1. Wen-Hsiao Peng Chun-Chi Chen
An Inter-Frame Prediction Technique Combining Template Matching Prediction and Block Motion Compensation for High Efficiency Video Coding
Circuits and Systems for Video Technology, 2013 IEEE Transactions on
Wen-Hsiao Peng
Chun-Chi Chen

2. Outline Introduction Background Bi-prediction Combining TMP and BMC
Analysis LS and LMS
Experiment Results
Conclusion

3. Introduction Inter prediction combines MVs from
TMP
BMC
for Overlapped Block Motion Compensation.
Prediction performance of OBMC close to that of bi-prediction.
without having to signal the template MV

4. Introduction TMP generally outperforms SKIP prediction.
TMP is inferior to block-based motion compensation.
Another MV is required to best complement the template MV.

5. Introduction
A key issue in video coders with motion-compensated prediction is how to trade off effectively between
accuracy of the motion field representation
required overhead
Based on HEVC version 6.0
Achieve the bitrate reduction.

6. Outline Introduction Background Bi-prediction Combining TMP and BMC
Template Matching Prediction
Block Motion Compensation
SKIP and Merge-SKIP
Signal Model
Prediction Error Surface
Bi-prediction Combining TMP and BMC
Analysis LS and LMS
Experiment Results
Conclusion

7. Template Matching Prediction
Obtains the MV at a current pixel by finding, in the reference frames, the best match for a template region composed of its surrounding reconstructed pixels.

8. Block Motion Compensation
The frames are partitioned in blocks of pixels and each block is predicted from a block of equal size in the reference frame.

9. Comparsion
True motion
BMC
TMP

10. SKIP and Merge-SKIP
SKIP
H.264/AVC
Merge-SKIP
Weighted sum

11. Signal Model Tao et al [19] Zheng et al [24] .
[19] B. Tao and M. T. Orchard, “A parametric solution for optimal overlapped block motion compensation,”
IEEE Trans. on Image Processing, vol. 10, no. 3, pp. 341–350, Mar
[24] W. Zheng, Y. Shishikui, M. Naemura, Y. Kanatsugu, and S. Itoh,“Analysis of space-dependent characteristics of motion-
compensated frame differences based on a statistical motion distribution model,”
IEEE Trans. on Image Processing, vol. 11, no. 4, pp. 377–386, Apr

12. Signal Model Mean-sqaured prediction error Tao et al [19].
Tao et al [19]
Zheng et al [24]
[19] B. Tao and M. T. Orchard, “A parametric solution for optimal overlapped block motion compensation,”
IEEE Trans. on Image Processing, vol. 10, no. 3, pp. 341–350, Mar
[24] W. Zheng, Y. Shishikui, M. Naemura, Y. Kanatsugu, and S. Itoh,“Analysis of space-dependent characteristics of motion-
compensated frame differences based on a statistical motion distribution model,”
IEEE Trans. on Image Processing, vol. 11, no. 4, pp. 377–386, Apr

13. Signal Model Block MV, vb , and block center, sc
vb = v(sc) .
Template MV, vt , and template center, st
vt = v(st)

14. Signal Model
Tao’s model
Zheng’s model

15. Prediction Error Surface
The error variance tends to be larger at the block boundaries and smaller around the center, which is understandable if we recall that vb approximates v(sc), the true motion associated with the block center.
TMP better than BMC => if the intensity and motion fields are less random or have a high spatial correlation

16. Prediction Performance Comparsion
Encoding 50 frames
BasketballDrill has complex motion
Johnny is of video-conferencing type and has less detail

17. Outline Introduction Background Bi-prediction Combining TMP and BMC
Overlapped Block Motion Compensation
Least Square Solution
Least Mean-Square Solution
Analysis LS and LMS
Experiment Results
Conclusion

18. Bi-prediction Combining TMP and BMC
Predictor is computed as a weighted average of two reference blocks.
Template MV, vt
Block MV, vb
TMP can better compensate for the movement of the top-left area of a prediction block.
BMC is thus aimed at reducing further the prediction residual in the remaining area.

19. Overlapped Block Motion Compensation
The weighting can be pixel adaptive. .
ω is indicating their likelihood
The problem is to determine the OBMC weights so that the resulting predictor would produce a minimal residual.

20. Overlapped Block Motion Compensation
How to minimize the prediction residual by a suitable choice of the block MV and OBMC weights. .
The approaches to solve the problem
Least Squares Approach
Least Mean-Square Approach

21. Least Square Solution
Rely on an iterative algorithm to solve for the optimal weights.
Estimating Block MVs : .
Adapting OBMC Weights :
It’s convergence to a possibly local minimum is usually between 5 to 10 iterations.

22. Least Mean-Square Solution
Introduce statistical signal models.
Given that every block is to be predicted using OBMC based on two MVs
defaulting to the true MV
MV sampling the motion field at some point sb
determine a set of OBMC weights
Template MV approximating the pixel true motion can be obtained first at the template centroid.
better compensate for top-left corner
The optimal choice for the other MV is given by the true motion of a pixel in the bottom-right quadrant.

23. Least Mean-Square Solution
Transform the problem of minimizing ξ into that of minimizing its expected value E[ξ]. .
Fixing sb determine the :
Find the optimal sb that yields the global minimum :

24. Outline Introduction Background Bi-prediction Combining TMP and BMC
Analysis LS and LMS
Experiment Results
Conclusion

25. Analysis LS and LMS
indicates the likelihood of vt being the true motion of a pixel at s relative to the other hypothesis vb.
Template MV is not as reliable for compensating pixels in the upper-left area as predicted by the theoretical results.
Tao’s model
Zheng’s model
LS solution

26. Multiple reference frames
Analysis LS and LMS
So, we would expect to drop to zero (or, equivalently,  to increase to unity)
without amendment
with amendment
Multiple reference frames

27. Results
Reductions in mean-square error

28. Outline Introduction Background Bi-prediction Combining TMP and BMC
Analysis LS and LMS
Experiment Results
Conclusion

29. Experiment Results Random Access High Efficiency Random Access Main
Low-Delay B High Efficiency
Low-Delay B Main

30. Experiment Results

31. Experiment Results

32. Experiment Results

33. Outline Introduction Background Bi-prediction Combining TMP and BMC
Analysis LS and LMS
Experiment Results
Conclusion

34. Conclusion
We proposed a bi-prediction scheme that combines BMC and TMP predictors through OBMC.
TMP is inferior to BMC, but is, in general, superior to SKIP prediction.
The data dependency complicates the pipeline design and hinders parallel processing.
The proposed method restricted the use of TB-mode to 2Nx2N PUs only.
For TMP to work properly, pixels in the template region must be reconstructed prior to the motion estimation and compensation of a current PU.