In our simulations, scalable bitstreams are generated off-line, with Wyner-Ziv video coding using LDPC coded based bit plane coding for SWC. Each source is encoded into an emdedded bitstream only once and the operational R-D data are stored as look-up tables. The transmission “server” computes the rate allocation based on the R-D data, the channel condition and the target transmission rate. Then it generate the RS codes and packetized the truncated bitstream into equal-length packets. We choose the packet length to be 48 bytes.
One hundred frames are compressed with a frame rate of 30Hz. For each of these sequences, the first frame is coded as I frame, and all the subsequent frames as P frames by H.26L. The encoder separate the video sequence into a series of group
100 200 300 400 500 600 700 800 900 38 39 40 41 42 43 44 45 46 RATE(kb/s) PSNR(dB)
H.26L+Layered WZC with LDPC codes H.26L+Layered WZC with LDPC codes+UEP
Fig. 28. Performance of Wyner-Ziv video coding with RS codes over packet erasure channel with p = 0.20 for “Mother daughter”.
of frames (GOF), each of which contains 20 frames. Each GOF is then encoded into an embedded bit stream. For simplicity, we use the average R-D data for all GOFs. We assume that the base layer bitstream is transmitted losslessly and we only consider the transmission of the enhancement layer bitstream. We design the UEP scheme for the enhancement layer bitstream to generate a fixed number of equal- length packets according to the target transmission rate. The packet mean loss rate of the erasure channel is 0.20. The goal of the optimal UEP scheme was to maximize the expected PSNR. Thus, instead of the cost function (5.2), we used the objective function ΣL
i=0Pi(F )P SNRi(F ), where P SNRi(F ) is the PSNR corresponding to the first i syndrome layers. Fig. 27 represents the performance in terms of rate vs. average PSNR for “Foreman”. To achieve the same PSNR performance as in noiseless channel
case, an extra 0.12 b/s is required to provide erasure protection. The performance for “Mother daughter” sequence is plotted in Fig. 28. The extra bit rate required to achieve the same performance as in noiseless channel case is 0.16 b/s.
CHAPTER VI
CONCLUSION
We have introduced the first practical layered video coder based on the Wyner-Ziv coding principle and addressed its error robustness. Treating a standard coded video as the base layer (or side information), a layered Wyner-Ziv bitstream of the original video sequence is generated to enhance the base layer such that it is still decodable with commensurate qualities at rates corresponding to layer boundaries. The simple Wyner-Ziv video encoder consists of the DCT, NSQ and LDPC codes based bit plane coding for SWC. Our proposed system is similar to FGS coding in the sense of an embedded enhancement layer with good R-D performance. However, the main motivation of FGS coding is scalable coding for error robustness while assuming that the base layer (with heavy error protection) is always available loselessly at the decoder/receiver. With Wyner-Ziv coding, this stringent requirement can be loosened somewhat as an error-concealed version of the base layer can still be used in the joint Wyner-Ziv decoder. Experiment results show the superior error-robustness of Wyner- Ziv video coding with noisy side information at the decoder over FGS coding .
For Wyner-Ziv coding over discrete noisy channels, additional channel coding is needed to protect the Wyner-Ziv bitstream from channel errors. WZC can be thought of as a quantizer followed by SWC, which can be viewed as a channel coding problem. The channel coding component used for error protection can be combined with the SWC component in a joint design. We propose a JSCC framework for Wyner-Ziv video coding over BSC based on the idea of two equivalent channels [23]. Compared with the traditional video transmission systems which use feedback and retransmission of lost packets based on FEC, our system enables one integrated design of both the systematic part and the parity part of the IRA codes for different channel conditions
and provides good R-D performance.
For video streaming applications where the transmission channel is packet based, a Wyner-Ziv video coding scheme using UEP of embedded data for packet erasure channel is presented. UEP generates distortion optimal packing scheme that mini- mizes the expected distortion at a target transmission rate. With a combined design of WZC and UEP, we obtain an efficient video coding and transmission system at target transmission rate over packet erasure channels.
These initial results are encouraging, but much remains to be done. For example, as stated in chapter III, more accurate statistical distribution modeling is necessary to improve the code design for WZC. Capacity achieving channel code that performs well with short block length is desired for SWC. Finally, our current implementation of layered Wyner-Ziv coding only allows decoding at layer boundaries – decoding at the middle of a layer (bit plane) will suffer a huge performance loss as unavailable bits in the layer have to be treated as erasures. This is due to the limitation of the LDPC/IRA codes used for SWC. Progressively decodable channel (or Slepian-Wolf) codes such as Tornado codes [64] should be studied to allow fine granularity scalability of Wyner-Ziv video coding.
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APPENDIX A
THE DETAILS OF IMPLEMENTATION IN CHAPTER III.
