Special Issue: Artificial Intelligence and Pattern Recognition

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Fast adaptive wavelet for remote sensing image compression

Bo Li{1,2, Run-Hai Jiao{1, and Yuan-Cheng Li1   

  1. 1Digital Media Laboratory, School of Computer Science and Engineering, Beihang University, Beijing 100083, China 2State Key Laboratory of Virtual Reality Technologies, Beihang University, Beijing 100083, China
  • Received:2007-01-15 Revised:2007-07-03 Online:2007-09-10 Published:2007-09-10

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm (FAWS). Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.

Key words: inequality with radicals; rationalization; dichotomous search;

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