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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.
Pre/Post-Processing 蔡茗光Outline
  • Pre/Post-Processing Overview
  • Pre-Processing introduction
  • Post-Processing introduction
  • System Block diagram
  • Pre/Post-Processing OverviewGenerally, the pre/post-processing is like the following︰Pre-ProcessingEncoderPost-ProcessingInputGoal︰the former enhance coding efficiency by removing noise information without compromising quality, the latter reduce the blocking(Grid Noise)、ringing(Staircase Noise) effectPre-Processing Introduction(1/X)ColorconversionDownconversionPre-filteringinputoutput
  • 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  YUVPre-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 domainPre-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 …….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 =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 orderTwo variables (D0 、n) can be tuned when implementing.Generally, n should be small to avoid ringingPre-Processing Introduction(6/X) n = 4 Wp = 7Original n = 4Wp = 10 n = 1 Wp = 7Pre-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 pixelPre-Processing Introduction(8/X)◆ Temporal domain ( in the different frame )Non-Linear︰ the following is a de-interlaced vertical median filterCurrent fieldPrevious field interpolated pixel which is median result of three arrows original pixelPre-Processing Introduction(9/X)Vertical-medianOriginal Square-median Pre-Processing Introduction(10/X)Frame-medianOriginal MB-median Pre-Processing Introduction(11/X)◆Spatial domain ( in the same frame )Linear︰ 1 1SUM pi original pixel value Pi new pixel value wi weighting ( integer )99 1 1SUMi=1i=1P5 = Sum(piwi)SUM = Sum(wi)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 9i=1P5 = median(pi)Pre-Processing Introduction(13/X)Frame-based Original MB-based Pre-Processing Introduction(14/X)I-frameP-framePre-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 frameThe 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 seriousPost-Processing Introduction(1/X)
  • Commonly, it can be partitioned into two parts shown below
  • De-BlockingDe-Ringinginputoutput— De-Blocking、De-Ringing reduce the artifacts due to the quantization of the DCT coefficients, the degradation mainly consists of two kinds of artifacts︰ 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 )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 appliedSystem Block Diagram(1/X)QVideo inTQ-1T-1MC/MELoop filterSystem Block Diagram(2/X)QVideo inTQ-1T-1MC/MELoop filterSystem Block Diagram(3/X)QfilterTQ-1Video inT-1MC/MELoop filterA Question
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