1. Fast Block Based Motion Estimation Algorithms in HEVC
Srikanth Vasireddy
Multimedia Processing Lab,UTA

2. Contents Growing demand for Video HEVC Encoder and Decoder
Features of Moving pictures
Block based Motion Estimation
Different Motion Estimation Algorithms
Test Sequences
Simulation Results
Work done and In Progress
Acronyms
References
Multimedia Processing Lab,UTA

3. Growing Demand for Video
Increase in Applications
Higher efficiency
Broadcast Quality
Higher throughput
Increase in Mobile data traffic
Low power
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4. HEVC Encoder [5]
Fig .1: HEVC Encoder[5]
ME has 84% coding complexity and time to encode [1] [5]
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5. HEVC Decoder [1]
Fig .2: HEVC Decoder[1]
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6. Features of Moving Pictures
Moving images contain significant temporal redundancy
successive frames are very similar
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7. Intra frame and Inter frame coding
Video coding algorithms usually contain two coding schemes :
Intraframe coding : Intraframe coding does not exploit the correlation among adjacent frames; Intraframe coding therefore is similar to the still image coding.
Interframe coding :The interframe coding should include motion estimation/compensation process to remove temporal redundancy.
“The amount of data can be reduced significantly if the previous frame is subtracted from the current frame.”[4]
Fig.3: Motion Estimation and Motion Compensation [4]
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8. Temporal Redundancy Removal [1]
M.J.Jakubowski and G.Pastuszak, “Block-based motion estimation algorithms – a survey ,” Opto-Electronic Review , Volume 21, pp ,,March2013.
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9. Block based Motion Estimation [6][11]
The MPEG and H.26X standards[4] use block based technique for motion estimation /compensation.
In this technique, each current frame is divided into equal-size blocks, called source blocks.
Each source block is associated with a search region in the reference frame.
The objective of block-matching is to find a candidate block in the search region best matched to the source block.
The relative distances between a source block and its candidate blocks are called motion vectors.
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10. X: Source block for block-matching Bx: Search area associated with X
Video Sequence
time
X: Source block for
block-matching
Bx: Search area
associated with X
MV: Motion vector
Reference frame
current frame
Fig.4: Block matching Scenario [6]
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11. Motion Vector and Search Area
Source block
Motion vector: (u, v)
Search Area:
Candidate block
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12. Motion Estimation Algorithms [11]
Full Search Algorithm
Three Step Search Algorithm
Four Step Search Algorithm
Diamond Search Algorithm
Hexagonal Search Algorithm
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13. Full Search Algorithm u v Candidate Block Search Area
If p=7, then there are
(2p+1)(2p+1)=225 candidate blocks.
Fig.5 : Full Search Scenario [6][11]

14. Computational complexity is of order n^2 for a block size of nxn
In order to get the best match block in the reference frame, it is necessary to compare the current block with all the candidate blocks of the reference frames.
Full search motion estimation calculates the sum of absolute difference (SAD) value at each possible location in the search window.
Full search computes the all candidate blocks intensively for the large search window.
Computational complexity is of order n^2 for a block size of nxn
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15. 3SS Algorithm
The first step involves block-matching based on 4-pel resolution at the nine location.(step size m).Now they check for minimum cost distance and shift center to the new point of minimum.
The second step involves block-matching based on 2-pel resolution around the location determined by the first step.(step size is m/2)
The third step repeats the process in the second step (but with resolution 1-pel).
Fig.6: 3 Step Search Scenario [6] [11]
The position with minimum cost will give us the motion vector and also position of the best match.
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16. 4SS Algorithm
4SS algorithm utilizes a center-biased search pattern with nine checking points on a 5 x 5 window in the instead of a 9 x 9 window in the 3SS.
This algorithm helps in reducing the number of search points compared to the 3SS and hence is more robust.
Block distortion method (BDM) point is used
Fig.7: 4Step Search Scenario [6] [11]
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17. Diamond Search Algorithm
The DS algorithm employs two search patterns.
Large diamond search pattern(LDSP) comprises 9 checking points from which eight points surround the center one to compose a diamond shape.
Small diamond search pattern (SDSP) consisting of 5 checking points forms a small diamond shape.
LDSP is repeatedly used until the minimum block distortion (MBD) occurs at the center point.
Fig.8 : Diamond Search Scenario for ME [7] [11]
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18. Hexagonal Search Algorithm
In block motion estimation, a search pattern with a different shape or size has a very important impact on search speed and distortion performance.
