程序代写代做代考 scheme data mining flex algorithm Java decision tree javascript KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 6, Jun. 2016 3286
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 6, Jun. 2016 3286
Copyright ⓒ2016 KSII
This work is supported in part by National Basic Research Program of China (No.2012CB316400), National
Natural Science Foundation of China (No. 61210006, 61402034), the Program for Changjiang Scholars,
Innovative Research Team in University under Grant IRT201206, Beijing Natural Science Foundation(4154082)
and CCF-Tencent Open Fund.
http://dx.doi.org/10.3837/tiis.2016.07.023 ISSN : 1976-7277
Fast Algorithm for Intra Prediction of HEVC
Using Adaptive Decision Trees
Xing Zheng, Yao Zhao, Huihui Bai, Chunyu Lin
Institute of Information Science, Beijing Jiaotong University
Beijing, 100044 – China
[e-mail: yzhao@bjtu.edu.cn]
*Corresponding author: Yao Zhao
Received February 10, 2016; revised May 15, 2016; accepted May 22, 2016; published July 31, 2016
Abstract
High Efficiency Video Coding (HEVC) Standard, as the latest coding standard, introduces
satisfying compression structures with respect to its predecessor Advanced Video Coding
(H.264/AVC). The new coding standard can offer improved encoding performance
compared with H.264/AVC. However, it also leads to enormous computational complexity
that makes it considerably difficult to be implemented in real time application. In this paper,
based on machine learning, a fast partitioning method is proposed, which can search for the
best splitting structures for Intra-Prediction. In view of the video texture characteristics, we
choose the entropy of Gray-Scale Difference Statistics (GDS) and the minimum of Sum of
Absolute Transformed Difference (SATD) as two important features, which can make a
balance between the computation complexity and classification performance. According to
the selected features, adaptive decision trees can be built for the Coding Units (CU) with
different size by offline training. Furthermore, by this way, the partition of CUs can be
resolved as a binary classification problem. Experimental results have shown that the
proposed algorithm can save over 34% encoding time on average, with a negligible
Bjontegaard Delta (BD)-rate increase.
Keywords: fast mode decisions, intra prediction, decision trees, offline training, HEVC
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 6, June 2016 3287
1. Introduction
In 2013, High Efficiency Video Coding (HEVC) standard, as the most revolutionary work,
has been launched by the joint coding standardization project of ITU-T Video Coding
Experts Group (ITU-T VCEG) and ISO/IEC Moving Picture Experts Group (ISO/IEC
MPEG) [1]. HEVC can offer an efficient solution to the strong demand of the bandwidth and
formats beyond High Definition (HD) resolution, even for the Ultra High Definition (UHD),
which have shown more and more popular in video industry.
In HEVC, a picture can be partitioned into many Coding Tree Units (CTU), corresponding
with Macro Block (MB) used in previous standards. The CTU is restricted to the square with
the size of 64×64 pixels. A CTU includes one luma Coding Tree Block (CTB) and two
chroma coding tree blocks with corresponding syntax elements. At the same time, through
recursive calculation, the CTU can be deeply partitioned into small quad-blocks called
Coding Units (CU), whose size are from 8 × 8 to 64 × 64, as shown in Fig. 1. From this
figure, we can see that there are close relations between the maximum allowed CU depth and
the encoding complexity. In other words, the greater of the depth, the consumption of
encoding time will be longer. Each CU contains more adaptive quadtree structures for the
purpose of prediction, so-called Prediction Units (PU), and of transform, so-called
Transform Units (TU). Similarly, each Coding Block (CB) can also be split into Prediction
Blocks (PB) and Transform Blocks (TB). The main goal of these structures is to adapt the
content of the video, so this variable-size standard is particularly suited to large resolution.
