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FEC and RDO in SVC

FEC and RDO in SVC. Thomas Wiegand. Outline. Introduction SVC Bit-Stream Raptor Codes Layer-Aware FEC Simulation Results Linear Signal Model Description of the Algorithm Experimental Results. Introduction.

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FEC and RDO in SVC

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  1. FEC and RDO in SVC Thomas Wiegand

  2. Outline • Introduction • SVC Bit-Stream • Raptor Codes • Layer-Aware FEC • Simulation Results • Linear Signal Model • Description of the Algorithm • Experimental Results

  3. Introduction • C. Hellge, T. Schierl, and T. Wiegand, “RECEIVER DRIVEN LAYERED MULTICAST WITH LAYER-AWARE FORWARD ERROR CORRECTION,” ICIP 2008. • C. Hellge, T. Schierl, and T. Wiegand, “MOBILE TV USING SCALABLE VIDEO CODING AND LAYER-AWARE FORWARD ERROR CORRECTION,” ICME 2008. • C. Hellge, T. Schierl, and T. Wiegand, “Multidimensional Layered Forward Error Correction using Rateless Codes,” ICC 2008. • M. Winken, H. Schwarz, and T. Wiegand, “JOING RATE-DISTORTION OPTIMIZATION OF TRANSFORM COEFFICIENTS FOR SPATIAL SCALABLE VIDEO CODING USING SVC,” ICIP 2008.

  4. SVC Bit-Stream • Spatial-temporal-quality cube of SVC http://vc.cs.nthu.edu.tw/home/paper/codfiles/kcyang/200710100050/Overview_of_the_Scalable_Video_Coding_Extension_of_the_H.264.ppt

  5. RECEIVER DRIVEN LAYERED MULTICAST WITH LAYER-AWARE FORWARD ERROR CORRECTION C. Hellge, T. Schierl, and T. Wiegand ICIP 2008 C. Hellge, T. Schierl, and T. Wiegand, “MOBILE TV USING SCALABLE VIDEO CODING AND LAYER-AWARE FORWARD ERROR CORRECTION,” ICME 2008. C. Hellge, T. Schierl, and T. Wiegand, “Multidimensional Layered Forward Error Correction using Rateless Codes,” ICC 2008.

  6. SVC Bit-Stream • Equal FEC

  7. Raptor Codes (1/2) SSs PSs PSs ESs 0 1 5 5 2 4 4 3 0 0 0 4 Gp = GLT = 1 1 1 5 2 2 2 6 3 3 3 7 precoding process LT coding process Non-systematic Raptor codes

  8. Raptor Codes (2/2) k … 0 0 n-1 Gp GLT 0 = = 0 … … … … k-1 p-1 p-1 k-1 unknown ? GLT’’ 0  = 0 … k GLT’ … p k s p-1 k-1 s Gp I unknown k GLT’ p 0 s 0 GpSys  = … 0 k … • Systematic Raptor codes • Construction of pre-code symbols • GLT , Gp, and SSs. • GpSys = • Solving p-1 k-1

  9. Layer-Aware FEC (1/5) • Example 1 • Example 2

  10. Layer-Aware FEC (2/5) • Encoding process • Example 3

  11. Layer-Aware FEC (3/5) • Decoding process • Example 4

  12. Layer-Aware FEC (4/5) PSs0 PSs1 PSs2 … PSsm ESs0 ESs1 ESs2 … ESsm GLayeredLT(m) = GLayeredLT(m) = [G*LT0 | G*LT1 | … | GLTm]

  13. Layer-Aware FEC (5/5) 0 ESs0 0 ESs1 … 0 ESsm PSs0 PSs1 PSs2 … PSsm GpSysLayered(m) = 0 SS0 0 SS1 0 GpSysLayered(m)

  14. Simulation Results (1/2) • QVGA (BL) and VGA (EL) resolution using SVC over a DVB-H channel. • JSVM 8.8 • GOP size = 16 • Size of a transmission block = 186 bytes • Mean error burst length = 100 TBs

  15. Simulation Results (2/2)

  16. Joint Rate-Distortion Optimization of Transform Coefficients For Spatial Scalable Video Coding Using SVC M. Winken, H. Schwarz, and T. Wiegand ICIP 2008

