In this paper, a new bit rate estimation scheme is proposed to determine the bit rate for each subclass in an MPEG2 TS to H.264/AVC transcoder after dividing an input MPEG2 TS sequence into several subclasses. Video format transcoding in conventional IPTV and Smart TV services is a timeconsuming process since the input sequence should be fully transcoded several times with different bitrates to decide the bitrate suitable for a service. The proposed scheme can automatically decide the bitrate for the transcoded video sequence in those services which can be stored on a video streaming server as small as possible without losing any subject quality loss. In the proposed scheme, an input sequence to the transcoder is subclassified by hierarchical clustering using a parameter value extracted from each frame. The candidate frames of each subclass are used to estimate the bit rate using a statistical analysis and a mathematical model. Experimental results show that the proposed scheme reduces the bit rate by, on an average approximately 52% in lowcomplexity video and 6% in highcomplexity video with negligible degradation in subjective quality.
1. Introduction
B
roadcasting and communications convergence services use limited networks to deliver IPTV, Smart TV, and other Internet services to consumers. The compression technology of serviced video is applied according to two business models: Managed Network and Open Internet. IPTV service providers deliver the H.264/AVC video content through the Managed Network. In the Open Internet, the service is delivered over the public Internet and should enable access to video content not only from TV sets but also from other home devices, such as portable multimedia players and laptop computers. The scalable video coding (SVC) technology enables the system to consider the available bandwidth for other devices. The fully implemented SVC, however, also comes with some increase in complexity and bit rate for the same fidelity as compared with singlelayer coding
[1]
. A further study is needed on how to best control the SVC rate according to the network resource availability
[2]
. Most IPTV services are focused on delivering highresolution/highquality video over the Managed Network, with supporting quality of service (QoS).
The MPEG2 standard has been widely deployed in video distribution infrastructures, such as cable and satellite networks, as well as in several consumer applications, such as DVDs and DVRs. The H.264/AVC standard is used in many video streaming services limited by the network bandwidth and offers a significant reduction in the bit rate over earlier standardsbased technologies such as MPEG2 (65%) and MPEG4 (4050%)
[3]
[4]
. The standard achieves better performance in terms of both the peak signal to noise ratio (PSNR) and visual quality at the same bit rate as compared with prior video coding standards.
In video streaming services with IPTV and Smart TV, a video transcoder is necessary to leverage the compression efficiency offered by H.264/AVC with broadcast quality content produced in the MPEG2 format. To service video content over the Managed Network for users,
Fig. 1
shows the process used for video content transmission.
Video content transcoding process in an IPTV service.
In the transcoder, the input video is decoded by MPEG2 and reencoded by H.264/AVC at a fixed bit rate. After performing the validation of subjective quality, the video content is stored on a video streaming server and then serviced to users with varied, engaging content via a streaming server
[5]
. The encoded video content is usually delivered through constant bit rate (CBR) channels. The bit rate channels needed for SDTV and HDTV video can be as high as 2–3Mbps and 10–12Mbps, respectively. Each item of video content on a CBR channel does not take into account the content’s characteristics because it is encoded by two different fixed bit rates; however, the serviced video content varies from lowcomplexity video to highcomplexity video. The former can be encoded with a bit rate less than the fixed bit rate, without degradation in subjective quality. In other words, the conventional scheme based on a fixed bit rate causes bandwidth loss and requires a huge amount of storage space on a streaming server. When the open IPTV service is activated later, IPTV service providers can deliver the content, which, unlike specific companies’ customized content, is a network resource that anyone can access. In order to deliver a considerable amount of content on a CBR channel, it is important to select an efficient bit rate.
Solving this problem requires a scheme capable of finding an appropriate bit rate for video content while maintaining a subjective quality equivalent to that of a scheme that uses a fixed bit rate. Employing this scheme requires determining a bit rate for video content prior to encoding it. A video transcoder can provide an additional controller that can also estimate the bit rate. A simple technique to estimate the video content’s bit rate is to vary the bit rate step in the H.264/AVC encoder part of the transcoder. The visual quality should be verified at each encoding pass. Even though this method can provide an accurate bit rate, it is a very timeconsuming process. The time required to estimate the bit rate should be minimized to meet the video streaming service requirements.
