1. BITS Pilani Pilani Campus EEE G612 Coding Theory and Practice SONU BALIYAN 2017H1240098P

2. BITS Pilani, Pilani Campus Distributed Video Coding SONU BALIYAN ID – 2017H1240098P

3. BITS Pilani, Pilani Campus Presentation Contents 3 Idea of Distributed Video Coding (DVC) Contrast to traditional video coding Advantages/Applications Theoretical foundations of DVC Slepian-Wolf coding Wyner-Ziv coding Practical implementation of DVC Pixel domain encoding Transform domain encoding Joint decoding and motion estimation Rate control Robust Video Transmission

4. BITS Pilani, Pilani Campus Idea of Distributed Video Coding 4 Traditional video coding (MPEG, H.26x) Encoder exploits statistical dependencies of source Intra-frame coding: spatial correlation within a frame Inter-frame coding: temporal correlation between frames  for low data rate highly complex encoders necessary (transform domain coding, motion compensation,…)  decoder is generally simple Well suited for broadcasting: video is encoded once and decoded often

5. BITS Pilani, Pilani Campus Idea of Distributed Video Coding 5 Slepian-Wolf and Wyner-Ziv theory: Efficient compression can be achieved by exploiting source statistics at the decoder only Distributed video coding idea: Encode individual frames independently Decode them conditionally by using previously decoded frames as “side information”  Combine simple intra-frame encoding with complex motion compensated decoding  Shift complexity to decoder

6. BITS Pilani, Pilani Campus DVC - applications For many applications encoder complexity is prohibitive Mobile video, wireless PC cameras,… DVC shifts complex tasks to the infrastructure: By using transcoders decoding becomes also simple for mobile devices 6

7. BITS Pilani, Pilani Campus Theoretical Foundations: Slepian-Wolf coding Slepian-Wolf theory for lossless distributed source coding 2 dependent sources X,Y Encoded independently Decoded jointly Slepian-Wolf established rate region: Sum rate equal to joint encoding 7

8. BITS Pilani, Pilani Campus Theoretical Foundations: Slepian-Wolf coding Slepian-Wolf coding – plausibility Consider a point near the corner Source coding theory: Y can be decoded error free Interpret joint pdf of X,Y as a DMC Channel coding theory: observing y a code exists that allows us to length n x sequences xsequences are likely to occur distinguish There are many such codes: N Typicality: Encoding of X: find the first of the N codes that contains X and transmit it’s index  Number of necessary codes (assuming disjoint codes)  Receiver knows the code and can decode x from y error free 8

9. BITS Pilani, Pilani Campus Slepian-Wolf coding in video encoding Lossless compression with receiver side information Special case of distributed coding e.g. X is the current frame Y are previous frames Same rate possible as in conventional video encoding 9

10. BITS Pilani, Pilani Campus Theoretical Foundations: Wyner-Ziv coding Wyner-Ziv theory for lossy distributed source coding Extension of Slepian-Wolf coding for lossy compression with side information at the decoder In most cases lossless compression rates are too high  rate reduction by quantization Rate is general higher as for a conventional system (rate increase < 0.5 bit per sample) Special case: Gaussian memoryless source with mean-squared error distortion  no rate loss 10

11. BITS Pilani, Pilani Campus Theoretical Foundations: Wyner-Ziv coding Practical implementation Cascading a Quantizer and a Slepian-Wolf encoder  Quantizer reduces source rate to that supported by the Slepian-Wolf code

12. BITS Pilani, Pilani Campus Practical Implementation of DVC Pixel domain encoding Combination of intraframe encoder and interframe decoder

13. BITS Pilani, Pilani Campus Pixel Domain Encoding Video sequence is partitioned into Key frames K: conventionally DCT coded frames, used as references for motion compensation and to stop error propagation (c.f. I,P frames) Wyner-Ziv frames S: pixels uniformly quantized and Slepian-Wolf encoded Decoder: Generates side information Ŝ by extrapolation of previously decoded frames Assumes Laplacian distribution of difference between individual pixel values of S and Ŝ Turbo decoder combines side information and received parity bits “Request and decode”: if not possible to decode reliably  request additional parity bits via feedback channel Reconstruction: calculates MMSE reconstruction of S given side information and parity bits

14. BITS Pilani, Pilani Campus Transform Domain Encoding To increase rate distortion performance conventional source coding schemes employ orthonormal transforms DCT can also be used in DVC