HEXBS algorithm can find a same motion vector with fewer search points than the DS algorithm. (Calculate the minimum cost at 6 corner points of Hexagon)
Generally speaking, the larger the motion vector, the more search points the HEXBS algorithm can save.
Fig.9:Hexagonal Search Scenario for ME [7][11]
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19. Test Sequences[23] RaceHorses_416x240_30.yuv sequence
BQMall_832x480_60.yuv sequence
KirstenAndSara_1280x720_60.yuv sequence
ParkScene_1920x1080_24.yuv sequence
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20. Simulation Results
RaceHorses_416x240_30.yuv, Number of frames encoded = 20
Random Access profile (FAST SEARCH)
Random Access profile (FULL SEARCH) QP
PSNR in dB
Bit rate in kbps
Encoding time in seconds 22 27 32
88.600 37
71.437
Table 1: Results for RaceHorses_416x240_30.yuv sequence in Random Access Configuration
BQMall_832x480_60.yuv , Number of frames encoded = 20
Random Access profile (FAST SEARCH)
Random Access profile (FULL SEARCH) QP
PSNR in dB
Bit rate kbps
Encoding time in seconds
Bit rate in kbps 22 27 32 37
Table 2: Results for BQMall_832x480_60.yuv sequence in Random Access Configuration
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21. Testing Platform: KristenAndSara_1280x720_60.yuv
KristenAndSara_1280x720_60.yuv
Number of fames encoded =20
ParkScene_1920x1080_24.yuv
Random Access profile (FAST SEARCH) QP
PSNR in dB
Bit rate in kbps
Encoding time in seconds 22 27 32 37
Table 3: Results for KirstenAndSara_1280x720_60.yuv & ParkScene_1920x1080_24.yuv sequences in Random Access Configuration
Testing Platform:
Processor
Intel Core(TM) i5 CPU 4210U 2.40 GHz
Number of cores 2
Memory
8GB
Operating System
64 bit (x64-based processor),Windows 8.1
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22. Figure 10: Snapshot of encoder_randomaccess_main.cfg[15]
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23. Fig 11: Bit rate vs QP for all test sequences
Fig 12: PSNR vs QP for all test sequences
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24. Fig 13: R-D plot for RaceHorses_416x240_30.yuv sequence
Fig 14: R-D plot for BQMall_832x480_60.yuv sequence
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25. Fig 15: R-D plot for KirstenAndSara_1280x720_60.yuv sequence
Fig 16: R-D plot for ParkScene_1920x1080_24.yuv sequence
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26. Fig 17: Encoding time comparison for RaceHorses_416x240_30
Fig 17: Encoding time comparison for RaceHorses_416x240_30.yuv sequence
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27. Fig 18: Encoding time comparison for RaceHorses_416x240_30
Fig 18: Encoding time comparison for RaceHorses_416x240_30.yuv sequence
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28. Fig 19: Fast Search Encoding time comparison for all test sequences
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29. Fig 20: Full Search Encoding time comparison for RaceHorses & BQMall
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30. Work done and In Progress
Analyzed Full Search,3SS and Diamond Search.
Developed functions in MATLAB like
To find PSNR w.r.t reference image
To compute motion compensated image
To find minimum distance among macro blocks
To compute Mean Absolute difference
Developing functions for Full Search ,3SS and Diamond Search
Multimedia Processing Lab,UTA

31. Acronyms BBME : Block Based Motion Estimation
BD-BR: Bjontegaard Delta Bitrate.
BD-PSNR: Bjontegaard Delta Peak Signal to Noise Ratio.
CABAC: Context Adaptive Binary Arithmetic Coding.
CTB: Coding Tree Block.
CTU: Coding Tree Unit.
CU: Coding Unit.
DBF: De-blocking Filter.
DCT: Discrete Cosine Transform.
fps: frames per second.
HEVC: High Efficiency Video Coding.
HM: HEVC Test Model.
ISO: International Organization for Standardization.
ITU-T: International Telecommunication Union- Telecommunication Standardization Sector.
JCT-VC: Joint Collaborative Team on Video Coding.
MAD: Mean Absolute Difference
MC: Motion Compensation.
ME: Motion Estimation.
MPEG: Moving Picture Experts Group.
MSE: Mean Square Error.
PB: Prediction Block.
PSNR: Peak Signal to Noise Ratio.