The PU is the basic unit used for prediction process in a rectangular shape. One PU can be
encoded with one of the modes in candidate sets, which is similar to MB mode of
H.264/AVC in spirit. However, the size of PU during the intra prediction can vary from 44,
8 8, 16 16, 32 32 to 64 64. Each size of PU also contains up to 33 directional
prediction modes, one DC prediction mode and one planar mode. Therefore, aiming to the
specific size of CU, the encoder has to evaluate the rate-distortion (R-D) cost for 35 times,
respectively for the 35 prediction modes. Furthermore, the encoder also searches the optimal
partitions of CUs in a recursive manner, which has posed great challenges on real-time
applications, especially HD and UHD video formats. In order to reduce the huge complexity
for the intra-picture prediction, a fast intra-prediction algorithm has been implemented in the
HEVC Test Model (HM). For each of 35 possible prediction modes, a low complexity cost
function is always computed using the Sum of Absolute Transformed Differences (SATD) as
follows:
p r e d p r e dH A DL S A T D B i t s (1)
where
HAD
L is the Lagrange cost function, pred is the Lagrangian multiplier and predBits
stands for the number of bits of the prediction mode. Since
HAD
L does not require the
implementation of the full encoding and decoding processes, it can speed up the intra
prediction to some extent.
The reason why the quad-tree structures [2] are adopted in HEVC is that the encoder can
traverse all possible combinations of CU, PU and TU, through RD cost calculation, as with
Advanced Video Coding (H.264/AVC), to find the optimal combination for the specific
CTU. Therefore, the eventual result of this operation can effectively deal with different
3288 Zheng et al.: Fast Algorithm for Intra Prediction of HEVC
regional characteristics of natural image. For example, in the flat zone, the optimal size of
the CU may be 64×64 pixels; however, in a region with complex movements, the optimal
size of the CU may be split into 8×8 pixels. While such flexibility leads tomore efficient
compression, it also increases encoder complexity dramatically.
Fig. 1. CU partition based on quad-tree structure
2. Related Work
However, with these new tools included in HEVC, consequently, the overall encoding time
is larger than before, which mostly wasted in Rate-Distortion Optimization (RDO) process
[3]. Thus, it is necessary to find a novel method to reduce the coding complexity of the
intra-frame prediction, a lot of works have been proposed to explore fast algorithms and
useful models in the state of encoding for HEVC. Most of the valid algorithms may commit
to find the potential links between the CU splitting and CU characteristic. In [4], a fast CU
decision is presented using the correlation between the optimal CU depth level and the video
content. In [5], Chen has shown a fast intra algorithm based on pixel gradient statistics which
employed through the analysis of the video content. In [6], a fast intra-frame prediction
algorithm is presented using a Rate-Distortion estimation based on Hadamard Transform
(HT). In [7], a fast bottom up pruning algorithm is proposed to reduce the computational cost.
In [8], a method based on entropy of CU level is also presented. Generally speaking, the key
of these methods is to find out the correlation between the video content and the optimal
partition of CU. Through this potential link, we can make precise prediction that aiming to
background or flat regions, some specific CU depth levels can be skipped, which can reduce
most of the encoding time. In recent years, another popular method which reduces the
computational complexity for the encoder is to use parallel computing enabled by many-core
processors. In [9], Yan et. al. have proposed a parallel framework to decide the optimal
coding unit tree for each image block. Similarly, in [10], to reduce the time of motion
estimation (ME) procedure, a parallel framework to decouple ME for different partitions on
many-core processors has also been proposed. These type of methods can be more effective
for inter prediction conmpared to intra prediction.
However, few approaches which apply machine learning have been introduced. In [11],
Shen proposed a CU size selection algorithm by trying to predict the CU size based on
Support Vector Machine (SVM) prediction model. In [12], a fast method aimed to
Inter-Prediction is proposed by extracting some typical features related to SKIP mode or
Inter 2Nx2N mode. In addition, in [13], we can also learn that machine learning can be
used in video transcoding to reduce the encoding time, just as the expected results.
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In this paper, to avoid to perform an exhaustive CU size evaluation which used in
traditional HM encoder, our approach introduces a data mining classifier generated by
machine learning. In order to make a balance of the computation complexity and
classification performance, we choose the entropy of Gray-Scale Difference Statistics (GDS)
and the minimum of SATD [14] as two important features to design the classifier. For the
next stage, these selected features can be applied to decision trees algorithm for the CUs with
different size. Through these trees, we can make fairly exact CTU partitioning decisions
compared to other works.
