Group Testing for Wavelet Packet Image Compression
DCC '01 Proceedings of the Data Compression Conference
Embedded image coding using zerotrees of wavelet coefficients
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Wavelet packet image coding using space-frequency quantization
IEEE Transactions on Image Processing
Fast adaptive wavelet packet image compression
IEEE Transactions on Image Processing
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Adaptive wavelet packet basis selection for zerotree image coding
IEEE Transactions on Image Processing
Image Coding Using Dual-Tree Discrete Wavelet Transform
IEEE Transactions on Image Processing
Best wavelet packet bases in a rate-distortion sense
IEEE Transactions on Image Processing
A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)
Expert Systems with Applications: An International Journal
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The most widely used basis selection algorithm for adaptive wavelet packets is the optimal basis selection method, which first grows a full wavelet packet tree and then prunes it into the optimal tree that gives the minimum cost. We observed that there exists the interscale embedding property in wavelet packet decomposition structures between interscale subbands along the same orientation. Based on this observation, we propose a fast basis selection algorithm by first decomposing a dyadic wavelet subband with the decomposition structure of its parent subband, before applying the optimal basis selection method for further decomposition. Experiments show that the proposed algorithm generates almost the same wavelet packet decomposition structures as the optimal basis selection method while significantly reducing the computational complexity for some image classes.