1. Recursive End-to-end Distortion Estimation with Model-based Cross-correlation Approximation Hua Yang, Kenneth Rose Signal Compression Lab University of California, Santa Barbara

2. 9/17/2003ICIP 20032 Outline  Introduction  Problems in applying ROPE to sub-pixel prediction  Model-based cross-correlation approximation  Simulation results  Conclusions

3. 9/17/2003ICIP 20033 Introduction  Video transmission over lossy networks Raw Video Source Encoder Channel Encoder Network Channel Decoder Source Decoder Displayed Video End-to-end Quality f (source coding, channel loss, error concealment) Loss due to error, buffer overflow, long delay

4. 9/17/2003ICIP 20034 Introduction  Rate-distortion (RD) optimization An efficient framework for error robustness.  Recursive optimal per-pixel estimate (ROPE) [ Zhang 2000] Account for all the relevant factors. Superior performance among existing schemes: high estimation accuracy and low complexity. Frequently applied to R-D optimized mode selection in several video coding frameworks. R: Coding bit rate D: End-to-end distortion Accurate measurement Trivial Non-trivial

5. 9/17/2003ICIP 20035 Introduction  Problems in applying ROPE Not accommodate sub-pixel prediction More generally Sub-pixel prediction Bi-directional prediction for B and EP frames De-blocking filter Overlapped block motion compensation, etc. Pixel averagingCross-correlation ROPE Prohibitive storage & comput. cost ? Low complexity Accurate estimation

6. 9/17/2003ICIP 20036 Introduction  One existent solution [Stuhlmuller, 2000] If d(X, Y) < d max, accurately estimate and store E{XY}; Otherwise, E{XY} = E{X}E{Y}. Motivation: two distant pixels are less likely to be averaged in practice. C Greatly reduce the complexity.  Still need to additionally compute and store a substantial number of cross-correlation values in advance.  The uncorrelation assumption compromises the estimation accuracy.

7. 9/17/2003ICIP 20037 Introduction  Our proposed solution in this work Two cross-correlation approximation schemes stemming from two differing model assumptions. Based on the marginal moments of pixels, which are available quantities in ROPE.  No additional storage space, and no redundant computation for possibly unused cross-correlation values.  The high estimation accuracy of ROPE is well maintained.

8. 9/17/2003ICIP 20038 Problems in Applying ROPE to Sub-pixel Prediction  Recursive optimal per-pixel estimate (ROPE) For Inter mode macroblock (MB):  Pixel i in frame n is predicted by pixel j in frame n-1.  To conceal a lost frame, simply replace it with the previous reconstructed frame.  Sub-pixel prediction Interpolation from the original pixels of integer position  Improve the performance of motion compensated prediction. H.263: half-pixel; H.264: quarter-pixel or even higher accuracy.

9. 9/17/2003ICIP 20039 Problems in Applying ROPE to Sub-pixel Prediction  Half-pixel prediction in H.263 Assume pixel i in frame n is predicted by a half pixel in frame n-1, e.g. b, then: AB C D ab cd Integer pixel position Half pixel position a=A b=(A+B+1-CTRL)/2 c=(A+C+1-CTRL)/2 d=(A+B+C+D+2-CTRL)/2 Inter-pixel cross-correlation Control parameter: 0 or 1

10. 9/17/2003ICIP 200310 Problems in Applying ROPE to Sub-pixel Prediction  Inter-pixel cross-correlation Essentially, its presence is due to the pixel averaging operation, which appears in many common techniques of video coding standards. Exact computation of the needed cross-correlation for the current frame may require the availability of all the cross-correlation terms in previous frames. This entails too much complexity for practical video coding systems. (E.g. assuming 4 bytes per value and QCIF, we need 2.4GB to store the cross-correlation terms for accurate distortion estimation.)

11. 9/17/2003ICIP 200311 Model-based Cross-correlation Approximation  Basic idea Approximate the cross-correlation between two pixels by a function of the available 1 st and 2 nd order marginal moments. Consequently, no additional storage requirements, and minimum additional computation complexity ( as the computation occurs only when a specific cross-correlation is needed ).  Problem formulation Approximate E{XY}, given E{X}, E{Y}, E{X 2 }, E{Y 2 }.

12. 9/17/2003ICIP 200312 Model-based Cross-correlation Approximation  Model-based cross-correlation approximation Model I X = a + bY, where a,b are unknown constants, b  0. Model II X = N + bY, b is constant. N is a zero-mean random variable, and is independent of Y., with X,Y are two pixels. Specify which of the two pixels is X or Y. Unsymmetric

13. 9/17/2003ICIP 200313 Model-based Cross-correlation Approximation  Useful bounds To further limit the propagation of estimation error. General bound for each involved quantity  Obvious fact: The pixel value is within the range of 0~255. Bound for cross-correlation Schwarz Inequality:

14. 9/17/2003ICIP 200314 Simulation Results  Simulation settings: UBC H.263+ codec Encoder  Given total bit rate and packet loss rate.  Half-pixel prediction is employed. Decoder  Averaging PSNR over 50 packet loss patterns generated under the same packet loss rate.

15. 9/17/2003ICIP 200315 Simulation Results  Tested methods “Model I”, “Model II” “Model 0”  Uncorrelation model: E{XY}=E{X}E{Y} “Full Pel”  method in original work of ROPE  Approximate half-pixel prediction simply by integer pixel prediction. “Actual”  Real average PSNR result at the decoder. “Performance Bound”  ROPE with integer pixel prediction demonstrates the best estimation accuracy of ROPE.

16. 9/17/2003ICIP 200316 Simulation Results Foreman, QCIF, 30f/s, 200kb/s, 1 st 150 frames, p = 5%, periodic Intra-update. Distortion estimation performance “Model II” has the best end-to-end distortion estimation accuracy.

17. 9/17/2003ICIP 200317 Simulation Results Distortion estimation performance comparison (cont.) Foreman, QCIF, 30f/s, 200kb/s, 1 st 150 frames, periodic Intra-update. In spite of the simplicity of the linear model, “Model II” approaches the performance bound of ROPE very closely. Both proposed methods achieve better estimation accuracy than that of the “Model 0” method.

18. 9/17/2003ICIP 200318 Simulation Results Performance improvement comparison Foreman, QCIF, 30f/s, 200kb/s, 1 st 150 frames, RD optimized Intra-update. Miss_am, QCIF, 30f/s, 100kb/s, 1 st 150 frames, RD optimized Intra-update. Performance gain of half-pixel prediction over integer pixel prediction in the case of RD optimized INTRA updating Both proposed approximation schemes consistently achieve better performance gains than the other two methods.

19. 9/17/2003ICIP 200319 Conclusions  Two model-based schemes to approximate the cross- correlation with the available quantities from ROPE.  Low complexity & High estimation accuracy  The practical applicability of ROPE is significantly enhanced.