This paper is organized as follows. In Section 2, fast CU size decision algorithm based on
decision trees is presented. Section 3 shows experimental results and make detailed analysis.
The conclusion is summarized in Section 4.
3. Proposed Algorithm
In this part, the fast CU partition method will be presented in detail. The task of our method
should find a tradeoff between the saving time and rate distortion performance. Therefore,
the partition structures used our method should be consistent with the RDO partition
structures used in original HM reference software as much as possible. Our algorithm can be
primarily divided into three steps. In the first step, fundamentally, the training sample sets
should be selected properly, whose coding units can contain a succession of different texture.
For example, in these CUs of the sample sets, not only have the smooth areas, but have the
regions that possess more movement information. The reasonable training sample sets can
greatly improve the accuracy of decision trees for the next step. In the second step, the key of
the task is to find two useful features (entropy on account of GDS, the minimum value of
SATD after intra prediction) trained by the data mining tool, Waikato Environment for
Knowledge Analysis (WEKA) [15]. The selected features should be closely related to
decision tree classification. In the final step, by extracting intermediate variable, collected
data should be preprocessed and divided into three categories which are corresponding to the
CU size of 64×64 pixels, 32×32 pixels and 16×16 pixels, respectively. Consequently, three
decision trees will be generated, and the accuracy of these trees can also be measured.
2.1 Training Sample Set
Table 1. Training Sequences
Sequence Frame Rate Bit Depth Resolution
BlowingBubbles 50 8 416×240
BQSquare 60 8 416×240
PartyScene 50 8 832×480
BQMall 30 8 832×480
KristenAndSara 60 8 1280×720
FourPeople 60 8 1280×720
BasketballDrive 60 8 1920×1280
Cactus 24 8 1920×1280
Traffic 30 8 2560×1600
Because this positive method is obviously different from original ones, it is noted that the
training sample sets should cover a very wide range of content complexity for the region of
3290 Zheng et al.: Fast Algorithm for Intra Prediction of HEVC
CUs as much as possible. In order to achieve this purpose, we have selected the first 30
frames of a collection of different resolution video sequences belonged to JCT-VC test
sequences [16] as the training sample sets. In our experiment, nine standard video sequences
listed in Table 1 have been used to construct the decision trees of the proposed scheme,
because they represent different visual content, motion, and resolution.
2.2 Feature Selection
Because this method involves the classification problem, generally speaking, the features
used in classification process can have a strong correlation with the partition of CU for the
final classifier. Otherwise, the performance of classifier would be unacceptable, what is more,
the accuracy of the decision trees will be greatly reduced.
The attributes which can describe CU’s content traits are enough, such as edge
information, shape type, texture complication and other motion information and so on.
However, in consideration of making fast decisions for the split process of CU, we have to
consider the computation complexity for a certain kind of feature, so the best deal is to find
the balance between the complexity and the well-behaved characteristic for classification
problem.
To achieve more previse judgment, a good deal of statistics have already been computed,
such as the entropy of CU blocks, the mean of CU blocks, the variance of the CU blocks, the
entropy of GDS of CU blocks, and the prediction residual between the original block and the
reconstruction block, that is the minimum value of SATD.
Giving attention to both performance and complexity, we observed that the combination
of the entropy of GDS and the minimum value of SATD can work better for making a
decision whether a CU has to be split or not to be split using the decision trees which have
already trained.
