1. Deep CNN of JPEG 2000
電信所R 林俊廷

2. problem JPEG2000 is lossy compression algorithms Distortion
Coding artifacts of JPEG2000
Blocking, Ringing, Aliasing

3. Blocking and Ringing
Blocking : A whole picture is too hard to analyze, so we just focus on part of it. e.g 3*3 pixel.
Lose the correlation of the different part of picture.
Ringing : the bandwidth is finite, so there will be some distortion in specific function.

4. Decoder Format Decoder Read the specific format file.
How to judge a picture format decoder ?
PSNR , SSIM
Format
Decoder
JPEG2000
PHOTOSHOP
DOC
WORD

5. PSNR and SSIM

6. Decoder implementation by deep CNN
Solution
Original decoder is time-consuming
Decoder implementation by deep CNN

7. Framework of Deep CNN

8. Convolution layer

9. Weight and Bias Bias(b) Weight(w)
Asumption : source domain and target domain distribution are the same.
Domain adaption : to make the initial data distribution the same, we suppose to make average equal 0, variance equal 1.

10. Filter

11. https://brohrer. mcknote

12. Batch normalization

13. Batch normalization
Asumption : source domain and target domain distribution are the same.
batch normalization : average and variance after each convolution layer may change, so we need to change them back again.

14. Rectifier Linear Unit

15. ReLU
Rectifier Linear Unit : use a max{0,x} function , because it’s like the neural of human beings .
In Dayan
ReLU function

16. Training framework

17. Testing flowchart
Compare JPEG, JPEG2000 with Deep CNN method

18. Comparison
PSNR the higher the better
SSIM the higher the better

21. Conclusion 1.Quality 2.Process time
under low to medium bit-rates, this method can enhance the performance of the JPEG2000. performances
1.Quality
2.Process time