In this paper, a scheme is proposed for automatically estimating the bit rate of each subclass without the repeated full encoding and subjective quality test. Using parameters, the video content is divided into several segments. To estimate the bit rate of each segment, candidate frames are extracted, which include intraframes that require a high number of bits. Finally, the bit rate of each segment is estimated by statistical analysis and a mathematical model based on a given target quality. The remainder of this paper is organized as follows. Section II explains the analysis of video content with respect to the quality and bit rate. Section III proposes a bit rate estimation scheme for unsupervised segmentation using the frame complexity of video content. Then, the experimental results and conclusions are presented in Sections IV and V, respectively.
2. Analysis of the Quality and Bit Rate of Video Content
The purpose of this analysis is to examine the human perceived quality corresponding to the bit rates of a video. The subjective quality of the H.264/AVC encoded video is evaluated, in which a lowcomplexity content category such as “lecture” is coded at bit rates from 1.0 to 2.5Mbps. The evaluation is performed using the doublestimulus continuous quality scale (DSCQS) method of ITUR Rec. BT.5007
[6]
. All the coded stimuli are rated by each of the five viewers. General conclusions were based on the quality ratings of the presented stimuli. The main idea of measuring the DSCQS score is to determine the differential mean opinion score (DMOS) between the reference encoded at 2.5Mbps and the test sequences averaged by all the viewers. A DMOS value,
dMOS
, is defined as follows:
where
MOSr
is the MOS of the reference sequence encoded at 2.5Mbps, and
MOSp
is the MOS of the test sequence encoded below 2.5Mbps. The task is to assess the degradation of the test sequence with respect to the reference sequence. If
dMOS
is near “0”, then the test sequence is similar to the reference sequence.
Fig. 2
shows the result of the average of all
dMOS
’s in a lowcomplexity video. The quality degradation determined by the video encoded bit rate was, on an average, 1.4Mbps. Therefore, the lowcomplexity video can encode a bit rate lower than 2.5Mbps, with negligible degradation of subjective quality.
Result of quality evaluation.
Further, the difference between the variable bit rate (VBR) at QP 22 and the CBR at 2.5Mbps is analyzed for the test sequence. As shown in
Fig. 3
, some video content can be encoded at a lower bit rate than at the fixed bit rate. Video content can be divided into two or three subclasses in terms of the quality of experience (QoE). It can also be delivered using more than one bit rate according to subclasses in a CBR channel.
The differential ratio between VBR and CBR.
3. Proposed Scheme
In this section, a bit rate estimation scheme is proposed that reduces the bit rate while maintaining the target quality in video streaming services limited by the network bandwidth.
Fig. 4
shows a block diagram of the proposed scheme. Given an input sequence as MPEG2 TS, the TS parser is used to gather MPEG2 video data and their data is decompressed by MPEG2 decoder. Deinterlacer performs deinterlacing interlaced video frames to progressive video frames because a common way to compress video is to interlace it. Using those parameters, the frames of video can be divided into several segments. To estimate the bit rate of each segment, candidate frames are extracted, which includes intraframes that require a large number of bits. Finally, the bit rate of each segment is estimated by statistical analysis and a mathematical model based on the target quality. The input video is reencoded by H.264/AVC at estimated bit rate. After performing the validation of subjective quality, the video content is stored on a video streaming server.
Block diagram of the proposed scheme.
The proposed scheme differs from the conventional scheme in that it employs a bit rate estimator. Because the proposed scheme does not encode full frames of video content, it is very important to determine parameters that can serve to indirectly measure a frame’s bits.
 3.1 Frame Complexity Estimation for an Intraframe
Some content complexity measurements for coding still images can be obtained without preencoding by using variance, edge, and gradient methods
[7]
. From the deviation of each macroblock (MB), the complexity can also be determined
[8]
. In the gradientbased method, the computation for calculating the gradient is low, and the output bit rate of each intraframe is highly correlated
[9]
. These properties are highly desirable for measuring the complexity of an intraframe. In addition to the gradient information, the histograms of luminance and chrominance pixel values are also very useful when combined with the gradient to represent the content complexity.