2.2.1 The Minimum Value of SATD
In HEVC, the intra prediction is applied to get rid of the spatial redundancies in the video
frame. HEVC provides 35 prediction modes for different size of PUs instead of only 9
prediction modes being available for luma blocks in H.264/AVC. From an increasing
number of prediction modes, HEVC can better adapt to the video content especially for the
large resolution of video sequences.For each mode, HEVC will always predict the spatial
pixels using the neighbor pixels, and through this operation, the reconstruction block will be
presented for us. What is more, the metric of this prediction method is co-called SATD
which served as the valid feature in our experiment. To calculate the cost of particular mode,
the value of SATD is used where transform is Hadamard Transform. The formula for
calculation is as follows:
,
( ) , ,
A Bi j
Sum of Absolute Difference SAD s i j s i j
(2)
,
( ) ,
i j
Hadamard Transformed SAD SATD HT i j
(3)
where ( , )
A
s i j and ( , )
B
s i j denote the ( , )
th
i j sample in blocks A and B of the same
size, respectively. ( , )HT i j in (2) is the ( , )
th
i j coefficient of a block that is obtained by
applying Hadamard transform to the block difference between blocks A and B . By
traversing the residual matrix, the sum of the absolute value of each element can be
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 6, June 2016 3291
computed, which is uesd to the value of SATD. For a flat region, the accuracy of prediction
can be higher, so the value of SATD can be smaller. On the contrary, for the area that
contains complex motion information, the value of SATD seems to be a little larger.
Therefore, to some extent, the values of the SATD can better reflect the video content
complexity.The more important reason why we choose the SATD is that the minimum value
of SATD can be gained easily in HM reference software. Taking the analysis into
consideration, we choose the value of SATD as the input property for the decision trees.
Fig. 2. The CU’s SATD values at different depth
3292 Zheng et al.: Fast Algorithm for Intra Prediction of HEVC
To further confirm the feature of SATD valueshave a close relationship with the split
process of CU optimal depth level, we encode the first 20 frames of the standard test video
sequence BQTerrace and recode the minimum value of SATD in HM 10.1. These SATD
values will be split into two classes. One class contains the data for the split of CUs and the
other class contains the data for the non-split of CUs. Each of class also includes three types
of data, corresponding to the CU size of 64×64, 32×32 and 16×16. We select one thousand
CU blocks that will be split and the same number of CU blocks that will be non-split. The
distribution of these values are shown in Fig. 2, where the blue dots on behalf of the CUs
that will be split, to the contrary, the red dots on behalf of the CUs that won’t be split further.
From the above figures, we can see clearly that the CUs corresponding with the smaller
values of SATD have the smaller possibility to be split, inversely, those CUs with the bigger
values of SATD have the higher possibility chance to be split. Therefore, these experiments
are conducted to demonstrate the effectiveness of the value of SATD which is helpful to the
training of the decision trees.
2.2.2 The Entropy of GDS
In the proposed algorithm, the entropy based on GDS plays an important role in the
classification problem. The GDS method [17] is suggested in an attempt to define texture
measures correlated with human perception. The gray-tone differences for each pixel can be
calculated as follows:
( , ) ( , ) ( , )g x y g x y g x x y y
(4)
where ( , )g x y is pixel values at the point of ( , )x y aiming to the specific range of the CUs,
and x represents the offset value relative to the horizontal position of the x , similarly,
y represents the offset value relative to the vertical position of the y , in the process of
our statistical results shows that the performance is better when the 1 x and 1 y , so
the values of ( , )
g x y represents the differences of the pixels.
It is assumed that there are M possible values of ( , )
g x y so when we can change the
values of x and y in the whole CU region, the frequency of a particular value for
( , )
g x y can be stored. Through these stored data, we can draw two-dimension histogram of
the value of ( , )
g x y . Then we can obtain the corresponding probability while ( , )
g x y is
assigned different values. Different useful parameters of image features can be worked out
from the histogram, which can be used to quantitatively describe the first-order statistical
properties of the CU region. A large number of features can be calculated using the GDS
method for the purpose of texture discrimination, such as the contrast, second-order
moments, entropy and mean. In view of low computation complexity, we choose the entropy
as the second feature for the training of decision trees.
