Regression-Based Prediction for Artifacts in JPEG-Compressed Images

Regression-Based Prediction for Artifacts in JPEG-Compressed Images

Park,Jungjin

Introduction To achieve high compression ratio in JPEG and

MPEG, the original image or video may be distorted by blocking and ringing artifact.

Goal Reduction artifacts Reduction time to process Reduction computational complexity Simple algorithm

Block Diagram

Low-Pass

FilteringDCT

Regression-

Based PredictingIDCT

3x3 Gaussian filter could reduce the blocking artifacts.

Results in undesirable blurring of filtered image.

8x8 DCT

Classifier

N

u

N

vvu GG

N 1 1

21,1

2,2

2

11̂

•To classify textures, details, and edges of each DCT block

•Calculate the local variable from the DCT coefficients

Each DCT coefficient of the DCT block is classified into two distinct classes,

CLASS1 CLASS2 vu

vu

,2

,2

ˆ

ˆ

Class1

Class2

u,v=1,..8

Threshold u,v 1 2 3 4 5 6 7 8

1 3 74 205 205 123 123 123 26

2 123 205 570 205 570 123 44 44

3 342 342 123 205 123 123 205 74

4 570 205 205 205 26 342 205 123

5 123 123 123 26 205 205 123 74

6 205 342 44 342 342 205 74 205

7 205 74 74 26 44 570 342 74

8 342 342 205 123 952 205 952 74

Regression Model with slope

zz

zz

z,

,

,

),|(

Gauss-Newton method can find the lest square fit estimate of coefficients using linearization

Class 2

Class 1

Without classifier

Regression Model with slope

Class1 has a larger slope than Class2 case

The slope in the predictors can control the effect of the low-pass filtering

1)Slope>1 image becomes smoother than low pass filtering image

2)Slope<1 image can alleviate the undesirable blurring

Result (reducing blocking)

Original test image Recovered image

Result (reducing ringing)

Result (reducing ringing)

Conclusion Regression based 4.1400 POCS based 17.1880