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Generalized Unequal Error Protection LT Codes for Progressive Data Transmission

Generalized Unequal Error Protection LT Codes for Progressive Data Transmission. Suayb S. Arslan , Student Member, IEEE, Pamela C. Cosman , Fellow, IEEE, and Laurence B. Milstein, Fellow, IEEE. Outline. Introduction Background UEP DF Code Designs

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Generalized Unequal Error Protection LT Codes for Progressive Data Transmission

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  1. Generalized Unequal Error Protection LT Codes for Progressive Data Transmission Suayb S. Arslan, Student Member, IEEE, Pamela C. Cosman, Fellow, IEEE, and Laurence B. Milstein, Fellow, IEEE

  2. Outline • Introduction • Background • UEP DF Code Designs • UEP GENERALIZED LT (UEP GLT) CODING • Generalization of “weighted approach” • Generalization of EWF codes • Progressive source transmission system description • Optimization • Numerical Results • Comparisons with the “weighted approach” • Comparisons with UEP EWF codes • Conclusion

  3. Introduction(1/2) • UEP(Unequal Error Protection) • Some source symbols are more important than others. • URT(Unequal Recovery Time) • The more important section can be recovered earlier in time. • UIT(Unequal Iteration Time) • Evaluate system performance as a function of the iteration index of the decoding algorithm.

  4. Introduction(2/2) • Introduce a systematic degree-dependent selection concept. • Tailor the parameters of the proposed design to get dramatic improvements in expected distortion. • Apply the generalized LT codes to a progressive source and show that it has better UEP properties than other published results in the literature.

  5. Background • Progressive Source Coding • The beginning part of the bit stream is more important than the succeeding parts of the bit stream. • In progressive source transmission, it is of more concern to consider the decoded useful bits rather than the decoded total bits. • Fountain Codes

  6. UEP DF Code Designs(1/2) • Weighted Approach • 1) choose degree according to some degree distribution(DD). • 2) for( i = 1 to i = ) • A) choose the set from {,, …, } with probability . • B) select input symbol uniformly from the set without replacement. • 3) XOR input symbols. k source symbols = …, and = i j. | | = k is an integer, where 0 < < 1 and = 1.

  7. UEP DF Code Designs(2/2) • Expanding Window Fountain Codes • 1) randomly choose a window according to a window selection distribution(SD).—definition 3 • 2) LT coding is applied only to the bits contained in that window using a suitably chosen degree distribution.—definition 4 k source symbols = …, and = i j. | | = k is an integer, where 0 < < 1 and = 1. r embedded windows such that = .

  8. UEP GENERALIZED LT (UEP GLT) CODING • Apply the degree-dependent selection idea to provide increased UEP, URT and UIT properties. • Generalization of “weighted approach” • Generalization of EWF codes

  9. Generalization of “weighted approach”(1/3) : degree distribution vector Parameter size = (r-1)k+k-1

  10. Generalization of “weighted approach”(2/3)

  11. Generalization of “weighted approach”(3/3) • The unequal protectionsachieved by allowing coded symbols to make more edge connections with more important information sets. • It is beneficial to have low degree check nodes generally make edge connections with important information sets.

  12. Generalization of EWF codes

  13. Generalization of EWF codes

  14. Progressive source transmission system description

  15. Progressive source transmission system description

  16. Optimization(1/3) • Design criterion • minimize the average distortion as equation (3). • To reduce the number of optimization parameters • Choose SD to be an exponential function of the degree number.

  17. Optimization(2/3) Parameter size = (r-1)k+k-1 Parameter size = 3(r-1) +k-1

  18. Optimization(3/3)

  19. Numerical Results • Use standard 512*512 Lena and 512*512 Goldhill images. • B = 50000 bits • Run all realization times • 2 different values for k: k=100 and k=1000. • Set r = 2 and + = 1. • Use the RSD with = c = 0.01.

  20. Comparisons with the “weighted approach”

  21. Comparisons with the “weighted approach”

  22. Comparisons with the “weighted approach”

  23. Comparisons with the “weighted approach”

  24. Comparisons with the “weighted approach”

  25. Comparisons with the “weighted approach”

  26. Truncated RSD [17] Comparisons with UEP EWF codes [17] D. Sejdinovic, D. Vukobratovic, A. Doufexi, V. Senk and R. Piechocki, “Expanding window Fountain codes for Unequal Error Protection”, IEEE Trans. Commun., Vol. 57, No. 9, pp. 2510–2516, Sep. 2007.

  27. Comparisons with UEP EWF codes • GLTexp：This scheme uses the Exponential SD with and optimizes the set { , }so that the proposed scheme achieves minimum distortion. • GLTexpOpt：This scheme uses the Exponential SD with. It optimizes the set { , , ,}sothat the proposed scheme achieves minimum distortion. • GLTexpFullOpt：This scheme uses the Exponential SD and optimizes the whole set of parameters{ , , , , }so that the proposed scheme achieves minimum distortion. As increasing the parameter space，we observe dramatic improvements in a progressive transmission scenario.

  28. Comparisons with UEP EWF codes

  29. Comparisons with UEP EWF codes

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