Hyperspectral Image Compression Method Based on Fractal Multiwavelet

Abstract

The present invention provides a hyperspectral image compression method based on fractal multi-wavelets, using the 3D SPIHT image compression system to group the three-dimensional hyperspectral images to be compressed, and using the constructed fractal multi-wavelet filter matrix to compress the expanded three-dimensional hyperspectral images The spectral image is subjected to fractal multi-wavelet transformation, and finally the quantization coefficient is used to obtain a 3D SPIHT hyperspectral image compression code stream based on fractal multi-wavelet; the hyperspectral image compression method based on fractal multi-wavelet according to the present invention is based on the 3D hyperspectral image compression method based on fractal multi-wavelet transformation. SPIHT, using the hierarchical tree set partitioning (SPIHT) coding method, can make good use of the distribution characteristics of hyperspectral image fractal multi-wavelet coefficients and the correlation of hyperspectral image wavelet coefficients between different scales, and also take care of the same scale as much as possible The correlation of wavelet coefficients between hyperspectral images completes the scalable compression coding of hyperspectral images, which has higher compression coding efficiency.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a hyperspectral image compression method based on fractal multi-wavelets. Background technique [0002] As an emerging remote sensing technology, hyperspectral imagery can provide more information that people need due to the addition of one-dimensional data, and is widely used in agriculture, industry, and military fields, but the rich information also leads to the expansion of data volume , requires special compression methods for effective encoding in order to store and transmit smoothly. Hierarchical Tree Set Partitioning (SPIHT) image compression method indirectly realizes the bit plane sorting of spatial wavelet, effectively utilizes the multi-resolution characteristics of wavelet decomposition, and has the characteristics of embedded code stream. Therefore, SPIHT image compression method has the ability to restore image quality High, easy to output bit rate precise control ...

AI Technical Summary

Problems solved by technology

However, the Haar wavelet and 9 / 7 wavelet are single wavelets, and it is impossible to have the symmetry, short support, orthogonality and high-order vanishing moment properties required to represent hyperspectral images at the same time. Each layer of wavelet transform can only obtain 4 different The characteristics in the direction, the concentration of energy and entropy after wavelet transform is not high, and the wavelet transform needs to extend the boundary of the image to cause boundary distortion, which is not conducive to block processing and parallel acceleration

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