I. Introduction
II. Proposed Approach
(1) R D cost (C U t )< ∑ i=0 3 R D cost (C U t+1 (i)).
- 1. Ratio Function
(2) R D cost (C U t−1 )≥ ∑ i=0 3 R D cost (C U t (i)) => ∑ i=0 3 R D cost ( C U t ( i ) ) R D cost ( C U t−1 ) ≤1.
(3) r i = R D cost (C U t (i)) R D cost (C U t−1 ) , where ∑ i=0 3 r i ≤1 .
- 2. Motion-Activity Information at PU Level
- 3. Local Average of RD Cost Calculation
(5) su m d wf =(av g d wf ×coun t d wf )+RD_ cost d wf ,
(6) av g d wf = su m d wf coun t d wf +1 .
- 4. Absolute Value of Motion Vector Calculation
(7) M V abs =0.5×( ∑ i=0 1 |M V x (i)|+|M V y (i)| ).
Percentage of CUs not split at the next level if a particular CU’s RD cost
Sequences | CU size | PU_WF |
0 | 1 | 2 | 3 |
Nebula (A) | 64 × 64 | 72.56 | 72.96 | 68.21 | 2.63 |
32 × 32 | 73.24 | 71.36 | 67.36 | 7.36 |
16 × 16 | 78.24 | 72.36 | 68.65 | 5.32 |
Park Scene (B) | 64 × 64 | 72.35 | 69.36 | 66.32 | 6.35 |
32 × 32 | 69.35 | 68.34 | 64.23 | 7.23 |
16 × 16 | 68.23 | 67.29 | 66.37 | 8.28 |
Party Scene (C) | 64 × 64 | 78.25 | 73.25 | 68.48 | 2.21 |
32 × 32 | 76.35 | 72.39 | 71.24 | 1.58 |
16 × 16 | 77.84 | 71.54 | 72.45 | 4.23 |
Blowing Bubbles (D) | 64 × 64 | 69.32 | 73.21 | 71.54 | 2.23 |
32 × 32 | 71.25 | 71.23 | 71.11 | 5.41 |
16 × 16 | 72.23 | 72.84 | 69.18 | 2.71 |
Percentage of CUs not split at the next level for differentMVabsvalues.
Sequence | CUs which are not split (%) |
MVabs= 0 | 1≤MVabs≤2 | 3≤MVabs≤5 | MVabs>5 |
Nebula (A) | 76.15 | 4.36 | 2.14 | 1.22 |
Park Scene (B) | 72.96 | 3.51 | 3.26 | 1.96 |
Party Scene (C) | 71.53 | 2.79 | 2.12 | 2.13 |
Blowing Bubbles (D) | 68.73 | 3.92 | 2.73 | 1.27 |
Average | 72.34 | 3.64 | 2.56 | 1.64 |
- 5. Proposed CU-Splitting Termination Algorithm
The proposed algorithm can be divided into two stages. In the first stage, only the CTU is considered while we do not have any information from higher levels. Hence, in this stage, the ratio function cannot be calculated. On the other hand, in the second stage, other higher level CUs are considered.
In this algorithm, initially we are checking PU_WF. If PU_WF is 0 (which means SKIP mode), then the decision is taken that there is no need for further splitting. On the other hand, for PU_WF values of 1 and 2, we cannot make any immediate decision. Hence, we need to check some other parameters. Since it is a two-stage algorithm, for both of the stages the parameters which are checked are different. For CTU, only
MVabs
and local average-RD cost of a suitable dimension of PU are considered for PU_WF values of 1 and 2. Otherwise, we have to check the ratio function for the non-CTU case. In this case, ratio function
MVabs
and local average-RD cost of suitable dimension of PU are checked for PU_WF values of 1 and 2. The final decision as to whether the CU will be split further it is taken based on the above mentioned checking of the parameters. The pseudocode of the proposed algorithm is given below. The PU_WF is used as an input and the output is CU_split flag.
