1. Regression-Based Prediction for Artifacts in JPEG-Compressed Images
Park,Jungjin

2. Introduction
To achieve high compression ratio in JPEG and MPEG, the original image or video may be distorted by blocking and ringing artifact.

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

4. Block Diagram 8x8 DCT Low-Pass Filtering Regression- Based Predicting
IDCT
3x3 Gaussian filter could reduce the blocking artifacts.
Results in undesirable blurring of filtered image.

5. Classifier •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
Class1
Class2
u,v=1,..8

6. Threshold
u,v 1 2 3 4 5 6 7 8 74
205
123 26
570 44
342
952

7. Regression Model with slope
Class 2
Without classifier
Gauss-Newton method can find the lest square fit estimate of coefficients using linearization
Class 1

8. 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

9. Result (reducing blocking)
Original test image
Recovered image

10. Result (reducing ringing)

11. Result (reducing ringing)

12. Conclusion
Regression based
POCS based