It is well known that the entropy is a very effective measure to estimate the complexity of
the video texture. If the region of the CU is more smooth, the value of the entropy can be
smaller; otherwise, if the region of the CU is more complicated, then the value of the entropy
can be larger. The equation for the entropy is expressed as follows:
ji
i
ii ppxH
0
2log)( (5)
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 6, June 2016 3293
where ( )H x is the entropy corresponding to the CU region,
i
p presents the probability of
the element i , and j is the number of elements. By iterating through all the possible
pixels in the CUs, the final ( )H x can be calculated and stored as an attribute of an
instances used in decision trees in the training phrase.
The Fig. 3 shows the precision of the split for different size of CUs using the entropy of
GDS and the conventional entropy of pixel-levels.
Fig. 3. The precision of the splitting for different CU size using two types of entropy
From Fig. 3, we can see that the methods using different kinds of entropy have different
performance of classification for the split process of the CUs. The method using the entropy
of GDS has shown the better performance of classification for the different size of CUs, so
the method will be adopted in our algorithm.
2.3 Decision Tree
In this paper, the tool applied to guide the data mining process is the WEKA with 3.6.12
version. The machine learning contains many approaches, and most of them have already
been realized in WEKA.The file format for WEKA is an Attribute-Relation File Format
(ARFF). In the specific case of building decision trees, the last line of this format file can
identify the class attribute, which in our experiment indicates that whether the CU can be
split or not be split.
Furthermore, we choose C.4.5 classifer in WEKA for its good performance. For C.4.5
classifer, the input is the ARFF file and the output is the well-trained decision trees. When
building the decision trees using the C.4.5 algorithm for the CU early termination, the
importance of each attribute can be evaluated through the Information Gain Attribute
Evaluation (IGAE) which be used to classify the data into the different classes in WEKA.
This indicator will employ the Kullback–Leibler divergence (KLD) as the only metric to
choose the most valuable attribute. So, the information gain of a feature shows that how
important it is for the process of training a decision tree aiming to the different size of CUs.
As shown in Fig. 4, the decision tree contains two parts, nodes and arcs. The nodes
represent tests performed on the attributes and the arcs are the prediction results of certain
tests. In our scheme, the combination of two features can be seen as an instance which stands
75%
80%
85%
90%
95%
100%
64×64 32×32 16×16
the precision of the split for CUs
using the conventional entropy
the precision of the split for CUs
using the entropy of GDS
3294 Zheng et al.: Fast Algorithm for Intra Prediction of HEVC
for the block of CUs. The C.4.5 algorithm will take all instances as the inputs based on the
values of KLD, and attain the thresholds in the current stage for classification. In Fig. 4, a
simple example has shown to explain the split process of the decision trees. For a given CU
block, the values of SATD and GDS entropy of this block can be calculated and then the
path can be traced from the root node to the leaf node. If the output of the leaf node is 1,
which represents split decision for the current CU block; otherwise, the current CU block
will not be split.
Fig. 4. The splitting process of the decision trees
Based on the above analysis, the characteristics of the well-trained decision trees can be
shown in Table 2. In this table, the accuracy for the different size of CUs along with their
depth, the number of the leave nodes have already be presented for us. We can see that the
decision trees can obtain the better decision accuracy with the percentage of the value can
reach more than 80%. At the same time, because the values of depth for the decision trees is
lower, so the time attached to the encoder can be accepted.
Table 2. The structures of the decision trees for training sequences in Table 1
CU Size
Decision
Accuracy
Depth
Leave
Node
64×64 83.6% 6 22
32×32 81.7% 8 28
16×16 80.2% 8 36
4. Simulation Results
In the proposed algorithm, the training data sets include a half of CUs that will be split into
small CUs and the other half of CUs that will not be split into small CUs, which can reduce
the problem of the classification imbalance. We can get the related training data for the
features about the minimum value of SATD and the entropy of the gray difference statistics,
through collecting the intermediate variables during the encoding procedure of the nine
video sequence sets in Table 1, using four quantization parameters (QP) values
(22,27,32,37). These data will be used to train the decision trees for prediction.