Algorithm : CU-Splitting Termination (CST) Algorithm input: PU_WF, CU structure output: CU_split. 1: if PU_WF = 0 then 2: CU_split = 0 3: end if 4: else 5: calculate RD cost (RD) and MVabs of Current CU 6: if CTU then 7: if PU_WF= 1 then 8: if RD < avg RD cost of 2N×2N and MVabs= 0 then 9: CU_split = 0. 10: end if 11: end if 12: else if PU_WF = 2 then 13: if RD < (avg RD cost of 2N×N, avg RD cost of N×2N and avg RD cost of SKIP) and MVabs=0 then 14: CU_split = 0. 15: end if 16: end if 17: else CU_split = 1. 18: end if 19: else 20: calculate ratio function (R) 21: if PU_WF = 1 then 22: if RD < avg RD cost of 2N×2N and R<0.25 then 23: CU_split = 0. 24: end if 25: end if 26: else if PU_WF = 2 then 27: if RD < (avg RD cost of 2N×N, avg RD cost of N×2N) and MVabs=0 and R<0.25 then 28: CU_split = 0. 29: end if 30: end if 31: else CU_split = 1. 32: end else 33: end else 34: Update avg RD cost of 2N×2N, avg RD cost of 2N×N, avg RD cost of N×2N and avg RD cost of SKIP.
III. Experimental Results
- 1. Test Condition
The proposed CST algorithm has been implemented in HM reference software, version 10.0. All simulation experiments are conducted on a PC with Intel Core (TM) i7-2600K processor having 3.4 GHz clock speed and 16 GB RAM. The test conditions are set as follows:
-
1. For each test sequence, the first 100 frames are encoded with group of pictures, size 8.
-
2. The QP is set at 22, 27, 32, and 37.
-
3. All experiments are performed in random-access mode.
-
4. Fast-encoder setting and fast decision for merge RD cost are on.
-
5. Cbf fast-mode setting and early SKIP detection options are off.
-
6. The test sequences have different resolution and belong to different classes. The description of different test sequences is given inTable 5. We have performed our experiments in random-access mode. Hence, Class-E sequences are not considered.
Test sequence description.
Sequence name | Class | Dimension (pixels) | Frame rate per second (fps) | Description |
Blowing Bubbles | D | 416×240 | 50 | Medium motion with zoom out |
BQ Square | D | 416×240 | 60 | Synthetic with camera movement |
Basketball Pass | D | 416×240 | 50 | High motion with rich texture |
Race Horses | D | 416×240 | 30 | Medium motion with rich texture |
Basketball Drill | C | 832×480 | 50 | High motion |
Party Scene | C | 832×480 | 50 | Medium motion with zoom-in effect |
Race Horses C | C | 832×480 | 30 | Medium motion with rich texture |
BQ Mall | C | 832×480 | 60 | Medium motion with camera movement |
Basketball Drive | B | 1,920×1,080 | 50 | High motion with rich texture |
Cactus | B | 1,920×1,080 | 50 | Medium motion with rich texture |
Kimono | B | 1,920×1,080 | 24 | Medium motion with rich texture |
Park Scene | B | 1,920×1,080 | 24 | Medium motion with rich texture |
BQ Terrace | B | 1,920×1,080 | 60 | Medium motion with camera movement |
Traffic | A | 2,560×1,600 | 30 | Medium motion with rich texture |
People on Street | A | 2,560×1,600 | 30 | Medium motion with rich texture |
Nebula | A | 2,560×1,600 | 60 | Medium motion with rich texture and camera movement |
Steam Locomotive | A | 2,560×1,600 | 60 | Medium motion with rich texture |
- 2. Performance Evaluation in HM 10.0
First of all, we have evaluated our algorithm with HM reference software, version 10.0. In this experiment, we have checked three parameters: required time to encode the video sequence, the number of bits in the encoded bit stream, and the corresponding peak signal-to-noise ratio (PSNR) value. In
Table 6
, the time reduction of the proposed algorithm compared with the original HM 10.0 is shown. In
Table 5
, QP values are shown for each sequence and the parameter Δ
T
is calculated using the following equation:
(8) ΔT= Tim e original −Tim e proposed Tim e original ×100%,
where
Timeoriginal
is the required time to encode the video sequence in HM 10.0 under the given test condition in section III, subsection 1.