  17. Hybrid Video Decoding s5 s6 s7 s8 s2 s3 ½ (s2+s3) sx u5 u6 u7 u8 1 2 5 6 = + 3 4 7 8 Decoded pixel values Motion compensated values Dequantized residual Motion compensation iQ and iDCT Exception 0 0 0 0 c5 c6 c7 c8 s1 s2 s3 s4 s5 s6 s7 s8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 ½½ 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 s1 s2 s3 s4 s5 s6 s7 s8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ? ? ? ? 0 0 0 0 ? ? ? ? 0 0 0 0 ? ? ? ? 0 0 0 0 ? ? ? ? s1 s2 s3 s4 0 0 0 sx = + + Motion vectors DeQuntized and iDCT parameters

  18. Linear Signal Model (1/6) WH s11 … s1WH … sK1 … sKWH s11 … s1WH … sK1 … sKWH c11 … c1WH … cK1 … cKWH p11 … p1WH … pK1 … pKWH WH = + + WH 1 K K+1 WH • Linear signal model for K inter frames • s = Ms + Tc + p • s: A (KWH)1 vector of decoded signal • M: A (KWH)(KWH) matrix of motion parameters • T: A (KWH)(KWH) matrix of inverse quantization and DCT parameters • c: A (KWH)1 vector of received transform coefficients • p: A (KWH)1 intra signal or motion parameters outside s

  19. Linear Signal Model (2/6) • Optimal transform coefficients selection • Decoder receives MVs (M) and quantized transform coefficients (c). • fixed motion parameters (M), quantization parameters (T), and intra predictions (p). • Rate and distortion are mainly controlled by c. • c’ = argminc{D(c) + R(c)} subject to s = Ms + Tc + p • D(c) = ||x - s||22, R(c) = ||c||1

  20. Linear Signal Model (3/6) • Optimal transform coefficients selection • Problem: MVs cannot be determined before the transform coefficients are selected (trade-off) • Solution: s11 … s1WH s21 … s2WH s31 … s3WH … sK1 … sKWH s11 … s1WH s21 … s2WH s31 … s3WH … sK1 … sKWH c11 … c1WH c21 … c2WH c31 … c3WH … cK1 … cKWH p11 … p1WH p21 … p2WH p31 … p3WH … pK1 … pKWH initial fixed initial = + + fixed initial initial

  21. Linear Signal Model (4/6) • Optimal transform coefficients • Problem size: K W  H • Sliding window approach (Reduce problem size) s = M s + T c + p window size step size

  22. Linear Signal Model (5/6) • Extension for spatial scalability • s0 = M0s0 + T0c0 + p0 • s1 = M1s1 + T1c1 + p1 + Bs0 + RT0c0 Inter-layer motion prediction Inter-layer residual prediction texture Hierarchical MCP & Intra-prediction Base layer coding Multiplex Scalable bit-stream motion • Inter-layer prediction • Intra • Motion • Residual Spatial decimation H.264/AVC compatible base layer bit-stream texture H.264/AVC MCP & Intra-prediction Base layer coding motion H.264/AVC compatible coder

  23. Linear Signal Model (6/6) • Optimal transform coefficients in spatial scalability • c0’ D0(c0) + 0R(c0) c1’ D1(c0,c1) + 1(R(c0)+R(c1)) subject to s0 = M0s0 + T0c0 + p0 s1 = M1s1 + T1c1 + p1 + Bs0 + RT0c0 c0’ (1-w)(D0(c0) + 0R(c0)) + c1’ w(D1(c0,c1) + 1(R(c0)+R(c1))) where  = (W1H1)/(W0H0) = argminc0’c1’ = argminc0’c1’

  24. Description of the Algorithm • Determine M0, T0, M1, T1, B, p0, R, and p1 by encoding the first K pictures using SVC reference encoder model. • Solve optimization to determine c0 of the base layer. • Based on new c0, determine B and R again. • Solve optimization problem for only the enhancement layer.

  25. Experimental Results (1/2) • JSVM 9.9 • IPPP • QCIF (base layer) and CIF (enhancement layer) • CABAC • QP difference: 3 • Sliding windows size: 55 for base layer and 1010 for enhancement layer

  26. Experimental Results (2/2)

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