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 6, June 2016 3295
Furthermore,the rest of the nine video sequences sets, as shown in Table 3, can be used to
validate the performance of the decision trees. What calls for special attention is that the nine
sequences used in the training stage should not be included in the testing stage. The metric of
the proposed algorithm is measured by PSNR difference PSNR , bit rate difference
Bitrate and time saving T . We encode the up to 100 frames for each test sequences in
Table 3. The experiment condition is set up as “All Intra-Main” (All-Main) configuration
based on HM10.1.
Table 3. Testing Sequences
Sequence Frame Rate Bit Resolution
BasketballPass 50 8 416×240
RaceHorses 30 8 416×240
BasketballDrill 50 8 832×480
RaceHorses 60 8 832×480
Johnny 60 8 1280×720
BQTerrace 50 8 1920×1280
Kimono 24 8 1920×1280
ParkScene 50 8 1920×1280
PeopleOnStreet 30 8 2560×1600
Table 4. Performance comparison between Huang’s and the proposed algorithm
Sequence
Proposed Huang’s
BD
-Rate
(%)
(%)
BD
-Rate
(%)
(%)
Class A
2560×1680
PeopleOnStreet 1.71 29.95 0.40 19.31
Class B
1920×1080
Kimono
BQTerrace
ParkScene
1.77 56.53 0.79 7.25
2.18 30.52 0.32 20.39
1.71 33.78 0.73 19.37
Class C
832×480
RaceHorses
BasketballDrill
1.90 30.64 0.38 23.19
1.52 22.25 0.43 24.44
Class D
416×240
BasketballPass
RaceHorses
2.08 32.64 0.37 18.70
0.27 14.20 0.20 24.53
Class E
1280×720
Johnny 4.83 59.58 0.89 14.43
Average 1.994 34.45 0.501 19.06
Table 4 shows the final results of our algorithm with Bitrate , PSNR and T .
Furthermore, affected by different QPs, the mean of T (noted by T ) takes the place of
T , which can be calculated by the following equation.
3296 Zheng et al.: Fast Algorithm for Intra Prediction of HEVC
%100
4
1
T T (6)
with
10.1
10.1
T
HM Proposed
HM
Time Time
Time
(7)
In Table 4, we can see that the proposed fast CU partition algorithm can achieve about
34.45% reduction of the total encoding complexity, and the Bjontegaard delta (BD) rate
exhibit 1.994% increment on average. Moreover, compared with Huang’s method in [7], the
proposed algorithm can save more encoding time with a tolerable bitrate increase.
(a)
(b)
Fig. 5. The CU Partition Structures for RaceHorses (a) The RDO algorithm in HM10.1; (b)
Proposed algorithm
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 6, June 2016 3297
(a) Kimono
(b) PeopleOnStreet
Fig. 6. RD Curves of HM10.1 and our proposed algorithm
Fig. 5 shows the partition comparison between the proposed and anchor RDO algorithm in
HM10.1 for the sequences RaceHorses. In the figures, white lines can be used for the same
partition while red lines for different partition. From Fig. 5, we can observe that most of the
CUs with 64×64 pixels have almost been split, even if in some flat regions, in other words,
the output of the decision tree of CU with 64×64 pixels is relatively accurate. The similar
performance of the decision trees appears in CUs with size of 32×32 pixels and 16×16 pixels.
In addition, Fig. 6 has displayed the rate distortion performance of the proposed algorithm
and RDO algorithm in HEVC for the specific sequence Kimono and PeopleOnStreet. We
can clearly see that the proposed algorithm can maintain the rate distortion performance for
Y component compared with the original RDO algorithm in HEVC.
3298 Zheng et al.: Fast Algorithm for Intra Prediction of HEVC
5. Conclusion
We present a fast CU partitioning approach for HEVC Intra-Prediction using machine
learning. Considering the balance between the performance and complexity, entropy of the
GDS and the minimum value of SATD are selected as features of the CU blocks. According
to these selected features, three decision trees can be constructed to predict the early
termination of CU partition process.. The experimental results indicated that the proposed
algorithm can achieve over 34% encoding time reducing on average with negligible coding
efficiency loss.