Table II. The theoretical rate limit and the actual LDPC code rate used for each bit plane after the NSQ of the DC component (a) and the first two AC components (b, c) of the DCT coefficients.
ith bit plane H(Bi|Y, Bi−1, . . . , B0) LDPC code rate
0 0.03433 0.94
1 0.03884 0.94
2 0.10157 0.85
3 0.19593 0.73
(a)
ith bit plane H(Bi|Y, Bi−1, . . . , B0) LDPC code rate
0 0.00178 0.99
1 0.03768 0.94
2 0.02532 0.96
3 0.16754 0.77
(b)
ith bit plane H(Bi|Y, Bi−1, . . . , B0) LDPC code rate
0 0.02785 0.95
1 0.00488 0.98
2 0.03240 0.94
3 0.44291 0.49
Table III. LDPC code profiles. λ(x) = ΣJ i=2λixi−1, ρ(x) = 0.5xα−1+ 0.5xα. rate 0.50 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 λ2 0.2086 0.2416 0.2163 0.2199 0.2002 0.2282 0.2076 0.2392 0.2392 0.2444 λ3 0.1448 0.1800 0.1555 0.1574 0.1495 0.1800 0.1601 0.1915 0.1915 0.1925 λ4 0.0177 0.0388 λ5 0.1302 0.0683 0.0162 0.0947 0.1048 0.0201 0.0201 0.0782 λ6 0.2406 0.0534 0.2074 0.2271 0.2271 0.1554 λ7 0.0123 0.0942 0.2350 0.0518 λ8 0.1154 0.1958 λ9 0.1495 0.0788 λ10 0.0454 0.0974 λ14 0.2336 λ15 0.0824 0.0824 0.0960 λ16 0.2397 0.2397 λ17 0.0947 λ18 0.2308 0.1230 λ19 0.2095 λ21 0.0249 λ22 0.3078 λ23 0.2110 0.0542 λ24 0.1216 0.2775 λ27 0.0093 λ28 0.3215 0.3168 α 9 8 9 9 10 9 10 9 9 9 (a)
rate 0.60 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 λ2 0.2576 0.1901 0.2394 0.2205 0.2256 0.2103 0.2154 0.2029 0.1927 0.1845 λ3 0.1916 0.1567 0.1940 0.1937 0.1957 0.1915 0.1958 0.1901 0.1830 0.1723 λ4 0.0423 λ5 0.1388 0.0882 0.1381 0.0443 0.0013 λ6 0.0480 0.0754 0.2374 0.1946 0.1838 0.2470 0.1627 0.1516 0.2284 λ7 0.0139 0.0790 0.1027 0.1070 λ9 0.1784 λ10 0.0002 λ11 0.2987 λ12 0.0709 λ13 0.2832 λ14 0.0699 λ15 0.3096 λ16 0.1378 0.0302 0.2345 λ17 0.1967 0.1074 λ18 0.3354 0.0839 λ19 0.2577 λ20 0.3656 0.4135 λ26 0.1578 λ27 0.1804 α 9 12 10 11 11 12 12 13 14 15 (b) rate 0.70 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 λ2 0.1892 0.2116 0.2190 0.2103 0.2382 0.2296 0.2377 0.2313 0.2266 0.2348 λ3 0.1855 0.2054 0.2080 0.2062 0.2101 0.2101 0.2272 0.2201 0.2117 0.2519 λ4 0.1213 0.0873 0.1006 0.1092 0.1235 0.0657 λ5 0.0192 0.1089 0.0615 0.0556 λ6 0.1213 0.2213 0.1047 0.1667 λ7 0.1474 λ8 0.0396 0.2656 λ9 0.2838 0.3949 0.3477 0.2199 0.1820 λ10 0.1466 0.4173 0.0917 0.2183 λ13 0.3593 λ14 0.3002 λ15 0.3425 0.0551 λ20 0.1692 λ21 0.1874 α 15 14 14 15 14 15 15 16 17 17 (c)
rate 0.80 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 λ2 0.2056 0.2291 0.1852 0.1866 0.2196 0.2137 0.2100 0.2006 0.2275 0.2462 λ3 0.2211 0.2556 0.2141 0.2204 0.2562 0.2482 0.2431 0.2480 0.3173 0.3185 λ4 0.0626 0.0673 0.0795 0.0906 0.0201 λ5 0.1342 0.1099 λ6 0.0549 0.2374 0.2352 0.3586 λ7 0.3258 0.0767 λ8 0.2620 0.1967 0.0695 0.1294 λ9 0.1906 0.2601 0.3889 0.4485 λ10 0.0078 0.4214 λ14 0.1000 λ15 0.2373 0.2577 λ16 0.1260 λ20 0.3843 α 20 19 24 25 23 25 27 30 29 30 (d)
VITA
Qian Xu was born in Jingzhou, China in 1982. She received the B.S. degree in Special Class for the Gifted Young from University of Science&Technology of China in 2002 and M.S. degrees in electrical engineering from Texas A&M University in 2004. She is currently a Ph.D. student under Dr. Zixiang Xiong in the Wireless Communications Laboratory at Texas A&M University.
Upon admittance to Texas A&M, Ms. Xu was a recipient of the TxTEC Fellow- ship in September 2002. From 2001 to 2002, she worked as a student intern in the multimedia group at Microsoft Research Asia, Beijing, China.
Her areas of interest include distributed video and stereo coding, video compres- sion and digital communication.
Ms. Xu can be reached by the following address:
Jiangling Middle School, Jingzhou City, Hubei Province,
P.R.China, 434100