论文部分内容阅读
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.
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. Model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. 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 Daubechies 9/7.