Entropy coding

1. Entropy coding Present by 陳群元

2. constraints • Compression efficiency • Computational efficiency • Error robustness

3. Encoding • DCT • Reordering • Run-level coding • Entropy coding

4. Encoding • DCT • Reordering • Run-level coding • Entropy coding

5. DCT

6. Reordering • The optimum method of reordering the quantised data depends on the distribution of the non-zero coefficients.

7. Evenly distribution

8. Zigzag reordering pattern

9. Interlaced video-vary more in vertical

10. Modified reordering pattern

11. Encoding • DCT • Reordering • Run-level coding • Entropy coding

12. Run-level coding • Long sequences of identical values (zeros in this case) can berepresented as a (run, level) code • (run) indicates the number of zeros preceding anon-zero value • (level) indicates the sign and magnitude of the non-zero coefficient

13. Run-level coding(ex)

14. Encoding • DCT • Reordering • Run-level coding • Entropy coding

15. Huffman Coding • ‘True’ Huffman Coding • Modified Huffman Coding • Table Design • Entropy Coding Example • Variable Length Encoder Design • Variable Length Decoder Design • Dealing with Errors

16. Huffman Coding • ‘True’ Huffman Coding • Modified Huffman Coding • Table Design • Entropy Coding Example • Variable Length Encoder Design • Variable Length Decoder Design • Dealing with Errors

17. True Huffman Coding

18. Generating the HufSman code tree • 1. Order the list of data in increasing order of probability. • 2. Combine the two lowest-probability data items into a ‘node’ and assign the joint probability of the data items to this node. • 3. Reorder the remaining data items and node(s) in increasing order of probability and repeat step 2.

20. encoding

21. decoding

22. disadvantage • the decoder must use the same codeword set as the encoder • reduces compression efficiency. • calculating the probability table for a large video sequence a significant computational overhead • cannot be done until after the video data is encoded

23. Huffman Coding • ‘True’ Huffman Coding • Modified Huffman Coding • Table Design • Entropy Coding Example • Variable Length Encoder Design • Variable Length Decoder Design • Dealing with Errors

24. Table design Final pair

25. Table design

26. H.263/MPEG-4 motion vector difference (MVD) • The H.263MPEG-4 differentially coded motion vectors (MVD) • are each encoded as a pair of VLCs, one for the x-component and one for the y-component

27. mvd

28. mvd

29. H.26L universal VLC (UVLC) • The emerging H.26L standard takes a step away from individually calculated Huffman tables by using a ‘universal’ set of VLCs for any coded element. Each codeword is generated from the following systematic list:

32. Huffman Coding • ‘True’ Huffman Coding • Modified Huffman Coding • Table Design • Entropy Coding Example • Variable Length Encoder Design • Variable Length Decoder Design • Dealing with Errors

33. Entropy Coding Example

34. Huffman Coding • ‘True’ Huffman Coding • Modified Huffman Coding • Table Design • Entropy Coding Example • Variable Length Encoder Design • Variable Length Decoder Design • Dealing with Errors

35. Variable Length Encoder Design

36. Huffman Coding • ‘True’ Huffman Coding • Modified Huffman Coding • Table Design • Entropy Coding Example • Variable Length Encoder Design • Variable Length Decoder Design • Dealing with Errors

37. Variable Length Decoder Design

38. Huffman Coding • ‘True’ Huffman Coding • Modified Huffman Coding • Table Design • Entropy Coding Example • Variable Length Encoder Design • Variable Length Decoder Design • Dealing with Errors

39. Dealing with Errors

40. Arithmetic Coding

41. Ex . encode (0,-1,0,2) 0.394

43. Ex . decode (0,-1,0,2)

44. Arithmetic coding vshuffman • Ideal • 0.394 in this case, which can be represented as a fixed-point number with sufficient accuracy using 9 bits • Huffman: 1;0011;1;0000110=>13 bits

45. The end • Thank you