1. Encoding Stored Video for Streaming Applications IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 11, NO. 2, FEBRUARY 2001 I.-Ming Pao Ming-Ting Sun

2. Outline  Introduction  Method  Simulation Result  Conclusion

3. Introduction  Digital video applications have become increasingly popular.  There are several video standards established for different purposes. e.g, MPEG-1, MPEG-2, H.263…

4. Introduction  Basic building blocks

5. Introduction  Real-time Visual Communication delay sensitive processes need to be done in constraint time rate control scheme is not suitable  Nonreal-time Visual Communication delay tolerable pre-loaded time decoder buffer rate control scheme is suitable

6. Buffer and Pre-loading

7. Introduction  Streaming video applications Video sequences are  encoded off-line  Stored in a server Pre-load before playback e.g, VOD

8. Problem  Bit allocation and video quality  Minimum distortion under the rate constraint

9. Introduction  Contribution of this paper : 1.Propose a sliding-window rate-control scheme. 2.A quantized DCT coefficient selection scheme. 3.Improve video quality for video streaming.

10. Outline  Introduction  Method  Simulation Result  Conclusion

11. Global View  Generate encoded bitstream Sliding-window encoding scheme Consider the constraints  buffer-size  pre-loading time  DCT coefficient selection  Run-length coding

12. Sliding-Window Encoding Scheme  Use future frames to improve video quality.  Set window size W to encode video frame. frames : i, i+1, …, i+W-1 let frame i be the current frame  This proposed encoder better than real-time’s for the same bitrate[20]. [20] I.-M. Pao and M.-T. Sun, “A rate-control scheme for streaming video encoding,”in Proc. 32nd Asilomar Conf. Signals, Systems and Computers, vol. 2, Asilomar, CA, Nov. 1998, pp. 1616–1620.

13. Sliding-Window Encoding Scheme  Bit allocation rate and distortion scheme[18]  low distortion or high-rate case  high distortion or low-rate case [18] J. Ribas-Corbera and S. Lei, “Rate control in DCT video coding for low-delay video communications,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, pp. 172–185, Feb. 1999.

15. Sliding-Window Encoding Scheme  Bit allocation mathematical modeling[18] [18] J. Ribas-Corbera and S. Lei, “Rate control in DCT video coding for low-delay video communications,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, pp. 172–185, Feb. 1999.

16. Sliding-Window Encoding Scheme  Target number of bits for encoding frame R × time

17. Buffer-size and Pre-loading Time Requirement  Why need buffer ? store the excess bits waiting to be decoded e.g, bits of future frames  Why need pre-loading time? the delay before playback

18. Buffer-size and Pre-loading Time Requirement  Buffer variation expression :

19. B0B0 p0p0 Buffer Size G

20. Buffer-size and Pre-loading Time Requirement A.Finding decoder buffer size and pre-loading time given a video bitstream

21. underflow overflow

22. Buffer-size and Pre-loading Time Requirement B.Generating a video bitstream given decoder buffer size and pre-loading time  To prevent the buffer-underflow :  To prevent the buffer-overflow :

23. Bit Allocation with Constraints Step 0 : Initialization : initialize the bit-count regulator. Step 1 : Compute the Proposed Target Bits for Frame : compute the ideal target number of bits for frame i (i = 0, 1, 2, 3,...). avoid the underflow and overflow constraints.

24. Bit Allocation with Constraints Step 2 : Macroblock-Layer Rate-Control : distribute to the macroblocks in the ith frame. find DCT coefficient selection for each macroblock. encode bitstream Step 3 : Update Bit-Count Regulator : update regulator : if there are more frames to be encoded, go to Step 1, or else stop.

25. DCT Coefficient Selection  Quantize the DCT coefficients rate-distortion sense and macroblock level. quantizer step-sizes(Q) largely determine the rate- distortion tradeoff.

26. DCT Coefficient Selection  Run-length coding with LAST (LAST, RUN, LEVEL) (0, 4, 6) bitstream

27. DCT Coefficient Selection  There are not optimal for all video sequences by limited quantizer selections and predetermined run-length codeword.  The encoder can adjust the quantized coefficient’s level. a marginal distortion increase but a significant bit-rate reduction.

28. DCT Coefficient Selection  This paper use Lagrange multiplier method for rate-distortion optimization in selecting the quantization parameter (QP) and adjusting the quantized DCT coefficients (LEVEL). the best combination of QP and LEVELs will be the lowest cost in the rate-distortion sense.

29. DCT Coefficient Selection  Goal is to find the minimum distortion under the rate constraint : for every 8 X 8 block  the optimal QP for the macroblock  the LEVEL for each coefficient

30. DCT Coefficient Selection  The constrained problem converts to an unconstrained problem through the Lagrange multiplier λ (≥ 0).. the problem becomes the determination of the LEVELs of the coefficients.

31. Better

32. Outline  Introduction  Method  Simulation Result  Conclusion

33. Simulation Result  Different bitrates : 32, 64, and 128 kbits/s  Different types of video sequences : large facial movement head and shoulder camera panning  Compare with TMN8

42. Outline  Introduction  Method  Simulation Result  Conclusion

43. Conclusion  Better video quality than TMN8 in high motion-activity frames and scene-change frames.  Require more buffer size and pre-loading time than TMN8.