Deep CNN of JPEG 2000 電信所R06942121林俊廷

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

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.

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

PSNR and SSIM -------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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

Framework of Deep CNN

Convolution layer

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.

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Batch normalization

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.

Rectifier Linear Unit

ReLU Rectifier Linear Unit : use a max{0,x} function , because it’s like the neural of human beings . In 2001. Dayan ReLU function

Training framework

Testing flowchart Compare JPEG, JPEG2000 with Deep CNN method

Comparison PSNR the higher the better SSIM the higher the better

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