Zerotree wavelet image compression with weighted sub-block-trees and adaptive coding order

Information distortion in regions of image edge is more perceptible for people than those in other regions. To improve the performance on the edge, we present an improved embedded zero-tree wavelet image compression algorithm with weighted sub-block-trees and adaptive coding order. First, we assign bigger weights to the sub-block-trees around image edge. The weights assigned to the sub-blocks in the same spatial location and the same orientation at different scales are equal so that the zero-tree structure of wavelet coefficients is maintained and only a little extra storage is needed. Then we prefer scanning the coefficients on the neighbor of previous significant coefficients and all of them are refined even they are not significant. Adaptive arithmetic coding is applied to the symbols of these coefficients and others respectively. The proposed method pays more attention to the edge and its neighborhood so that the decoded image on the edge is clearer. Compared with similar algorithms, experimental results show that the proposed method can improve the PSNR and SSIM, as well as the subjective visual experience. The proposed method is applicable to any genre of embedded wavelet image codec.