Abstract— In the process of wavelet based image coding, it is
possible to enhance the performance by applying prediction.
However, it is difficult to apply the prediction using a decoded
image to the 2D DWT which is used in JPEG2000 because the
decoded pixels are apart from pixels which should be predicted.
Therefore, not images but DWT coefficients have been predicted.
To solve this problem, predictive coding is applied for
one-dimensional transform part in 2D DWT. Zhou and
Yamashita proposed to use half-pixel line segment matching for
the prediction of wavelet based image coding with prediction. In
this research, convolutional neural networks are used as the
predictor which estimates a pair of target pixels from the values
of pixels which have already been decoded and adjacent to the
target row. It helps to reduce the redundancy by sending the
error between the real value and its predicted value. We also
show its advantage by experimental results.
Index Terms—Wavelet image coding, discrete wavelet transform, predictive coding, neural networks, super resolution.
The authors are with Tokyo Institute of Technology, School of Environment and Society, Meguro-ku, Tokyo 1528552 Japan (e-mail: email@example.com, firstname.lastname@example.org).
Cite: Takuma Takezawa and Yukihiko Yamashita, "Wavelet Based Image Coding via Image Component Prediction Using Neural Networks," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 137-142, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).