Wavelet Based Image Coding via Image Component Prediction Using Neural Networks

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.