Given the arbitrary sth test sequence
Q
_{s}
, the set contains a number of groups of pictures (GOPs) specified in the order in which the intra and interframes are arranged:
where
M
is the total number of GOPs, and
N
is the number of frames in a GOP.
Q_{s}
(
i
,
j
) denotes the
j
th frame of the
i
th GOP. Our objective is to measure the intraframe complexity in
Q
_{s}
. In order to measure the frame complexity, the complexity measurement defined in
[10]
,
FC_{intra}
, is used. The value of
FC_{intra}
for
Q_{s}
(
i
,
j
) ∈
Q
_{s}
, CC(
Q_{s}
(
i
,
j
)), can be computed by (3).
where
In (3),
Grad_{s,i}
and
SOH_{s,i}
are the gradient and the statistic, respectively, of the histogram information of the
i
th intraframe.
Y_{s,i}
(
x
,
y
) is the luminance value of pixel (
x
,
y
) in the
i
th frame.
U_{s,i}
(
x
,
y
) and
V_{s,i}
(
x
,
y
) are the corresponding chrominance values.
K_{Y}L_{Y}
,
K_{U}L_{U}
, and
K_{V}L_{V}
are the sizes of the Y, U, and Vframes in
Q_{s}
_{(i,1)}
.
HY_{s,i}
[
l
] is the histogram of the luminance level
l
, and
HU_{s,i}
[
l
] and
HV_{s,i}
[
l
] are the histograms corresponding to the chrominance level
l
.
To investigate the relationship between the actual number of encoded bits and
FC_{intra}
, various test sequences were extensively encoded using the intracoding mode under constant quantization parameters (QPs), and both the number of encoded bits and the
FC_{intra}
for each frame were recorded.
Fig. 5
shows the scatter plots of the number of bits versus FCintra at different QPs in our test content, where each dot represents a frame.
Fig. 5
also shows the accuracy of the linear approximations (as blue dotted lines) by plotting the correlation coefficient
r
, which is an indicator of how closely the approximated linear relationship represents the actual data. The value of
r
lies between 1 and 1. For the test sequences, the value of
r
between the number of bits and
FC_{intra}
is, on an average, 0.93. When the value of
r
is at or near 1, the approximated linear relationship is the most reliable. Therefore, it is clear that a linear relationship exists in our test sequences with different slopes, and (3) can be used accurately to estimate the number of bits for intraframes.
Scatter plots of the number of encoded bits versus FCintra: (a) Documentary, (b) Lecture, (c) Religion, and (d) Sports.
 3.2 Hierarchical ClusteringBased Video Subclassification
Each of the subclasses—clusters, or groups of patterns of
FC_{intra}
—has a similar number of bits. The classifier for
FC_{intra}
is designed by hierarchical clustering with Bayesian decision theory
[11]
.
Consider a sequence
T
containing
n
samples and
c
clusters. To conduct agglomerative hierarchical clustering for
FC_{intra}
, the number of initial clusters,
n
, is determined by analyzing the temporal characteristic between frames. The scaledinvariant feature transform (SIFT) is sequentially applied to detect stable frames among temporal frames
[12]
. Let
T
(
x
,
y
,
t
) be the ordinal signature of the (
x
,
y
)th block of the
t
th frame in
T
.
G_{σ}
(
x
,
y
,
t
) defines a 3×3×3 Gaussian kernel with standard deviation
σ
as follows:
A 3×3×3 differenceofGaussian (DoG) kernel
[13]
is derived by computing the difference between two Gaussian kernels as follows:
where
k
> 1 is a multiplicative factor, and
s
= 1,2,…, is the scale of the DoG kernel. Then, the DoG kernel sliding over
T
is used to generate a vector
ψ
by the convolution operation as follows:
for
t
= 1,…,
m
. If the
t
th element in
ψ
is a local extreme, it is considered to be a key frame in
T
. In this paper, the parameters are set to
σ
= 1.8,
, and
s
= 3. A sequence consists of the static subclass
ω
_{0}
and dynamic subclass
ω
_{1}
divided by distribution of
ψ
. The two subclasses are defined as follows:
where
ω
_{0}
denotes the same value between the
t
th element and (
t
1)th element in
ψ
, whereas
ω
_{1}
denotes the different value between them. The number of initial clusters
n
is decided by the intervals of successive
ω
_{0}
’s and the number of
ω
_{1}
’s.
