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Fine Granularity Video Compression and Optimal FEC Assignment for FG Video Streaming over Burst Error Channel

Fine Granularity Video Compression and Optimal FEC Assignment for FG Video Streaming over Burst Error Channel . Yih-Ching Su Department of Computer Science and Engineering, National Sun Yat-Sen University. Contents. Introduction Gilbert Channel with Loss Rate Feedback

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Fine Granularity Video Compression and Optimal FEC Assignment for FG Video Streaming over Burst Error Channel

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  1. Fine Granularity Video Compression and Optimal FEC Assignment for FG Video Streaming over Burst Error Channel Yih-Ching Su Department of Computer Science and Engineering, National Sun Yat-Sen University

  2. Contents • Introduction • Gilbert Channel with Loss Rate Feedback • Optimal FEC Assignment for FG Video • HSDD Motion Estimation Metric • HMRME Motion Estimation Algorithm • ABEC Embedded Coder • Conclusions & Future Works

  3. 1. Introduction

  4. Research Focuses • Optimal FEC assignment scheme for FG video transmission over burst error channel (as wireless Internet) with or without loss rate feedback. • Wavelet domain video compression algorithms with high-performance or low-complexity features. Introduction

  5. Research Focuses (cont.) ABEC Source Coder Motion Estimation Transform Quantization & Entropy Coding Raw Video HSDD HMRME Channel Coder FEC Protection Error-Resilient Video Packets Optimal FEC Assignment Introduction

  6. arbitrary enhancement layer length (in bits) R max. min. (base layer length) Definition of Fine Granularity Video Stream • Bit stream is scalable (layered). • Rate can be precisely controlled. Introduction

  7. Merits of Fine Granularity Video Stream • Precise rate control • Bandwidth adaptation EL Client BL Media Server FG Video Encoder Heterogeneous Internet Environment EL FG BL EL Client No transcoding! BL Introduction

  8. EL EL BL Merits of Fine Granularity Video Stream (cont.) • Content-adaptive error protection BL Unequal Error Protection Equal Error Protection

  9. Fine Granularity Video Compression Systems DCT based: • MPEG-4 FGS • ISO/IEC 14496-2:2001/Amd 2:2002 • Base layer plus enhancement layer DWT based: • “Multirate 3-D subband coding of video”, D. Taubman et al., 1994. • “3D SPIHT”, B.-J. Kim et al., 2000. • “HSDD”, Y.-C. Su et al., 2003. Introduction

  10. 2. Gilbert Channel with Loss Rate Feedback

  11. : the probability of m lost packets within a block of n packets. Packet Loss • Packet loss can severely affect the quality of delay sensitive multimedia applications. • FEC (Forward Error Correction) technique can be used when delay time is strictly restricted. BOP len = n pkts data FEC redundancy data len = k pkts Gilbert Channel with Loss Rate Feedback

  12. Gilbert Channel Model • The ability of the application to react is enhanced by the availability of simple and efficient loss models. • A two state Markov model or Gilbert-model is often used to simulate burst loss patterns over wired/wireless channel. C. C. Tan, N. C. Beaulieu, ”On first-order Markov modeling for the rayleigh fading channel,” IEEE Commun., 2000. Gilbert Channel with Loss Rate Feedback

  13. feedback delay b mv m m2 m1 BOP-b-v+1 BOP-b-1 BOP-b BOP0 Enhanced Video Transmission over Gilbert Channel • Feedback loss rate. • Decide FEC protection ratio relying on a new probability function which is conditioned on loss rate feedback. Gilbert Channel with Loss Rate Feedback

  14. Renewal Error Process Gap probabilities: • Packet loss over Gilbert-model can be modeled with a renewal error process. • The lengths of consecutive inter-error intervals (also called gaps) are independently and identically distributed. Probability that m-1 packet losses occur in the next n-1 packets following an error: Probability that m packet losses occur within a block of n packets: E. N. Gilbert, "Capacity of a burst-noise channel," Bell Syst. Tech. J., vol.39, pp.1253-1265, Sept. 1960. E. O. Elliott, "A model of the switched telephone network for data communications," Bell Syst. Tech. J., 1965. Gilbert Channel with Loss Rate Feedback

  15. n-1 E E#(m-1) E n-1 E E#(m-1) S Probability Toolbox Gilbert Channel with Loss Rate Feedback

  16. n-1 n-1 S S#(m-1) S S#(m-1) S n-1 S S#(m-1) E Probability Toolbox (cont.)

  17. Probability Toolbox (cont.) n E E n E S n S S n S E Gilbert Channel with Loss Rate Feedback

  18. n-1 S E#(m) S n-1 n-1 n-1 E E S E#(m-1) E#(m-1) E#(m) E E S Probability Toolbox (cont.)

