Image coding with adaptive wavelet packet trees

Embedded zero-tree image coding in the wavelet domain has drawn a lot of attention. Among noteworthy algorithms is the set partitioning in hierarchical trees (SPIHT). Typically, most of images' energy is concentrated in the low frequency subbands. For images with textures, however many middle-high frequency wavelet coefficients are likely to become significant in the early passes of SPIHT; thus the coding results are often insufficient. The middle and high frequency subbands of an image may demand a further decomposition with adaptive basis functions. As wavelet packet transform offers a great diversity of basis functions, we propose a quad-tree based adaptive wavelet packet transform to construct adaptive wavelet packet trees for zero-tree image coding. Experimental results show that the coding performance can be significantly improved especially for fingerprints images.