References
[1] B. Bross, W. J. Han, J. R. Ohm, G. J. Sullivan and T. Wiegand, “High Efficiency Video Coding
(HEVC) text specification draft 10 (JCTVC-L1003),” in Proc. of JCT-VC Meeting (Joint
Collaborative Team of ISO/IEC MPEG & ITU-T VCEG), Geneva, Switzerland, January 14-23,
2013.
[2] W. J. Han, J. Min, I. K. Kim, E. Alshina, A. Alshin, T. Lee, J. Chen, V. Seregin, S. Lee, Y. M.
Hong, M. S. Cheon, N. Shlyakhov, K. McCann, T. Davies and J. H. Park, “Improved Video
Compression Efficiency Through Flexible Unit Representation and Corresponding Extension of
Coding Tools,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no.
12, pp. 1709-1720, December, 2010. Article (CrossRef Link).
[3] X. Li, M. Wien, and J. R. Ohm, “Rate-complexity distortion optimization for hybrid video
coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 21, no. 7, pp.
957–970, July, 2011. Article (CrossRef Link).
[4] L. Shen, Z. Zhang and P. An, “Fast CU size decision and mode decision algorithm for HEVC
intra coding,” IEEE Transactions on Consumer Electronics, vol. 59, no. 1, pp. 207-213, February,
2013. Article (CrossRef Link).
[5] G. Chen, Z. Pei, L. Sun, Z. Liu and T. Ikenaga, “Fast intra prediction for HEVC based on pixel
gradient statistics and mode refinement,” in Proc. of IEEE China Summit & International
Conference on Signal and Information Processing (ChinaSIP), pp. 514-517, July 6-10, 2013.
Article (CrossRef Link).
[6] Y. Kim, D. Jun, S. H. Jung, J. S. Choi and J. Kim, “A Fast Intra-Prediction Method in HEVC
Using Rate-Distortion Estimation Based on Hadamard Transform,” ETRI Journal, vol. 35, no. 2,
pp. 270-280, April, 2013. Article (CrossRef Link).
[7] H. Huang, Y. Zhao, C. Lin, and H. Bai, “Fast bottom-up pruning for HEVC intra frame coding,”
in Proc. of Visual Communications and Image Processing (VCIP), pp. 1–5, November 17-20,
2013. Article (CrossRef Link).
[8] M. Zhang, J. Qu, and H. Bai, “Entropy-Based Fast Largest Coding Unit Partition Algorithm in
High-Efficiency Video Coding,” Entropy, vol. 15, no. 6, pp. 2277-2287, June, 2013.
Article (CrossRef Link).
[9] C. Yan, Y. Zhang, J. Xu, F. Dai, L. Li, Q. Dai and F. Wu, “A Highly Parallel Framework for
HEVC Coding Unit Partitioning Tree Decision on Many-core Processors,” IEEE Signal
Processing Letters, vol. 21, no. 5, pp. 573-576, May, 2014. Article (CrossRef Link).
[10] C. Yan, Y. Zhang, J. Xu, F. Dai, J. Zhang, Q. Dai and F. Wu, “Efficient Parallel Framework for
HEVC Motion Estimation on Many-Core Processors,” IEEE Transactions on Circuits and
Systems for Video Technology, vol. 24, no. 12, pp. 2077-2089, December, 2014.
Article (CrossRef Link).
[11] X. Shen and L. Yu, “CU splitting early termination based on weighted SVM,” EURASIP Journal
on Image and Video Processing, vol. 2013, no. 1, pp. 1–11, January, 2013.
Article (CrossRef Link).
[12] G. Correa, P. A. Assuncao, L. V. Agostini, and L. A. da Silva Cruz, “Fast HEVC Encoding
Decisions Using Data Mining,” IEEE Transactions on Circuits and Systems for Video
http://dx.doi.org/10.1109/TCSVT.2010.2092612
http://dx.doi.org/10.1109/TCSVT.2011.2133750
http://dx.doi.org/10.1109/TCE.2013.6490261
http://dx.doi.org/10.1109/chinasip.2013.6625393
http://dx.doi.org/10.4218/etrij.13.0112.0223
http://dx.doi.org/doi:%2010.1109/VCIP.2013.6706389
http://dx.doi.org/10.3390/e15062277
http://dx.doi.org/10.1109/LSP.2014.2310494
http://dx.doi.org/10.1109/TCSVT.2014.2335852
http://dx.doi.org/10.1186/1687-5281-2013-4
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS VOL. 10, NO. 6, June 2016 3299
Technology, vol. 25, no. 4, pp. 660-673, April, 2015. Article (CrossRef Link).
