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Pre/Post-Processing

Pre/Post-Processing. 蔡茗光. Outline. Pre/Post-Processing Overview Pre-Processing introduction Post-Processing introduction System Block diagram. Pre/Post-Processing Overview. Generally, the pre/post-processing is like the following︰. Pre- Processing. Encoder. Post- Processing.

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Pre/Post-Processing

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  1. Pre/Post-Processing 蔡茗光

  2. Outline • Pre/Post-Processing Overview • Pre-Processing introduction • Post-Processing introduction • System Block diagram

  3. Pre/Post-Processing Overview Generally, the pre/post-processing is like the following︰ Pre- Processing Encoder Post- Processing Input Goal︰the former enhance coding efficiency by removing noise information without compromising quality, the latter reduce the blocking(Grid Noise)、ringing(Staircase Noise) effect

  4. Pre-Processing Introduction(1/X) Color conversion Down conversion Pre- filtering input output • Basically it can be separated into three stages shown in the above — Color conversion images are transformed in a more convenient form ex︰RGB  HSL、RGB  YUV

  5. Pre-Processing Introduction(2/X) — Down conversion images are down sampled for data reduction ex︰422  420、422  411 — Pre-filtering reduce the complexity of video sequences by attenuating noise and small figures ( that is smoothing ), the resulting frames are less prone to errors such as blocking、ringing 、temporal flicker. it can be divided into three portions frequency domain temporal domain spatial domain

  6. Pre-Processing Introduction(3/X) Examples of noise - Random Noise source Residual noise Film blotch and scratch noise Compression artifacts ……. - Impulse Noise source Satellite glitches Analog clamping errors Bit errors in digital transmission …….

  7. Pre-Processing Introduction(4/X) ◆ Frequency domain ( in the same frame ) Transform input data to frequency domain(ex︰DFT、DCT..) g(t) = h(t) * f(t)  G(w) = H(w) F(w) A Butter-worth LPF is illustrated below (1D - form )︰ H frequency response w input frequency wp pass-band frequency n order 111111 1+(w / wp)2n | H(w) |2 =

  8. Pre-Processing Introduction(5/X) ◆ Frequency domain ( in the same frame ) g(x,y) = h(x,y) * f(x,y)  G(u,v) = H(u,v) F(u,v) A Butter-worth LPF is illustrated below ( 2D - form )︰ 11111111 1+[ D(u,v) / D0 ]2n | H(u,v) | = H(u,v) frequency response D(u,v) input frequency D0 cut-off frequency n order Two variables (D0 、n) can be tuned when implementing. Generally, n should be small to avoid ringing

  9. Pre-Processing Introduction(6/X) n = 4 Wp = 7 Original n = 4 Wp = 10 n = 1 Wp = 7

  10. Pre-Processing Introduction(7/X) ◆ Temporal domain ( in the different frame ) Linear︰ the following is a de-interlaced vertical temporal filter current field neighboring field(s) weighted sum original pixel interpolated pixel

  11. Pre-Processing Introduction(8/X) ◆ Temporal domain ( in the different frame ) Non-Linear︰ the following is a de-interlaced vertical median filter Current field Previous field interpolated pixel which is median result of three arrows original pixel

  12. Pre-Processing Introduction(9/X) Vertical- median Original Square- median

  13. Pre-Processing Introduction(10/X) Frame- median Original MB- median

  14. Pre-Processing Introduction(11/X) ◆Spatial domain ( in the same frame ) Linear︰ 1 1 SUM pi original pixel value Pi new pixel value wi weighting ( integer ) 9 9 1 1 SUM i=1 i=1 P5 = Sum(piwi) SUM = Sum(wi)

  15. Pre-Processing Introduction(12/X) ◆Spatial domain ( in the same frame ) Non-linear (ex︰median、max、min、average)︰ pi original pixel value Pi new pixel value 9 i=1 P5 = median(pi)

  16. Pre-Processing Introduction(13/X) Frame- based Original MB- based

  17. Pre-Processing Introduction(14/X) I-frame P-frame

  18. Pre-Processing Introduction(15/X) From the table, a problem is generated in the MB-based filter.  bit rate is higher than the original frame  PSNR is lower than the original frame The reason may be  About bit rate︰ due to the noise variance in the same MB, the median value would be different  About PSNR︰ due to the uncontinuous edge, it’ll make the situation more serious

  19. Post-Processing Introduction(1/X) • Commonly, it can be partitioned into two parts shown below De- Blocking De- Ringing input output — De-Blocking、De-Ringing reduce the artifacts due to the quantization of the DCT coefficients, the degradation mainly consists of two kinds of artifacts︰

  20. Post-Processing Introduction(2/X) 1. the gradual intensity changes in original image become abrupt intensity variations along block boundaries ( Grid Noise ), 2. while the pixel values at either side of an edge is modified, increasing the degradation of the entire edge ( Staircase Noise )

  21. Post-Processing Introduction(3/X) For areas near block edge a low-pass filtering is performed by ultilizing fuzzy computation of its coefficients a block area near edge fine detailed area For fine detailed areas filtering isn’t applied

  22. System Block Diagram(1/X) Q Video in T Q-1 T-1 MC/ME Loop filter

  23. System Block Diagram(2/X) Q Video in T Q-1 T-1 MC/ME Loop filter

  24. System Block Diagram(3/X) Q filter T Q-1 Video in T-1 MC/ME Loop filter

  25. A Question

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