IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2008

1. A Fast MB Mode Decision Algorithm for MPEG-2 to H.264 P-Frame transcoding Pedro Cuenca, Member, IEEE, Luis Orozco-Barbosa, Member, IEEE, Gerardo Fernández-Escribano, Antonio Garrido,HariKalva IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2008

2. Outline • Introduction • Fast MB Mode Decision Using Machine Learning • Performance Evaluation • Conclusion

3. Introduction1/3 • Motivation: make transcoding from MPEG-2 to H.264 seamless. • Hypothesis: the MB mode decision in H.264 have a correlation with the distribution of the motion compensated residual in MPEG-2 video.

4. Introduction2/3 • the H.264 MB mode computation problem is posed as a data classification problem. • the MPEG-2 MB coding mode and residual have to be classified into one of the several H.264 coding modes. Fig. 1. Relationship between MPEG-2 MB residual and H.264 MB coding mode.

5. Introduction3/3 • Method: use machine learningtools to exploit the correlation and construct decision trees toclassify the MPEG-2 MBs into one of the codingmodes in H.264.

6. Fast MB Mode Decision Using Machine Learning1/14 Fig. 2. Process for building decision trees for MPEG-2 to H.264 transcoding.

7. Fast MB Mode DecisionUsing Machine Learning2/14 • WEKA data mining tool : machine learning software written in Java and supports several standard data mining tasks. • the J48 algorithm: • implemented in the WEKA data mining tool was used to create the WEKA decision trees. • the J48 algorithm is an implementation of the C4.5 algorithm which widely used as a reference for building decision trees.

8. Fast MB Mode Decision Using Machine Learning 3/14 • Attribute-Relation File Format (ARFF): The file used by the WEKA data mining program, contain the existing relationship between a set of attributes. An ARFF file has two sections: (1) header: contains the name of the relation, the attributes and their types. (2) section: containing the data.

9. Fast MB Mode Decision Using Machine Learning 4/14 • Training sets: • the MPEG-2 sequences encoded at high quality since no B-frames have been used. • use H.264 encoder with a QP of 25 and the R-D optimization enable. • Goal: develop a single, generalized, decision tree to be used for the MPEG-2 to H.264 transcoding process. It’s found that Flower sequence was good for a large number of videos.

10. Fast MB Mode Decision Using Machine Learning 5/14 • The Decision Tree for the proposed transcoder is a hierarchical decision tree consisting of three different WEKA trees. Fig. 3. Decision tree.

11. Fast MB Mode DecisionUsing Machine Learning6/14 • mean and variance of each one ofthe 4x4 residual subblocks. • MB mode in MPEG-2. • coded block pattern (CBPC) usedin MPEG-2. A. Creating the Training Files

12. Fast MB Mode DecisionUsing Machine Learning7/14 B. Decision Tree

13. Fast MB Mode DecisionUsing Machine Learning8/14 B. Decision Tree

14. Fast MB Mode DecisionUsing Machine Learning9/14 B. Decision Tree

15. Fast MB Mode DecisionUsing Machine Learning10/14 B. Decision Tree

16. Fast MB Mode DecisionUsing Machine Learning11/14 • MB mode decision and threshold used in the decision tree depend on the QP used in the H.264 encoding stage. • The mean and variance threshold will have to be different at each QP.

17. Fast MB Mode DecisionUsing Machine Learning12/14 Solution(1): method: Develop the decision trees for each QP and use the appropriatedecision tree depending on the QP selected. drawback: It's complexsince implies to switch between 52 different decision trees resultingin 156 WEKA trees for a transcoder.

18. Fast MB Mode DecisionUsing Machine Learning13/14 Solution(2): method: • Develop asingle decision tree and adjust the mean and variance thresholdused by thetrees basedon the QP of 25. • For QPvalues higher than 25, thethresholds aredecreasedand for QPvalues lower than 25 thresholds are oportionallyincreased. The threshold are adjusted by 2.5% for a change in QP of 1.

19. . Fast MB Mode DecisionUsing Machine Learning14/14 . Fig. 2. Process for building decision trees for MPEG-2 to H.264 transcoding Fig. 4. Proposedtranscoder

20. Performance Evaluation1/8 Input: (1) Ahigh quality MPEG-2 video. (2) QPranging from 5 up to 45 in steps of 5. (3) The sizeof the GOP is 12 frames;where the first frame wasI-frame, and the restof the frames were P-frames. (4) The rate control and CABAC algorithmswere disabled for all the simulations. (5) The number of referencein P-frames was set to 1. (6) The motion search range was set to16 pels with aMV resolution of 1/4 pel.

21. Performance Evaluation2/8 Fig. 6. MB mode decisions generated by the proposed algorithm for the first P-frame in the Ayersroc, Paris, and Foreman sequence. Proposed algorithm Full estimation of H.264

22. Performance Evaluation3/8 RD-results: R-D-cost without FME option or R-D-cost with FME option Test sequence: Martin, Ayersroc, Paries, Tempete, News, Foreman Fromat: CCIR, CIF, QCIF

23. Performance Evaluation4/8

24. Performance Evaluation5/8 RD-results: SAE-cost without FME option or SAE-cost with FME option

25. Performance Evaluation6/8

26. Performance Evaluation7/8 Reference transcoder WIN Proposed transcoder

27. Performance Evaluation8/8

28. Conclusion • The proposed algorithm uses machine learning techniques to develop decision tree decide MPEG-2 to H.264 coding mode, considerably reducing the computational complexity . • It can be applied to develop other transcoders as well.