Joint Source-Channel Coding for Image Transmission over Flat Fading Channels

Transcription

1 Joint Source-Channel Coding for Image Transmission over Flat Fading Channels Thesis presentation Greg Håkonsen 29/6-2007

2 Outline Motivation Communication basics Source coding Channel coding Combination Results Conclusion

3 Motivation Problem with wireless image transmission. Need to squeeze image through band limited channel. Limited power. Low delay. Want little complexity.

4 Source coding Image compression: Want to represent the image with as little resources as possible, with as little distortion as possible. Irrelevancy, human eye not perfect. Remove statistical dependency, signal can be predicted. Lossy compression, reduce information. A must for analog sources.

5 Source coding cont d Information reduction can be done quick and dirty...

6 Source coding cont d or a bit smarter...

7 Impact of errors

8 Fading channels Transmitted signal can experience reflection, scattering and diffraction due to obstacles such as buildings, terrain etc. Signal components might be delayed, attenuated and shifted in phase at receiver. Constructive/destructive components fluctuations in received power.

9 Fading channels cont d x 10 3 γ (db) p(γ) Time (channel samples) x γ Channel signal-to-noise ratio(csnr), γ = 20 db.

10 Channel codes Need to add redundancy to protect information against channel noise, while using the channel bandwidth efficiently. Time

11 Joint source-channel coding Claude Shannon proved that separate design of source and channel codes can give an overall optimal system. Infinite complexity and delay. A joint approach might give a better overall system performance when delay and complexity are considered. Joint source-channel coding (JSCC) takes information about both source and channel into account.

12 Multimedia wireless transmission Digital vs analog: Analog systems: Robust, no clear breakdown. Can track channel quality. Inefficient use of bandwidth. Low compression. Digital systems: Compression possible, use little bandwidth. Can experience sharp breakdown. Our goal: To join the best of the two worlds.

13 Proposed system Source image x(i, j) Analysis filter bank x(k) Preallocation/ Classification Adaptive mapping allocation Mapping 1. Mapping J Transmitter g(k) Power s(k) Control σs(γ) 2 Sideinfo CSI n(k) Channel α(k) CSI Receiver y(k) Perfect channel estimator Channel gain mismatch equalizer ĝ(k) Demapping ˆx(k) Synthesis filter bank ˆx(i, j) Decoded image Sideinfo Fading channel, source prepared for transmission. Map source samples into channel space through nonlinear mappings. Channel samples sent as time discrete amplitude continuous PAM symbols.

14 Mapping example Shannon, Kotel nikov Optimized for a CSNR Limited set ˆr j {0, 1 4, 1 2, 2, 1, 2} Give different protection Rate given in channel/source samples

15 Robustness SNR (db) 15 SNR (db) CSNR (db) CSNR (db) ˆr = 1 2 ˆr = 2.0 Figures optimized for γ = {10, 20, 30} db.

16 Technique to increase rate Increase rate by splitting CSNR range into regions and use separate settings within each region. Traditional systems use lower threshold in a region as value to code for. Can be increased with power allocation. Robustness of mappings means that design is more free. Outage * γ C1 γ T0 γ T1... * * * * γcm γ C2 γ T2 γ C3 γ CM 1 γ TM 1

17 Adapt to channel gain Assume that complete channel state information (CSI) is available. Possible to invert channel gain within each channel region to maintain fixed CSNR at receiver. Requires much CSI. Rely on robustness of mappings: No gain adaptation. Use single factor per channel region. Receiver can partly compensate for channel gain mismatch.

18 Results, parameters Parameter Symbol Value Carrier frequency f c 2.0 GHz Symbol duration T s 4 µs Doppler shift f m 100 Hz Statistical sample size D 2000 Mobile velocity v 15 m/s

19 Results, robustness No channel information at either end. γ = 15 db, r avg = 0.5.

20 ravg t PSNRt (db) NTNU Results, spread True CSNR, γ t (db) True CSNR, γ t (db) Goldhill ravg = {0.5(blue), 0.1(green)} Four regions with transmission + Outage 34 38

21 Results, preallocation Representation points, γcm (db) Mapping rate, ˆrj SNR (db) Sorted blocknumber Sorted blocknumber Preallocation and example of received SNR Four regions + outage, γ = 6 db, r avg = 0.5.

22 Results, preallocation Representation points, γcm (db) Mapping rate, ˆrj SNR (db) Sorted blocknumber Sorted blocknumber Preallocation and example of received SNR, each block coded for given CSNR. Four regions + outage, γ = 6 db, r avg = 0.5.

23 Image examples r avg = 0.1, γ = 10 db. Reference system: JPEG2000 coded for bits/pixel R s, given by R s = r avg R c, where R c is the rate of the channel system for a given γ. Reference systems: 1. Using set of AWGN capacity achieving codes, infinite power adjustment. 2. Using Turbo Coded Modulation (TuCM). Varying transmit power, constellation size and turbo code.

24 Image examples P: PSNR = 31.1 db 1: PSNR = 31.5 db 2: PSNR = 30.4 db

25 Image examples P: PSNR = 24.0 db 1: PSNR = 23.7 db 2: PSNR = 23.1 db

26 Conclusion Presented a system that transmits images over a wireless channel using nonlinear mappings. Robust, with graceful degradation. Can track channel changes. Little loss in performance when reducing amount of channel information. High adaption flexibility.

27 ? Questions?