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Region-of-Interest Based H.264 Encoding Parameter Allocation for Low Power Video Communication

Region-of-Interest Based H.264 Encoding Parameter Allocation for Low Power Video Communication. M. Wang, T. Zhang, C. Liu and S. Goto CSPA 5th International Colloquium on Signal Processing & Its Applications, 2009. Advisor: 葉家宏 Presenter: 陳詠霖 Date:2010/01/13. Outline. Introduction

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Region-of-Interest Based H.264 Encoding Parameter Allocation for Low Power Video Communication

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  1. Region-of-Interest Based H.264 Encoding Parameter Allocation for Low Power Video Communication M. Wang, T. Zhang, C. Liu and S. Goto CSPA 5th International Colloquium on Signal Processing & Its Applications, 2009 Advisor:葉家宏 Presenter:陳詠霖 Date:2010/01/13

  2. Outline • Introduction • System Architecture • ROI Detector • Experimental Result • Conclusion

  3. Introduction • H.264 performs an extremely high compression rate and high visual quality • H.264 has huge computation on motion estimation

  4. Introduction • Human eyes only focus on certain object or area, rather than whole frame. • ROI (Region of Interest) • texture contrast, skin color

  5. Introduction • Unequally Encoding • ROI allocated small QP, higher search range, higher reference frame • Non-ROI allocated higher QP, small search range, small reference frame

  6. Introduction

  7. Outline • Introduction • System Architecture • ROI Detector • Experimental Result • Conclusion

  8. System Architecture

  9. Outline • Introduction • System Architecture • ROI Detector • Experimental Result • Conclusion

  10. ROI Detector

  11. ROI Detector • Y channel • Saliency Map • Human vision is more sensitive on a certain spatial spectrum • Band-pass mask • Gaussian mask

  12. ROI Detector

  13. ROI Detector

  14. ROI Detector • Stripes on the wall are always omitted by eyes

  15. Skin Color Map (UV Channel) • u(i,j) and v(i,j) are U,V components of the pixel at (i,j) • Lu, Hu, Lv, Hv, Luv, Huv are thresholds of the corresponding various • α=0.6,Lu=97, Hu=118, Lv=138, Hv=163, Luv=198, Huv=207

  16. Skin Color Map

  17. Skin Oriented Saliency Map

  18. Greedy Algorithm

  19. Greedy Algorithm • Parameter • L :length • A :sum of saliency skin oriented saliency point • TH :shrink ratio threshold • A*TH :saliency sum of ROI

  20. Greedy Algorithm • Border of image (top, bottom, left, right) • Step 1. Shifts L length • Step 2. Surplus saliency points ≦ A*TH • Step 3. Intersect four border

  21. Outline • Introduction • System Architecture • ROI Detector • Experimental Result • Conclusion

  22. Experimental Result • The QP of ROI and non-ROI are set to QPr and QPn. QPr = QP-1 ; QPn = QP+3;

  23. Experimental Result • QP=32,MRF=5/1(ROI/NON-ROI) SEARCH RANGE=32/8(ROI/NON-ROI)

  24. Experimental Result

  25. Outline • Introduction • System Architecture • ROI Detector • Experimental Result • Conclusion

  26. Conclusion • ROI based parameter allocation is proposed to avoid unnecessary computation • ROI detection can decreases motion estimation time about 65% time • The proposed algorithm keeps visual quality in the ROI

  27. Thanks for your attention

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