms the H.264
intra codec and the WZ codec with a GOP size of 3 by 1 dB and 0.1 dB,
respectively. However, a performance loss of 0.4 dB can be observed with the
Salesman sequence at 900 kbps using a dynamic GOP size, compared to the
fixed-GOP (size=3) WZ codec. In fact, this is due to a significant mismatch
between the side information available at the encoder (estimated using average
interpolation) and the one available at the decoder (obtained by
motion-compensated interpolation).Figure 5
AverageR-D curves for the Foreman sequence using a WZ codec with fixed and variable
GOP sizes.Figure 6
AverageR-D curves for the Grandmother sequence using a WZ codec with
fixed and variable GOP sizes.Figure 7
AverageR-D curves for the Salesman sequence using a WZ codec with fixed and variable
GOP sizes.Figures8 and 9 show the
rate and PSNR variations along the Salesman sequence for M=4 and M=2,
respectively, for the case where the rate estimation is done at the encoder
using average-interpolated side information, and for the case where this
estimation is performed at the decoder using
the side information obtained by motion-compensated interpolation. The
source coding rate is the one estimated by the encoder, whereas the real PSNR
is the one obtained after the decoding process and thus, the corresponding
curves are shown as solid lines. On the other hand, decoder-estimated bit rate
and encoder-estimated PSNR (dotted curves) are shown only to analyze the
system’s behavior at both (encoder and decoder) sides. It can be seen that, in
some regions (e.g., frames 61 to 64 and frames 71 to 74 with M=4), the encoder
underestimates the rate necessary for the decoding of WZ frames, which yields a
very high bit error rate at the turbo decoder output. As a result, the
reconstruction function of the WZ codec cannot yield a reliable output and
thus, a significant performance loss is observed in these regions, which
greatly affects the average system performance as shown in Figure 7. However,
such estimation errors rarely occur. As it can be clearly seen in Figures 8 and
9, the encoder estimations are accurate most of the time when M=4, and all the
time when M=2. Similar results were observed with the other sequences for
different values of M.Rate
(a) and PSNR (b) variations along the Salesman sequence using a WZ codec
with the proposed GOP size control algorithm forM=4.
(a)(b)Rate
(a) and PSNR (b) variations along the Salesman sequence using a WZ codec
with the proposed GOP size control algorithm forM=2.
(a)(b)Table2 shows the percentage of
GOP sizes obtained for each of the sequences using the proposed adaptive GOP
size control algorithm (nonsimplified), for different values of the WZ
quantization parameter M. For the Foreman sequence, when M=1, 100% of the GOPs
are of size 1. In other words, the system switches to H.264 intracoding mode
all the time, and no frame in the sequence is
WZ-encoded. For M=2 and 4, most of the
GOPs are of size 1, while the maximum GOP size does not exceed 3. This explains the  reason behind the
similar performance between the H.264 and our proposed WZ codec for the Foreman
sequence, as shown in Figure 5. More GOP size variations can be noticed with
the two other sequences. For the Grandmother sequence with M=1 and M=2, the
system is always in WZ coding mode. In other words, not any GOP is of size 1
and only key frames are intracoded, since the WZ coding outperforms
H.264 intracoding in this case, according to the encoder estimations.Table 2
Percentage of GOP sizes used in each sequence.
Gop size12345ForemanM=11000000M=291.16.72.200M=487.410.32.300GrandmotherM=104203640M=2%04.216.737.541.6M=423.133.320.52.620.5SalesmanM=13.722.218.5055.6M=218.212.139.4030.3M=422.27.43.7066.7The Foreman sequence is characterized by very high motion levels,
especially in its second half. In such regions with high motion, H.264
intracoding usually outperforms WZ coding. For this reason, in order to analyze
our system’s performance with high motion video, we encoded the complete
Foreman sequence (400 frames) and the results are reported in Figure10. This figure shows the GOP size variations along the sequence for M=2 and M=4. Long runs of consecutive
GOP sizes equal to 1 can be noticed in high motion areas, which indicate that
the system has switched to H.264 intracoding mode in these regions. As a
result, the R-D performance for the Foreman sequence is slightly better than the one obtained with a pure H.264 intracoder, as was also noticed in Figure 5.GOP
size variations along the complete Foreman sequence
(400 frames) forM=2 (a) and M=4 (b).
(a)(b)


## 6. Conclusion and Future Work

This paper presents simple
algorithms that dynamically adapt the GOP size for a distributed Wyner-Ziv
video codec, depending on the content of the video scene to be encoded. Based
on H.264 intracoding for key frames, the system relies on theoretical calculations
to estimate the bitrate necessary to successfully decode Wyner-Ziv frames
without the need for a feedback channel, which makes it suitable for
broadcasting applications. Automatic mode selection allows the system to switch
between H.264 intracoding and WZ coding modes in order to optimize the overall
system performance. Simulation results show an average gain that can reach 3 dB
compared to an H.264 intracodec and 0.8 dB compared to a WZ codec with a fixed
GOP size.As a future work, authors will
focus on taking into account the transmission channel conditions in the GOP
size control algorithm, and implementing the system in a more realistic network
environment with multiple users, based on their previous research in [14, 15]. Further
research may consider dynamically varying the quantization parameters (QP for
key frames and M for WZ frames) and using advanced interpolation techniques to
improve the overall performance.



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*Source: 319021-2009-01-12.xml*