[13] G. Fernandez-Escribano, J. Bialkowski, J. Gamez, H. Kalva, P. Cuenca, L. Orozco-Barbosa and
A. Kaup, “Low-Complexity Heterogeneous Video Transcoding Using Data Mining,” IEEE
Transactions on Multimedia, vol. 10, no. 2, pp. 286-299, February, 2008.
Article (CrossRef Link).
[14] V. Sze, M. Budagavi and G. J. Sullivan, High Efficiency Video Coding (HEVC) Algorithm and
Architectures, 1st
Edition, Springer International Publishing, Switzerland, 2014.
[15] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, “The WEKA data
mining software: an update,” ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10-18,
June, 2009. Article (CrossRef Link).
[16] JCT-VC test sequences. [Online] ftp://hevc@ftp.tnt.unihannover.de/testsequences/
[17] D. Li, Y. Chen, Computer and Computing Technologies in Agriculture VIII, 1st
Edition, Springer
International Publishing, China, 2015. Article (CrossRef Link).
Xing Zheng received her B.S. degree from ShanXi University, China, in 2014. He is
currently a Master student in Beijing Jiaotong University, China. His research interests
are fast encoding of HEVC.
Yao Zhao received the BS degree from Fuzhou University, China, in 1989, and the
ME degree from Southeast University, Nanjing, China, in 1992, both from the Radio
Engineering Department, and the PhD degree from the Institute of Information Science,
Beijing Jiaotong University (BJTU), China, in 1996. He became an associate professor
at BJTU in 1998 and became a professor in 2001. From 2001 to 2002, he was a senior
research fellow with the Information and Communication Theory Group, Faculty of
Information Technology and Systems, Delft University of Technology, Delft, The
Netherlands. He is currently the director of the Institute of Information Science, BJTU.
His current research interests include image/video coding, digital watermarking and
forensics, and video analysis and understanding. He serves on the editorial boards of
several international journals, including as associate editors of IEEE Transactions on
Cybernetics, IEEE Signal Processing Letters, and an area editor of Signal Processing:
Image Communication (Elsevier), etc. He was named a distinguished young scholar by
the National Science Foundation of China in 2010, and was elected as a Chang Jiang
Scholar of Ministry of Education of China in 2013. He is a senior member of the IEEE.
http://dx.doi.org/10.1109/TCSVT.2014.2363753
http://dx.doi.org/10.1109/TMM.2007.911838
http://dx.doi.org/10.1145/1656274.1656278
ftp://hevc@ftp.tnt.unihannover.de/testsequences/
http://dx.doi.org/10.1007/978-3-319-19620-6
3300 Zheng et al.: Fast Algorithm for Intra Prediction of HEVC
Huihui Bai received her B.S. degree from Beijing Jiaotong University, China, in 2001,
and her Ph.D. degree from Beijing Jiaotong University, China, in 2008. She is currently
a professor in Beijing Jiaotong University. She has been engaged in R&D work in video
coding technologies and standards, such as HEVC, 3D video compression, multiple
description video coding (MDC), and distributed video coding (DVC).
Chunyu Lin was born in Liaoning Province, China.He received the Ph.D. degree from
Beijing Jiaotong University, Beijing, China, in 2011. From 2011 to 2012, he was a
Postdoctoral Researcher with the Multimedia Laboratory, Ghent University, Ghent,
Belgium. He is currently an Associate Professor with Beijing Jiaotong University. His
current research interests include image/video compression and robust transmission,
2Dto-3D conversion, and 3-D video processing.