Timeproposed
is the required time after implementing the proposed CST algorithm in HM 10.0 in the same experimental environment.
Time reduction in proposed algorithm.
Sequence name | ΔT % | Avg for all QPs |
QP=22 | QP=27 | QP=32 | QP=37 |
Blowing Bubbles | −25.24 | −37.74 | −59.21 | −47.45 | −42.41 |
BQ Square | −31.23 | −40.42 | −52.05 | −59.74 | −45.87 |
Basketball Pass | −26.40 | −38.73 | −36.30 | −46.07 | −36.87 |
Race Horses | −12.95 | −17.11 | −23.48 | −34.67 | −22.05 |
Basketball Drill | −31.35 | −34.01 | −41.69 | −49.66 | −39.18 |
Party Scene | −25.53 | −35.76 | −51.21 | −45.37 | −39.47 |
Race Horses C | −18.62 | −27.98 | −37.81 | −45.56 | −32.50 |
BQ Mall | −24.82 | −31.11 | −38.38 | −45.56 | −34.97 |
Basketball Drive | −30.73 | −38.61 | −45.59 | −52.16 | −41.77 |
Cactus | −32.62 | −43.86 | −49.79 | −55.73 | −45.50 |
Kimono | −21.36 | −29.37 | −39.10 | −49.72 | −34.89 |
Park Scene | −31.45 | −42.54 | −45.32 | −52.21 | −42.88 |
BQ Terrace | −25.54 | −37.65 | −41.43 | −51.34 | −38.99 |
Traffic | −24.43 | −34.85 | −42.32 | −51.67 | −38.32 |
People on Street | −28.32 | −33.54 | −41.67 | −50.21 | −37.18 |
Nebula | −20.32 | −29.56 | −37.32 | −48.56 | −33.94 |
Steam Locomotive | −26.32 | −31.43 | −40.43 | −50.75 | −36.48 |
Average | −26.05 | −34.36 | −43.24 | −49.02 | −38.03 |
From this table, it is quite clear that our proposed algorithm gives on average a 38.03% time reduction, with a maximum value of 59.74%. For some sequences (like Race Horses), it gives a relatively low time-reduction factor, due to the presence of high motion complexity and rich texture in the sequence.
To evaluate the performance and quality degradation of the proposed algorithm, we have checked the encoded bits and PSNR. We have defined two parameters, Δ
PSNR
and Δ
Bit
, to calculate the quality degradation. These two parameters are calculated using the following equations, (9) and (10), respectively:
(9) ΔPSNR=PSN R original −PSN R proposed ,
(10) ΔBit= Bi t original −Bi t proposed Bi t original ×100%.
The performance degradation in our proposed algorithm is shown in
Table 7
. In this table, the average value of each sequence is given in the row. Also, the Bjontegaard Delta (BD) rate is shown in
Table 7
for each sequence. The BD rate includes both BD-PSNR and BD-Bitrate
[22]
.
For higher-resolution video sequences, the proposed algorithm gives a better result. To justify this, we have tested the same sequence (Race Horses) for different resolutions (416 pixels × 240 pixels and 832 pixels × 480 pixels). The results are given in
Table 6
and
Table 7
. For low resolution (416 × 240), the proposed CST algorithm produces a 22.05% decrease in time with 3.1 BD rate for Race Horses. On the other hand, for higher resolutions (832 × 480), it gives only 1.3 BD rate degradation with a 32.5% time-reduction factor.
Performance and quality degradation of proposed algorithm.
