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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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Presentation Transcript
slide2

Outline

  • Pre/Post-Processing Overview
  • Pre-Processing introduction
  • Post-Processing introduction
  • System Block diagram
slide3

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

slide4

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

slide5

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

slide6

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

…….

slide7

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 =

slide8

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

slide9

Pre-Processing Introduction(6/X)

n = 4

Wp = 7

Original

n = 4

Wp = 10

n = 1

Wp = 7

slide10

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

slide11

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

slide12

Pre-Processing Introduction(9/X)

Vertical-

median

Original

Square-

median

slide13

Pre-Processing Introduction(10/X)

Frame-

median

Original

MB-

median

slide14

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)

slide15

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)

slide16

Pre-Processing Introduction(13/X)

Frame-

based

Original

MB-

based

slide18

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

slide19

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︰

slide20

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 )

slide21

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

slide22

System Block Diagram(1/X)

Q

Video in

T

Q-1

T-1

MC/ME

Loop filter

slide23

System Block Diagram(2/X)

Q

Video in

T

Q-1

T-1

MC/ME

Loop filter

slide24

System Block Diagram(3/X)

Q

filter

T

Q-1

Video in

T-1

MC/ME

Loop filter