Fig. 6
shows the number of initial clusters in a sequence.
Examples of the number of initial clusters
To show
ω
_{0}
and
ω
_{1}
for the distribution of frame variations, the lines in the figure denote 0 and 1 for
ω
_{0}
and
ω
_{1}
, respectively. The number of initial clusters in a sequence is finally 71 as shown in
Fig. 6
. Each cluster center is the average of
FC_{intra}
’s in
ω
_{0}
and an
FC_{intra}
in
ω
_{1}
, respectively. The measure of the distance between two clusters uses the Euclidean metric
[14]
.
Given two clusters, whether they are in the same subclass or not is decided by the Bayesian decision theory. This approach is based on quantifying the tradeoffs between various classification decisions using probability and the costs that accompany such decisions. It makes the assumption that the decision problem is posed in probabilistic terms and that all of the relevant probability values are known. More generally, assume that there is a prior probability
P
(
ω_{k}
) of each subclass
k
. These prior probabilities reflect prior knowledge of how likely it is that the static or dynamic subclass can be obtained before a sequence actually appears. The difference between the representative
FC_{intra}
’s in the two clusters is measured. Its value x is considered to be a random variable whose distribution depends on the class and is expressed as
p
(
x

ω_{k}
). To determine the subclass of a cluster, the following decision rule is used: decide
ω
_{0}
if
P
(
ω
_{0}

x
) >
P
(
ω
_{1}

x
); otherwise decide
ω
_{1}
. The decision rule can be expressed as follows:
Suppose that both the prior probabilities
P
(
ω_{k}
) and the conditional densities
P
(
x

ω_{k}
) are known. It is known that the joint probability density of finding a pattern that is in subclass
ω_{k}
and has feature value
x
can be written two ways:
P
(
ω_{k}
,
x
) =
P
(
ω_{k}

x
)
p
(
x
) =
P
(
x

ω_{k}
)
P
(
ω_{k}
). Bayes’ formula can be expressed as follows:
Using (9), the decision rule of (8) can be rewritten as follows:
The quantity on the left is called the likelihood ratio and is denoted by Λ(
x
)
The quantity on the righthand side of (10) is the threshold of the test and is denoted by
η
:
Thus, the Bayes criterion leads to the likelihood ratio test (LRT) shown in (13):
Owing to the goodness of fit between the actual data and the theoretical data, the distributions of
P
(
x

ω
_{0}
) and
P
(
x

ω
_{1}
) are assumed to have an approximately exponential distribution:
where
k
is 0 or 1 of each subclass
ω
, and
α_{k}
and
β_{k}
are the model’s parameters. In this paper, the prior probabilities
P
(
ω
_{0}
) and
P
(
ω
_{1}
) for test sequences are investigated as shown in
Table 1
. On an average,
P
(
ω
_{0}
) is 0.93, and
P
(
ω
_{1}
) is 0.07. The model parameter values are
α
_{0}
= 1,140,000,
β
_{0}
= 2.824,
α
_{1}
= 2,810, and
β
_{1}
= 0.390.
Prior probabilities according to test sequences
Prior probabilities according to test sequences
Using (13), it can be determined whether the given two clusters are merged or not: two clusters are merged if Λ(
x
) is greater than
η
. Finally,
c
clusters can be obtained according to
FC_{intra}
distribution, as shown in
Fig. 7
.
Relationship between FC_{intra} distribution fc and the final clusters
Although the correlation between
FC_{intra}
and the number of bits is high, the maximum
FC_{intra}
frame does not always have the maximum number of encoded bits. Thus, the candidate intraframe needs to be extracted. The candidate frame set
H
_{s}
contains intraframes, and a candidate frame in
H
_{s}
is the frame that requires more than a certain number of encoded bits.
