Wavelet Filter selection Based on Spectral Features in Multispectral Image Compression

摘要

The problem of selecting an appropriate wavelet filter is always present in signal compression based on teh wavelet transform. In this report, we give a method to select a wavelet filter for multispectral image compression. The wavelet filter selection is based on teh Learning Vector Quantization (LVQ). In the training phase for the test images, the best wavelet filter has been found by a careful compression-decompression evaluation. Certain spectral features are used in characterizing the pixel spectra. The LVQ is used to form the best wavelet filter class for different types of spectral images. When a new image is to be compressed, a set of spectra from theat image is selected, the spectra are classified by the trained LVq and the filter associated to the largest class is selected for the compression of the whole multispectral image. The results show, that our method finds the most suitable wavelet filter for compression of multispectral images.