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Distortion-Aware Retransmission of Video Packets and Error Concealment using Thumbnail

Distortion-Aware Retransmission of Video Packets and Error Concealment using Thumbnail. EE398 Course Project Winter 07/08 Presenter: Zhi Li. Presentation Outline. Idea in a nutshell Realizations Distortion estimation Retransmission decision Adaptive error concealment Experiment results

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Distortion-Aware Retransmission of Video Packets and Error Concealment using Thumbnail

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  1. Distortion-Aware Retransmission of Video Packets and Error Concealment using Thumbnail EE398 Course Project Winter 07/08 Presenter: Zhi Li

  2. Presentation Outline • Idea in a nutshell • Realizations • Distortion estimation • Retransmission decision • Adaptive error concealment • Experiment results • Conclusions

  3. Retransmission-Based Robust Video Streaming over Lossy Network 0101 1101 0001 0100 NACK NACK 1101 0001 What if retransmission is subjected to a rate constraint?

  4. Prior Related Work • Soft ARQ [Vetterli’98] • avoids retransmitting packets that already passed deadline • Syntax-aware (frame-aware) [Zakhor’02] • gives retransmission priority to I packets over P and B packets • Analysis-by-synthesis [De Martin’07] • With NACKs, Sender simulates decoding (with ErC) multiple times and decides retransmission based on distortion

  5. Proposed Approach: Thumbnail-Aided Retransmission and Error Concealment 0101 1101 0001 0100 NACK NACK 1101 0001

  6. System Overview Raw video Video Packets Lossy video Video Encoder Lossy Network Video Decoder w/ErC Locate Error Packets Send NACK Thumb. Generator Thumbnail

  7. Decomposition of Module Raw video Video Packets Lossy video Video Encoder Lossy Network Video Decoder w/ErC Locate Error Packets Send NACK Thumb. Generator Thumbnail Lossy video Slice Dist. Estimation Pkt Dist. Estimation Retrans. Decision Thumbnail

  8. Thumbnail Generation & Slice Distortion Estimation • Two types of projections • Projection into the mean • Random projection (J240) Z X Y Block Projection Projection Thumbnail pixel Welsh- Hardamard Transform (WHT) Est. MSE e.g. 4 bits

  9. Comparing Two Projections Projection into the mean J240 random projection (Foreman CIF)

  10. Distortion model: decaying and additive Knowing slicing distortion, we can solve distortion contribution of lost packets through a set of linear equations Packet Distortion Estimation

  11. Packet Distortion Estimation (Cont’d) (Foreman CIF)

  12. D D R R Retransmission Decision • Retransmission priority based on (assume packet size known) • Sender randomly drops B packets to maintain 100% transmission rate (also compensating thumbnail rate) . . TR

  13. Intra ErC Inter ErC Adaptive Error Concealment Control Data DCT Coefs Decoder Deq./Inv. Transform 0 Motion- Compensated Predictor ErC Intra/Inter Motion Data Thumbnail

  14. Comparing Various Schemes • Oracle: assume receiver knows orginal video • Thumbnail-aided: proposed method using mean-based thumbnail • Frame-aware: receiver requests retransmission based on the assumption that I packets are more important than P packets • No retransmission

  15. PSNR vs. Packet Loss Rate (default ErC) (Foreman CIF)

  16. PSNR vs. Packet Loss Rate (Adaptive ErC & default ErC) (Foreman CIF)

  17. Visual Quality Thumbnail-aided retran. + default ErC (26.7 dB) Thumbnail-aided retran. + adaptive ErC (27.0 dB) Experiment settings: 20% packet loss, 100% bandwidth

  18. Conclusions • Thumbnail-aided distortion-aware retransmission can achieve gains of 0.5 ~ 1.5 dB over distortion-unaware heuristic methods • Thumbnail-aided adaptive error concealment can achieve additional gains of 0.5 ~ 1 dB under severe distortion conditions • Key ingredient leading to gain is content-level error detection and correction

  19. Acknowledgement • I would like to thank Prof. Girod, Yao-Chung Lin, Xiaoqing Zhu, David Varodayan and Pierpaolo Baccichet for extremely helpful discussions.

  20. Basic Settings • Packetization • one slice per packet • Thumbnail generation • I and P pictures only • For example, 32x32 block maps to1 pixel of 4-bit in thumbnail GOP Video Sequence … … I B P B P B Picture Packet 0101 Thumbnail Slice

  21. Z X Y Block Projection Projection Est. MSE 4 bits Analysis of Two Projections • Analysis model • Channel distortion ~ non-zero mean (to characterize local distortion) and s.d. , i.i.d. • Want to estimate the noise power • Mean-based estimator: • Random-projection estimator: where with equal prob.

  22. Analysis of Two Projections (Cont’d) • Mean-based estimator: • Mean: i.e. biased, always underestimate • Variance: as • Random-projection estimator • Mean: , i.e. unbiased • Variance: as i.e. large variance if noise is not zero-mean

  23. PSNR vs. Packet Loss Rate (w/o Error Concealment)

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