Sequence name | ΔPSNR | ΔBit % | BD Rate |
Blowing Bubbles | −0.084 | −0.199 | 2.0 |
BQ Square | −0.115 | −0.407 | 2.3 |
Basketball Pass | −0.065 | 0.222 | 1.8 |
Race Horses | −0.093 | 0.962 | 3.1 |
Basketball Drill | −0.056 | −0.017 | 1.4 |
Party Scene | −0.110 | 0.901 | 2.7 |
Race Horses C | −0.055 | −0.040 | 1.3 |
BQ Mall | −0.097 | 0.094 | 2.5 |
Basketball Drive | 0.016 | −0.201 | 0.4 |
Cactus | −0.038 | −0.430 | 1.2 |
Kimono | −0.031 | −0.365 | 0.5 |
Park Scene | −0.036 | −0.430 | 1.2 |
BQ Terrace | −0.056 | −0.018 | 1.4 |
Traffic | −0.061 | 0.224 | 1.7 |
People on Street | −0.039 | −0.510 | 1.3 |
Nebula | −0.101 | −0.410 | 2.4 |
Steam Locomotive | −0.040 | −0.520 | 1.4 |
Average | −0.064 | 0.067 | 1.68 |
The RD curves for the three sequences (Race Horses, Party Scene, and Cactus), with different resolutions for our method and the original HM software 10.0, are shown in
Fig. 6
. Our method achieved performance that is similar to the original HM encoder, especially at a low bit rate. At a high bit rate, our method suffers a small loss in quality.
RD curves: (a) Race Horses, (b) Party Scene, and (c) Cactus sequences.
- 3. Performance Comparison
We have compared the proposed CST algorithm with ECU
[3]
. Both of the algorithms are implemented in HM 10.0 under the same test conditions as discussed in section III, subsection 1. In both of the algorithms, we have compared PSNR, number of bits, and time consumption. The parameters which are considered in this comparison are calculated using equations (11)–(13). In
Table 8
, the performance comparison is given. Form this result, it is inferred that our proposed algorithm is superior in time reduction over ECU, on average 11% of the time. Apart from that, the quality (PSNR and bit) in both of the algorithms are very similar
(11) ΔPSN R ECU =PSN R ECU −PSN R proposed ,
(12) ΔBi t ECU = Bi t ECU −Bi t proposed Bi t ECU ×100%,
(13) Δ T ECU = Tim e ECU −Tim e proposed Tim e proposed ×100%.
Performance comparison with ECU[3]
Sequence Name | ΔPSNRECU | ΔBitECU% | ΔBDECU | ΔTECU% |
Blowing Bubbles | −0.039 | 0.566 | −1.6 | −9.37 |
BQ Square | −0.058 | 0.772 | −2.1 | −8.37 |
Basketball Pass | −0.015 | 0.974 | −1.3 | −8.50 |
Race Horses | −0.044 | 1.597 | −2.5 | −8.70 |
Basketball Drill | −0.017 | 0.794 | −1.2 | −9.93 |
Party Scene | −0.050 | 0.842 | −2.1 | −8.80 |
Race Horses C | −0.016 | 0.525 | −1.0 | −10.57 |
BQ Mall | −0.039 | 0.779 | −1.9 | −8.30 |
Basketball Drive | 0.000 | 0.108 | −0.1 | −13.07 |
Cactus | −0.005 | 0.124 | −0.4 | −15.10 |
Kimono | 0.004 | 0.137 | 0 | −13.07 |
Park Scene | −0.016 | 0.432 | −0.8 | −11.21 |
BQ Terrace | −0.017 | 0.573 | −0.9 | −12.65 |
Traffic | −0.009 | 0.624 | −0.7 | −12.89 |
People on Street | −0.016 | 0.741 | −0.8 | −13.59 |
Nebula | −0.018 | 0.834 | −1.1 | −12.19 |
Steam Locomotive | −0.009 | 0.847 | −0.7 | −11.67 |
Average | −0.021 | 0.662 | −1.12 | −11.05 |
IV. Conclusion
We have proposed a new, early CU-splitting termination (CST) algorithm for fast HEVC encoding, based on a ratio function. The RD costs for different CU dimensions and the PU-level motion complexity are also considered in our two-stage algorithm. In the first stage, only the CTU is considered. Other dimensions are considered in the second stage. Our algorithm achieved, on average, a 38.03% time reduction over the original HM 10.0 software, with a 1.68% BD loss. Compared with ECU
[3]
, our method achieves a better than 11.05% time-reduction factor.