H
_{s}
is specified in (15):
In (15),
is a candidate intraframe,
D
is the number of candidate frames,
M
is the number of intraframes,
θ
(•) is a nondecreasing mapping function from the integer set {1,…,
M
}, and
μ_{c}
is the average of
FC_{intra}
’s in each cluster. If CC(
Q_{s}
_{(i,1)}
) is greater than the contentadaptive threshold
μ_{c}
, the
i
th intraframe is extracted as
of the
c
th cluster.
 3.3 ModelBased Bit Rate Estimation
Using candidate frames with
FC_{intra}
value of each cluster, the bit rates of clusters can be estimated via statistical analysis and a mathematical model. To estimate the bit rate while maintaining the given PSNR quality, a PSNRQ model derived from the H.264/AVC quantization process
[15]
is proposed in this paper. With this model, an estimated QP is determined and is finally applied to the bit rate estimation. The relationship between the quantization step size (
Qstep
) and QP is given in (16) as follows:
where
PF
and
MF
are a postscaling and a multiplication factor, respectively, in the H.264/AVC standard, and
qbits
= 15+floor (QP/6). When uniform quantization is applied to the uniformly distributed inputs, the mean square error (
MSE
) is given by
From (16) and (17), the PSNR can be derived as
where
a
and
b
are constants obtained by linear regression
[16]
. As a result, the value of QP can be estimated as
where
PSNR_{t}
is a given target PSNR, and
QP_{e}
is an estimated QP.
Using
QP_{e}
, the number of intraframe bits is first estimated. Some parameters obtained by intraframe estimation are used to estimate the number of interframes bits in a GOP. To estimate the number of intraframe bits, a simple but effective RateQuantization (RQ) model is used. An exponential relationship between the actual number of encoded bits and QP was modeled by Zhou and his colleagues
[17]
. For simplicity, the RQ model for an intraframe is defined as:
where
R_{q}
_{,1}
(
QP_{e}
) is the number of encoded bits for the
q
th candidate intraframe at
QP_{e}
, and
α_{q}
and
β_{q}
are the model parameters. To reveal the relationship between the number of encoded bits and QP,
Fig. 8
shows several examples of curvefitting results for intraframes, with each small dot of the mathematically approximated curves representing the actual number of encoded bits of an intraframe at each QP. Because
α_{q}
and
β_{q}
can be obtained by exponential regression,
R_{q}
_{,1}
can also be calculated by (20).
RQ curves for the test sequences. (a) Music video, (b) Lecture, (c) Sports, (d) Documentary
It is difficult to directly estimate the number of interframe bits in H.264/AVC. Thus, the bit rate conversion method introduced in
[18]
is used with the value of
QP_{e}
instead of using the intraframe RQ model. The bit rate conversion is defined as
where
R_{q,j}
_{+1}
(
QPP
) is the number of encoded bits for the (
j
+1)th interframe in the
q
th GOP at
QPP
, and
G
is a GOP size. As defined in (21), this method requires encoding a GOP at a certain value of QP,
QP_{s}
, as a reference, that is,
R_{q,j}
_{+1}
(
QP_{s}
) is computed in advance. In experiments, the value of
QP_{s}
used is 26. Furthermore,
QPP
is set to
QP_{e}
+1 here because an interframe QP is an intraframe QP+1 in H.264/AVC rate control. After estimating the number of intra and interframe bits, the total number of bits for each GOP,
R_{q}
, can be estimated using (20) and (21) as follows:
The bit rate of each cluster is estimated using the GOP that is expected to have the maximum number of encoded bits among all candidate frames in each cluster. If the same bit rate between clusters is estimated, these clusters are grouped as a segment. Finally, the number of segments in a sequence is less than or equal to the number of clusters.
4. Experimental Results
The performance of the proposed scheme is evaluated with several types of IPTV content. The proposed scheme will be called classbased bit rate estimation (CBRE) hereinafter, and the conventional scheme with a fixed bit rate of 2.5 Mbps will be called fixed bit rate estimation (FBRE)
[19]
. The standard definition (SD) resolution video content is categorized into four genres: lecture, religion and documentary, drama and animation, and music video and sports. A total of 30 videos in
Table 2
are used as test sequences.
