Weighted Adaptive Lifting-Basedwavelet Transform

Abstract

In this paper, we propose a new weighted adaptive lifting (WAL)-based wavelet transform that is designed to solve the problems existing in the previous adaptive directional lifting (ADL) approach. The proposed approach uses the weighted function to make sure that the prediction and update stages are consistent, the directional interpolation to improve the orientation property of interpolated image, and adaptive interpolation filter to adjust to statistical property of each image. Experimental results show that the proposed WAL-based wavelet transform for image coding outperforms the conventional lifting-based wavelet transform up to 3.02 dB in PSNR and significant improvement in subjective quality is also observed. Compared with the ADL approach, up to 1.18 dB improvement in PSNR is reported.