QP: Quantization Parameter
SAO: Sample Adaptive Offset.
TB: Transform Block.
TU: Transform Unit.
VCEG: Visual Coding Experts Group.
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32. REFERENCES
[1] V. Sze and M. Budagavi, “Design and Implementation of Next Generation Video Coding Systems (H.265/HEVC Tutorial)”, IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia, June 2014, available on [2] HEVC tutorials [3] G.J. Sullivan; J. Ohm; W.J, Han and T. Wiegand, “Overview of the High Efficiency Video Coding (HEVC) Standard”, IEEE Trans. on Circuits and Systems for Video Technology, Volume: 22, Issue: 12, pp , Dec [4] I.E. Richardson “Video Codec Design : Developing Image and Video compression systems”,Wiley,2002. [5] G. J. Sullivan et al “Standardized Extensions of High Efficiency Video Coding (HEVC).”IEEE Journal of selected topics in Signal Processing” vol. 7, pp , Dec [6] L.C.Manikandan et al “A new survey on Block Matching Algorithms in Video Coding” in International Journal of Engineering Research ,Volume 3,pp ,Feb [7] ] L.N.A. Alves, and A. Navarro, " Fast Motion Estimation Algorithm for HEVC ", Proc IEEE International Conf. on Consumer Electronics -ICCE Berlin , Germany , vol.11 , pp , Sep. , 2012 [8] F. Bossen, et al, “HEVC complexity and implementation analysis”, IEEE Trans. on Circuits and Systems for Video Technology, Volume: 22, Issue: 12, pp , Dec [9] J. Ohm, et al, “Comparison of the Coding Efficiency of Video Coding Standards –Including High Efficiency Video Coding (HEVC)”, IEEE Trans. on Circuits and Systems for Video Technology, volume: 22, Issue: 12, pp , Dec [10] K. Kim, et al, “Block partitioning structure in the HEVC standard,” IEEE Trans. on circuits and systems for video technology, vol. 22, pp , Dec [11] M. Jakubowski and G. Pastuszak, “Block-based motion estimation algorithms-a survey,” Journal of Opto-Electronics Review, vol. 21, pp , Mar [12] A. Abdelazim, W. Masri and B. Noaman "Motion estimation optimization tools for the emerging high efficiency video coding (HEVC)", SPIE vol. 9029, Visual Information Processing and Communication V, , Feb. 17, 2014, doi: /
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33. [13] Software repository for HEVC - https://hevc. hhi. fraunhofer
[14] HEVC white paper –Ittiam systems -
[15] HM Software Manual -
[16] G. Bjontegaard, "Calculation of average PSNR difference between RD curves", VCEG-M33,ITU-T SG 16/Q 6,Austin, TX, April 2001.
[17] Multimedia Processing Lab at UTA:
Analysis of Motion Estimation (ME) Algorithms. By Tuan Phan Minh Ho (Spring 2014)
Comparative study of Motion Estimation (ME) Algorithms by Khyati Mistry (Spring 2008)
[18] has info on developments in HEVC NGVC – Next generation video coding.
[19] Detailed Overview of HEVC/H.265 by Shevach Riabtsev :
[20] W. Hong, “Coherent Block-Based Motion Estimation for Motion-Compensated Frame Rate Up-Conversion", IEEE International Conference on Consumer Electronics, pp , Jan.2010. 
[21] N.Purnachand and L.N. Alves, A. Navarro “Improvements to TZ search motion estimation algorithm for multiview video coding” 19th International Conference on Systems, Signals and Image Processing IWSSIP, pp , 2012.
[22] Video test sequences or or ftp://ftp.kw.bbc.co.uk/hevc/hm-11.0-anchors/bitstreams/ or
[23] M. Wien, “High efficiency video coding: Tools and specification”, Springer, 2015.
[24] I.E. Richardson, “Coding video: A practical guide to HEVC and beyond”, Wiley, 11 May 2015
[25] V.Sze ,M.Budagavi and G.J.Sullivan “ High Efficiency Video Coding(HEVC) –Algorithms and Architectures”, Springer, 2014.
[26] X. Li et al, “Rate-complexity-distortion evaluation for hybrid video coding”, IEEE International Conference on Multimedia and Expo (ICME), pp , July
[27] G. Correa et al, “Performance and computational complexity assessment of high efficiency video encoders”, IEEE Trans. on Circuits and Systems for Video Technology, Vol.22, pp , Dec.2012.
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