Test sequences
In our experiment, the size of GOP is 15, and its type is set to IPPP. The target PSNR is set to 42dB. The simulated results encoded by FBRE can be compared in terms of the bit rate and quality to those encoded by CBRE. In order to evaluate the bit rate reduction, ΔR is calculated as follows:
where
and
indicate the bit rates by FBRE and CBRE in the
i
th cluster, respectively.
Table 3
shows the results of bit rate reduction. CBRE can reduce the bit rate by up to 65.2% as compared with FBRE. CBRE can reduce the bit rate, on an average, by approximately 52% and 6% in low and highcomplexity video sequences, respectively. Because CBRE assigns the bit rate according to the complexity of each segment, a relatively high bit rate reduction in the lowcomplexity video class can be achieved.
Bit rate reduction ratios of CBRE
Bit rate reduction ratios of CBRE
Since the bit rate can be estimated by encoding candidate frames instead of the total frames, the computational complexity for CBRE depends on the ratio of the number of candidate frames to the total number of frames.
Fig. 9
shows these ratios in the test sequences.
Ratios of the number of candidate frames to the total number of frames in test sequences
Table 4
shows that the difference in the PSNR performance is approximately 1.2dB on an average. However, that is too small a difference to affect the subject quality degradation in test sequences as shown in
Fig. 10
, since the target bit rate is set to 40dB in (19), which makes it difficult to determine a subjective quality difference.
PSNR difference between FBRE and CBRE
PSNR difference between FBRE and CBRE
Subjective quality comparison: (a) CBRE and (b) FBRE
5. Conclusions
The transcoding bitrate decision in conventional IPTV and Smart TV services is a timeconsuming process since the input sequence should be fully transcoded several times with different bitrates to decide a suitable bitrate. This paper shows that the video bit rate in an MPEG2 TS to H.264/AVC transcoder which is an essential device in those services can be automatically decided with keeping subjective video quality. The proposed bit rate estimation scheme was organized into two modules: one was hierarchical clusteringbased subclassification and the other was statistical analysisbased bit rate estimation. The input sequence was grouped as several subclasses by hierarchical clustering using the parameter value extracted from each frame. The candidate frames of each subclass were used to estimate the bit rate using statistical analysis and mathematical model. The bit rate could be automatically estimated by encoding only the candidate frames.
The proposed scheme could reduce the fixed bit rate, on an average, by 52% in lowcomplexity video and by 6% in highcomplexity video while maintaining the subjective quality, respectively. For future work, we plan to study some practical issues for implementing the proposed scheme. Note that in real TV services, additional works need to be developed in order to simplify the proposed scheme, especially clusteringbased video subclassification. We also need to extend the results to HD test sequences.
BIO
Hye Jeong Cho received the B.S. degree in 2004 from the Department of Internet Information Engineering, Hanyang Women’s College, Seoul, Korea. In 2012, she received the joint M.S. and Ph.D. degree in electronic engineering, Kwangwoon University, Seoul, Korea. She is currently a senior engineer in AV Research and Development Laboratory, ARION Technology Inc., Gyeonggido, Korea. Her research interests include video processing, STB and IPTV video streaming services.
ChaeBong Sohn received the B.S., M.S., and Ph.D. degree in electronic engineering from Kwangwoon University, Seoul, Korea in 1993, 1995, and 2006, respectively. He is currently an associate professor in department of Electronics and Communications Engineering, Kwangwoon University, Seoul, Korea. His research interests include image compression, transcoding, digital broadcasting systems.
SeoungJun Oh was born Seoul, Korea, in 1957. He received both the B.S. and the M.S. degrees in electronic engineering from Seoul National University, Seoul, in 1980 and 1982, respectively, and the Ph.D. degree in electrical and computer engineering from Syracuse University, New York, in 1988. In 1988, he joined ETRI, Daejeon, Korea, as a senior research member. From 1990 to 1992, he was a Director of Multimedia Research Section, ETRI. Since 1992, he has been a professor of Department of Electronic Engineering, Kwangwoon University, Seoul, Korea. He has been a chairman of SC29Korea since 2001. His research interests include image and video processing, video coding, and object recognition.
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