Packet Video Error Concealment With Auto Regressive Model

1. Packet Video Error Concealment With Auto Regressive Model Yongbing Zhang, Xinguang Xiang, Debin Zhao, Siwe Ma, Student Member, IEEE, and Wen Gao, Fellow, IEEE IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 22, NO. 1, JANUARY 2012

2. Outline • Introduction • Auto-Regressive Model-Based Error Concealment • AR Coefficient Derivation Under Spatial Continuity Constraint • AR Coefficient Derivation Under Temporal Continuity • Experimental Results and Analysis • Conclusion

3. Introduction

4. Introduction • Error resilienceand error concealmentare two major techniques to combat the problem. • Error resilience adds redundant information at the encoder and decreases the compression efficiency. • Error concealment is a post-processing technique utilizing the correctly received information at the decoder side

5. Introduction • error concealment • Spatial approaches • Temporal approaches • hybrid approaches (mixed above)

6. Error concealment • Spatial approach • utilizing the correctly decoded surrounding pixels under smoothness constraint • Spatial approaches may yield better performance than temporal ones in scenes with high motion

7. Error concealment • Temporal approaches • Temporal approaches restore the corrupted blocks by exploiting temporal correlation between successive frames.

8. Error concealment • hybrid approaches(proposed) • The interpolation filters are separable and the coefficients are fixed • achieved good performance for isotropic regions,poor for anisotropic regions. • auto-regressive(AR) model based error concealment proposed

9. Auto-Regressive Model-Based Error Concealment

10. Auto-Regressive Model-Based Error Concealment

11. Auto-Regressive Model-Based Error Concealment • we have to estimate the AR coefficients by exploring the spatial and temporal correlations of the corrupted block with its available spatial and temporal neighboring pixels.

12. AR Coefficient Derivation Under Spatial Continuity Constraint • we assume all the pixels within the corrupted block possess the same AR coefficients

13. AR Coefficient Derivation Under Spatial Continuity Constraint • If any of the neighboring blocks are correctly received, the correctly received neighboring blocks are utilized to train AR coefficients of the corrupted block. • If all the neighboring blocks are lost, the already concealed neighboring blocks are utilized to train AR coefficients of the corrupted block.

14. AR Coefficient Derivation Under Temporal Continuity • xt(k, l), the corresponding motion aligned pixel et−1 (k, l) in the extended block is first found, and then the corresponding pixel xt−2 (k, l) in the second closest reconstructed frame is also found by the same MV.

15. AR Coefficient Derivation Under Temporal Continuity

16. AR Coefficient Derivation Under Temporal Continuity • After having obtained the AR coefficients α and β, we merge the two regression results as • If no solution to α and β, use the traditional methods (BMA or STBMA)

17. Experimental Results and Analysis • The encoding group of picture (GOP) is set to be IPPP. . . , where I frames are encoded every 16 frames. • The transmission errors are assumed to only occur in P frames

18. Experimental Results and Analysis

19. Experimental Results and Analysis

20. Experimental Results and Analysis

24. Conclusion • For each corrupted block, we first derived the motion vector and then replenished each corrupted pixel. • We proposed two block-dependent AR coefficient derivation algorithms under spatial and temporal continuity constraints. • The results outperforms the method without AR with acceptable computational complexity.