  19. BOP-b-v+1 BOP-b-i BOP-b Iterative Equation Set Gilbert Channel with Loss Rate Feedback

  20. Initial Conditions Gilbert Channel with Loss Rate Feedback

  21. Conditional Probability Function Gilbert Channel with Loss Rate Feedback

  22. Validation of Correctness Gilbert Channel with Loss Rate Feedback

  23. Performance Evaluation Gilbert Channel with Loss Rate Feedback

  24. 3. Optimal FEC Assignment for FG Video

  25. B. Hong and A. Nostratinia, "Rate-constrained scalable video transmission over the internet," Packet Video 2002. Y.C. Su, C.S. Yang, and C.W. Lee, "Optimal FEC Assignment for Scalable Video Transmission over Burst Error Channel with Loss Rate Feedback," Packet Video 2003. FEC Assignment Schemes • Equal error protection • Content-adaptive unequal error protection • Content-adaptive plus channel-adaptive unequal error protection Optimal FEC Assignment for FG Video

  26. Layer 0 Layer 1 Layer i Layer l packet k0 k1 ki kl • number • of • packets • n FEC overhead s1 si s0 sl packet size s Block of Packets (BOP) Structure

  27. Complete Expected Quality Optimal FEC Assignment for FG Video

  28. content adaptive content+channel adaptive Simplified Expected Quality Optimal FEC Assignment for FG Video

  29. The Optimization Problem Constrained by Optimal FEC Assignment for FG Video

  30. Dynamic Programming Optimal FEC Assignment for FG Video

  31. Validation of Correctness (i) frame resolution = CIF format (352x288) (ii) constant stream rate = 256 Kbps (iii) 1 GOP = 1 intra frame accompanied with 14 inter frames and frame rate = 15 fps (iv) sequence length = 9 GOPs Optimal FEC Assignment for FG Video

  32. Performance Discrepancy between Complete & Simplified Models Optimal FEC Assignment for FG Video

  33. Performance Evaluation Optimal FEC Assignment for FG Video

  34. Performance Evaluation (cont.) Optimal FEC Assignment for FG Video

  35. Performance Evaluation (cont.) Optimal FEC Assignment for FG Video

  36. Performance Evaluation (cont.) Optimal FEC Assignment for FG Video

  37. Performance Evaluation (cont.) Optimal FEC Assignment for FG Video

  38. 4. HSDD Motion Estimation Metric

  39. Bit-Plane Coding • The Core of FGS or Embedded Coder Just bit-plane coding! HSDD Motion Estimation Metric

  40. Zero-Tree Coding • Natural images in general have a low pass spectrum. • Large wavelet coefficients are more important than small wavelet coefficients. • A zero-tree is a quad-tree of which all nodes are equal to or smaller than the root. HSDD Motion Estimation Metric

  41. Hierarchical Sum of Double Difference Metric • Zero-tree coding aware • Jointly constrain motion vector searching for both temporal and spatial (quad-tree) directions • Fewer bits are spent later for describing isolated zeros HSDD Motion Estimation Metric

  42. Reference Block 2p+1 2p+1 Current Block Sum of Absolute Difference Metric Current block's pixel (block size nxn) Reference block's pixel within search area (2p+1)x(2p+1) SAD metric conflicts with the zerotree rule often, because the goal of SAD metric is just to minimize the temporal difference, and it is irrelevant to the magnitude hierarchy of the spatial quad-trees. HSDD Motion Estimation Metric

  43. Double Difference Hierarchy Sum Corresponding parent pixel information in the upper levelof motion compensation pyramid HSDD Metric Calculation Current block's pixel (block size nxn) Reference block's pixel within search area (2p+1)x(2p+1) HSDD Motion Estimation Metric

  44. Observations on HSDD Metric • HSDD value may be negative, but a larger positive one is preferred. • Given any parent pixel information, the maximal HSDD(MV) occurs if and only if the perfect SAD matching exists, that is SAD(MV)->0. HSDD Motion Estimation Metric

  45. Motion Estimation Applying HSDD Metric HSDD Motion Estimation Metric

  46. Layered Magnitude Distributions for HSDD & SAD HSDD Motion Estimation Metric

  47. Performance Evaluation HSDD Motion Estimation Metric

  48. 5. HMRME Motion Estimation Algorithm

  49. Half-Pixel Multi-Resolution Motion Estimation • Combine transform-adapted half-pixel interpolation with anti-aliasing under complexity constraints. • Avoid multiple inverse transforms. • Can be united with the conventional wavelet domain motion estimation algorithms. HMRME Motion Estimation Algorithm

  50. H-Transform h = H a HMRME Motion Estimation Algorithm

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