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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.

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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 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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