15. BITS Pilani, Pilani Campus Transform Domain Encoding Encoder DCT applied to each Wyner-Ziv frame Transform coefficients indepentently quantized and coded Decoder Obtains side information from previously detected frames Side information is also transformed Turbo decoder reconstructs transform coefficients independently Spatial transform exploits statistical dependencies within a frame  performance gain compared to pixel domain coder (~2 dB) Complexity increases slightly; comparable to conventional intraframe coding

16. BITS Pilani, Pilani Campus Performance Comparison DCT codec: all frames are I (intra) frames WZ pixel codec: every fourth frame is a key frame WZ DCT codec: 4x4 DCT applied H.263: I-B-B-B-I predictive structure; B… bidirectional

17. BITS Pilani, Pilani Campus Benefits of DVC Pixel domain encoding Very low encoder complexity No DCT/IDCT No motion estimation/compensation Better performance than conventional intraframe coding at lower complexity Transform domain encoding Still low encoder complexity No motion estimation/compensation Comparable to conventional intraframe coding Performance gain due to exploitation of spatial correlation

18. BITS Pilani, Pilani Campus Joint Decoding and Motion Estimation For high compression efficiency motion must be estimated at the decoder In conventional system motion vector can be obtained by directly comparing frames Not possible in DVC because the current frame is not known, it needs to be decoded first  joint decoding and motion estimation necessary Performance gain possible by allowing the encoder to aid the decoder in motion estimation

19. BITS Pilani, Pilani Campus Encoder Aided Motion Estimation Simple example: encoder sends a robust hash code word Hash consists of a small subset of DCT coefficients Encoder : Stores hashes of all blocks of previous frame Compares hashes of previous frame with current frame If distance exceeds a threshold, new hashes together with Wyner-Ziv bits are sent Otherwise encoder lets the decoder repeat previous block Decoder: If new hash: perform motion search to generate side information that produces hash with smallest distance

20. BITS Pilani, Pilani Campus Encoder Aided Motion Estimation - Performance H.263 I-P-P codec; P… predictive WZ DCT codec with different amount of key frames Distance to interframe encoding decreases at low rate

21. BITS Pilani, Pilani Campus Rate Control Required bit rate depends on correlation between side information and current frame Side information is exploited only at the decoder  encoder does not know necessary bit rate Solutions: Feedback from the decoder: “decode and request” Decoder requests additional bits if necessary Drawbacks: Feedback channel introduces latency Process has to be performed online Advantage: Encoder is fixed – Compression performance can be increased by only changing the decoder

22. BITS Pilani, Pilani Campus Rate Control Second solution: Allow simple temporal dependence estimation at the encoder – compare frame difference energy Classify blocks into several coding modes depending on difference energy Drawbacks: Larger encoder complexity Less flexible Advantages: No feedback channel necessary No online decoding required  suitable for storage applications

23. BITS Pilani, Pilani Campus Wyner-Ziv codec can not only be used to “correct errors” in the side information but also channel errors  video quality is much less effected than in conventional video coding Complement a conventional video codec by a WZ codec to achieve error resilience Wyner-Ziv bit stream uses coarser quantization than MPEG If errors occur decoded waveform S’ acts as side information for WZ decoder and delivers a waveform S* with maximum degradation limited by the WZ coder  Systematic lossy error protection (SLEP) Robust Video Transmission

24. BITS Pilani, Pilani Campus Systematic Lossy Error Protection Advantage Tradeoff between WZ bit rate and residual distortion allows graceful degradation with worsening channel error rates Conventional video codec does not change  compatible with already deployed systems Example: Conventional MPEG codec complemented with a coarser MPEG/RS encoder combination as WZ codec

25. BITS Pilani, Pilani Campus Conclusions Slepian-Wolf coding: efficient lossless compression by exploiting statistical dependencies at the decoder only possible without a rate loss Wyner-Ziv coding: extension to lossy compression; in general a rate loss is incurred Distributed video coding: keep encoder simple by exploiting these ideas Conventional techniques like DCT and motion compensation must be employed to reduce rates Rate control is difficult in DVC (feedback, simple temporal prediction) DVC can be used to achieve error resilience

26. BITS Pilani, Pilani Campus References B. Girod,A. Aaron, S. Rane, D. Rebollo-Monedero, “Distributed Video Coding,” Proceedings of the IEEE, Vol. 93, No. 1, January 1995. J. Slepian, J. Wolf, “Noiseless coding of correlated information sources,” IEEE Trans. Inf. Theory, Vol. 19, pp. 471-480, July 1973. A. Wyner, “Recent results in the Shannon theory,” IEEE Trans. Inf. Theory, Vol. 20, No. 1, pp. 2-10, Jan. 1974

27. BITS Pilani, Pilani Campus THANK YOU