1. A hardware-Friendly Wavelet Entropy Codec for Scalable video Hendrik Eeckhaut ELIS-PARIS Ghent University Belgium

2. 2 Scalable video Encode once Decode according to required QoS or available hardware resources. Quality ~ deployed hardware resources

3. 3 Overview Scalable video codec Scalable Wavelet Entropy Encoding : New algorithm Memory footprint Compression results Conclusions

4. 4 Overview video codec Motion Estimation PackPack Entropy Encoding Wavelet Transform Motion Vector Encoding original frames Exploit temporal redundancy Exploit spatial redundancy Exploit statistical properties

5. 5 Motion estimation  vectors + error frame

6. 6 Motion compensation Reference frame current GOP Reference frame next GOP time Scalability in time

7. 7 Wavelet transform Scalability in resolution

8. 8 Wavelet Entropy Coder Scalability: Quality Resolution Good compression Economical with memory High degree of parallelism Sweet and simple Wavelet Entropy Codec Actual compression of frames Tight coupling of pixel characterization and Arithmetic coding (modified arithmetic encoder of CABAC) Model Selector Arithmetic Encoder Entropy Encoding

9. 9 Scalability in quality

10. 10 Parallelism In parallel in Parallel Easy to omit subbands → resolution scalability

11. 11 Algorithm Bitplane Significance bitmap Sign bitmap 0.30.70.8 0.2

12. 12 Algorithm Bitplane Significance bitmap Sign bitmap

13. 13 Compression gain Context/Arithmetic models 64 different models: each model needs only a 9 bit state 1 data model 27 sign models 27 significance models 8 special highest bitlayer significance models 1 refinement model 64 for each type of subband: 480 subband models [LL,HL,LH,HH] [resolution layer] [color channel] [temporal level] ~> 30.720 different models in total 0 1 1/2 real probability time unitialised trained probability Models are initialised (warmed up) with values obtained from training a number of representative sequences

14. 14 Memory requirements Arithmetic Decoder: State Lookup table: 256B Range Lookup table: 2048B Model state table: 64B Buffer Model Selector Significance and sign bitmap: 2 x 3168B A few buffers (1 MRAM block is large enough to contain multiple significance and sign bitmaps) Arithmetic Decoder Model selector NameNumberSize (byte + parity) MRAM264k M4K138512 M51222464 Registers256601/8 214kiB Altera Stratix S25 ~7 M4K block ~13 M4K blocks

15. 15 Results QTL: QuadTree Limited (comparable compression as EBCOT of jpeg2000)

16. 16 Conclusion Algorithm is simple No recursive behaviour Small memory footprint [ 2 bitmaps of size(wavelet subband) ] Memory access is very regular Arithmetic coder must process a lot symbols, but is simple and fast Compression is better than QTL-algorithm for all bitrates TODO: We are working hard on an effective FPGA-implementation of the decoder

17. 17

18. 18 Arithmetic Coding 0.30.70.8 0.2

19. 19 Scalable video (animated) Encode once Decode according to required QoS or available hardware resources. Quality ~ deployed hardware resources

20. 20 Overview video codec Motion Estimation PackPack Entropy Encoding Wavelet Transform Motion Vector Encoding Motion Comp. Entropy Decoding Motion Vector Decoding UnpackUnpack Pull bitstream Inverse Wavelet T. original frames decoded frames