CST combined with other algorithms.
Sequence | CST+[10] | CST+[12] | CST+[13] |
ΔT% | ΔBD | ΔT% | ΔBD | ΔT% | ΔBD |
Blowing Bubbles | −40.38 | 0.8 | −50.91 | 2.8 | −48.41 | 2.1 |
BQ Square | −42.12 | 0.3 | −51.22 | 3.1 | −47.53 | 2.4 |
Basketball Pass | −33.24 | 0.8 | −45.28 | 2.4 | −42.89 | 1.8 |
Race Horses | −22.78 | 0.9 | −41.56 | 3.4 | −31.51 | 3.0 |
Basketball Drill | −35.81 | 0.9 | −48.27 | 1.9 | −42.91 | 1.4 |
Party Scene | −35.78 | 1.2 | −42.93 | 2.9 | −44.81 | 2.5 |
Race Horses C | −30.12 | 0.8 | −41.87 | 1.8 | −38.64 | 1.7 |
BQ Mall | −32.12 | 1.1 | −45.78 | 2.6 | −41.78 | 2.6 |
Basketball Drive | −39.83 | 0.1 | −51.56 | 0.8 | −46.32 | 0.7 |
Cactus | −42.16 | 0.4 | −57.84 | 1.6 | −49.37 | 1.2 |
Kimono | −31.53 | 0.2 | −48.78 | 0.7 | −38.72 | 0.9 |
Park Scene | −39.11 | 0.6 | −52.91 | 1.6 | −48.61 | 1.2 |
BQ Terrace | −35.12 | 0.6 | −49.78 | 1.5 | −43.21 | 1.3 |
Traffic | −37.58 | 0.5 | −48.51 | 1.9 | −45.63 | 1.8 |
People on Street | −36.15 | 0.9 | −46.21 | 1.4 | −42.67 | 1.3 |
Nebula | −31.91 | 0.6 | −49.51 | 2.9 | −38.62 | 2.6 |
Steam Locomotive | −31.41 | 0.4 | −51.93 | 2.1 | −41.75 | 1.5 |
Average | −35.12 | 0.65 | −48.52 | 2.08 | −43.14 | 1.76 |
Apart from that, we have combined our algorithm with other related algorithms. There are three algorithms that have been reported which are related with INTER mode decision
[10]
–
[12]
. Among them, the performance of the combined algorithms of CST and
[11]
are not very impressive. Hence in
Table 9
, we have shown the performance of the combined algorithms CST+
[10]
and CST+
[12]
. While we combine the proposed algorithm with
[10]
, it gives a more robust performance in terms of quality loss. In this implementation, we have included the adaptive CU depth-range estimation
[10]
. Moreover, the early termination techniques proposed in
[10]
use motion homogeneity; and RD cost–based and SKIP-based checking, which are incorporated here. However, the time-saving factor is degraded in this case, but it gives lower BD loss. On the other hand,
[12]
and CST give better time reduction with insignificant high-BD loss. In this implementation, we have included the features of coded block flags (CBF) of the INTER 2N × 2N mode (
xcbf
), the side information in RD cost (
xsi
), the CU depth of the co-located CU (
xtp
), the RD cost difference between SKIP and INTER 2N × 2N mode (
xdrc
), and the sum of absolute transformed difference between prediction and original pixel values (
xstd
), which are discussed in
[12]
. Moreover, we have combined a related work for CU splitting in a INTRA mode decision algorithm
[13]
. In this implementation, we have considered only the first part of
[13]
, which is related with CU splitting. However, we have implemented in this work the early termination of CU cost calculation, which is discussed in
[13]
in detail. In
Table 9
, the performance of CST+
[13]
is given. This combined algorithm provides a slight improvement in time-saving factor, with similar BD loss.
