1. Sub-Band Coding Multimedia Systems and Standards S2 IF Telkom University

2. Overview Compression schemes were efficient when the data exhibit certain characteristics. Unfortunately, most source outputs exhibit a combination of characteristics.  difficult to select a compression scheme exactly suited to the source output. 2

3. Now, these compression techniques as you know by now, are most efficient when the data exhibit some predominant characteristic. 1. If the source output is truly random, it is best to use scalar quantization. 2. The vector quantization scheme is most effective if blocks of the source output show a high degree of clustering. 3. The differential encoding scheme is most effective when the sample-to-sample difference is small. Thus, if a source exhibited certain well-defined characteristics, we could choose a compression scheme most suited to that characteristic. 3

4. Overview - cont’d Decomposing the source output into constituent parts using some method. Each constituent part is encoded using one or more of the methods described previously.  enables the use of these compression schemes more effectively. 4

5. Example Xn Zn Yn Zn Yn Compression Scheme 1 Compression Scheme 2 Xn 5

6. Video Compression 6 Layered Coder D D D + + Layer 0 Layer 1 Layer 2 1 Mb/s 256 kb/s 64 kb/s Layered video encoding/decoding. D denotes the decoder.

7. Introduction to Subband Coding The source output can be decomposed into its constituent parts using digital filters. Each of these constituent parts will be different bands of frequencies which make up the source. 7

8. Subband Coding A compression approach where digital filters are used to separate the source output into different bands of frequencies.  Each part then can be encoded separately. 8

9. Filters A filter is system that isolates certain frequencies. (i)Low Pass Filters (ii)High Pass Filters (iii)Band Pass Filters 9

10. Filters – Cont’d Filter Characteristics  Magnitude Transfer Function : the ratio of the magnitude of the input and output of the filter as a function of frequency.  f o = Cutoff Frequency. 10

11. Digital Filters Sampling and Nyquist rule : If fo is the highest frequency of the signal then the sampling rate > 2fo per second can accurately represent the continuous signal in digital form. Extension of Nyquist rule: For signal with frequency components between frequencies f1and f2 then, sampling rate = 2f2 per second. Violation of Nyquist rule: Distortion due to aliasing. 11

12. Digital Filtering The general form of the input-output relationships of the filter is given by where, {Xn}= input, {Yn}=output of the filter, Values {ai} and {bi} = filter coefficients, N is called the taps in the filter.  FIR Filter  IIR Filter 12

13. Example Filter Coefficients a o = 1.25, a 1 = 0.5 and the input sequence {Xn} is given by – then the output {Yn} is given by 13

14. Example Consider a filter with a o = 1 and b 1 = 2. The input sequence is a 1 followed by 0s. Then the output is 14

15. Filters used in Subband Coding Couple of examples of –  Quadrature Mirror Filters (QMF),  Johnston Filter  Smith-Barnwell Filters  Daubechies Filters ….and so on 15

16. 16 Filter Banks Subband coding uses filter banks. Filter banks are essentially a cascade of stages, where each stage consists of a low- pass filter and a high-pass filter.

17. Subband Coding Algorithm 17

18. (1) Analysis Source output  analysis filter bank  sub-sampled  encoded. Analysis Filter Bank  The source output is passed through a bank of filters.  This filter bank covers the range of frequencies that make up the source output.  The passband of each filter specifies each set of frequencies that can pass through. 18

19. (1) Analysis Source output  analysis filter bank  sub-sampled  encoded. Analysis Filter Bank Decimation  The outputs of the filters are subsampled thus reducing the number of samples. 19

20. (1) Analysis Source output  analysis filter bank  sub-sampled  encoded. Analysis Filter Bank Decimation  The justification for the subsampling is the Nyquist rule and its extension justifies this downsampling. 20

21. (1) Analysis Source output  analysis filter bank  sub-sampled  encoded. Analysis Filter Bank Decimation  The amount of decimation depends on the ratio of the bandwidth of the filter output to the filter input. 21

22. (1) Analysis Source output  analysis filter bank  sub-sampled  encoded. Analysis Filter Bank Decimation Encoding  The decimated output is encoded using one of several encoding schemes, including ADPCM, PCM, and vector quantization. 22

23. (2) Quantization and Coding  Selection of the compression scheme  Allocation of bits between the subbands  allocate the available bits among the subbands according to measure of the information content in each subband. This bit allocation procedure significantly impacts quality of the final reconstruction. 23

24. Bit Allocation Minimizing the distortion i.e. minimizing the reconstruction error drives the bit allocation procedure. Different subbands  different amount of information. Bit allocation procedure can have a significant impact on the quality of the final reconstruction 24

25. (3) Synthesis  Quantized and Coded coefficients are used to reconstruct a representation of the original signal at the decoder. Encoded samples from each subband  decoded  upsampled  bank of reconstruction filters  outputs combined  Final reconstructed output 25

26. Application The subband coding algorithm has applications in -  Speech Coding  Audio Coding  Image Compression 26

27. Application to Image Compression LLLH HL HH 27

28. Decomposing and Image 28

29. Decomposing and Image 29

30. Decomposing and Image 30

31. Coding the Subbands SQ LLLH HL HH DiscardDPCM Some bands  VQ 31

32. Coding the Subbands 32

33. Coding the Subbands 33

34. Summary Subband coding is another approach to decompose the source output into components based on frequency. Each of these components can then be encoded using one of the techniques described in the previous chapters. 34

35. Summary The general subband encoding procedure can be summarized as follows: Select a set of filters for decomposing the source. Using the filters, obtain the subband signals. Decimate the output of the filters. Encode the decimated output. The decoding procedure is the inverse of the encoding procedure. 35

36. Example – Cont’d Xn = 10 14 10 12 14 8 14 12 10 8 10 12 Yn = Xn = Yn + Zn Zn = 36