This research was supported by the Korea Communications Commission, Korea, under the ETRI R&D support program supervised by the Korea Communications Agency (KCA-2012-11921-02001).
BIO
iit.kalyan@gmail.com
Kalyan Goswami is a PhD student at the Department of Computer Engineering, Sun Moon University, Asan, Rep. of Korea. He received his BTech degree in electronics and telecommunication engineering from Kalyani University, Calcutta, India, in 2004, and his MS in Advanced Technology Development Center from IIT Kharagpur, India, in 2011. Before joining IIT, he was working as a programmer analyst in Cognizant Technology Solutions, Kolkata, India. His research interests include algorithm development in the field of Video Processing.
Corresponding Author bg.kim@mpcl.sunmoon.ac.kr
Byung-Gyu Kim received his BS degree from Pusan National University, Busan, Rep. of Korea, in 1996 and his MS degree from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Rep. of Korea in 1998. In 2004, he received a PhD degree at the Department of Electrical Engineering and Computer Science from Korea Advanced Institute of Science and Technology (KAIST). In March 2004, he joined in the real-time multimedia research team at the Electronics and Telecommunications Research Institute (ETRI), Daejeon, Rep. of Korea, where he was a senior researcher. In February 2009, he joined the Division of Computer Science and Engineering at Sun Moon University, Asan, Rep. of Korea, where he is currently a professor. In 2007, he served as an editorial board member of the International Journal of Soft Computing, Recent Patents on Signal Processing, Research Journal of Information Technology, Journal of Convergence Information Technology, and Journal of Engineering and Applied Sciences. Also, he is serving as an associate editor of Circuits, Systems and Signal Processing (Springer), International Journal of Image Processing and Visual Communication (IJIPVC), and The Scientific World Journal (Hindawi). He was an organizing committee member of CSIP2013 in Shenzhen, China. He also served as program committee member of CSIP 2011, CUTE2012, EMC 2012, FCC2014, and EMC2014. He has published over 90 international journal and conference papers in his field. His research interests include image and video object segmentation for content-based image coding, wireless multimedia sensor networks, real-time multimedia communication, and intelligent information systems for image signal processing. He is a member of IEEE, ACM, and IEICE.
hopeof@mail.kaist.ac.kr
Dongsan Jun received his BS degree in electrical engineering and computer science from Pusan University, Busan, Rep. of Korea in 2002 and his MS and PhD degrees in electrical engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Rep. of Korea in 2004 and 2011, respectively. He has been a senior researcher at Electronics and Telecommunications Research Institute (ETRI), Daejeon, Rep. of Korea, since 2004, and an adjunct professor of the Mobile Communication and Digital Broadcasting Engineering Department at the University of Science and Technology (UST), Daejeon, Rep. of Korea, since 2011. His research interests include image computing systems, pattern recognition, video compression, and realistic broadcasting systems.
zeroone@etri.re.kr
Soon-Heung Jung received his BS degree in electronic engineering in 2001 from Pusan National University, Busan, Rep. of Korea. He received his MS degree in electronic engineering in 2003 from Korea Advanced Institute in Science and Technology (KAIST), Daejeon, Rep. of Korea. From 2003 to 2005, he was a research engineer at LG Electronics, Rep. of Korea. Since 2005, he has been a senior member of the engineering staff at ETRI and he is also working toward a PhD in electronic engineering at KAIST, Daejeon, Rep. of Korea. His research interests are in the area of visual communication, video signal processing, video coding, and realistic broadcasting systems.
chitos@mail.kaist.ac.kr
Jin Soo Choi received his BE, ME, and PhD in electronic engineering from Kyungpook National University, Daegu, Rep. of Korea, in 1990, 1992, and 1996, respectively. Since 1996, he has been a principal member of the engineering staff at ETRI, Daejeon, Rep. of Korea. He has been involved in developing the MPEG-4 codec system, data broadcasting systems, and UDTV. His research interests include visual signal processing and interactive services in the field of digital broadcasting technology.
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