Distributed hyper spectrum image compression method based on 3D wavelet transformation

Abstract

The invention provides a distributed hyper spectrum image compression method based on 3D wavelet transformation. The method comprises the following steps of: 1, dividing a hyper spectrum image into a plurality of coding units; 2, performing the 3D wavelet transformation on each coding unit of the hyper spectrum image; 3, grouping images after transformation, wherein one group is used as a reference group, and another group is used as a current group; 4, coding the reference group by a 3D SPIHT method to obtain a compression code stream of the reference group, and sending the compression code stream to a decoding end; 5, resolving the current group by an SW-SPIHT algorithm to obtain three groups of corresponding bit plane code streams which are an importance factor bit plane, an importance factor symbol bit plane and a thinning bit plane respectively; and 6, coding the importance factor symbol bit plane and the thinning bit plane of the current group by a low-density parity check code to obtain a corresponding check bit stream, transmitting the code stream of the importance factor bit plane of the current group to the decoding end, and completing the compression of the hyper spectrum image. The method has the advantages of low coding complexity and good compression property, and can be used for compressing the hyper spectrum image.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image compression method, which can be used for low-complexity hyperspectral image compression. Background technique [0002] In today's era of rapid development of remote sensing technology, hyperspectral remote sensing images obtained from ground remote sensing observations are an important data source. However, due to the huge amount of data in hyperspectral remote sensing images, each spectral image contains information of dozens to hundreds of bands, which brings a great burden to data storage and transmission. The image is compressed. [0003] As we all know, hyperspectral image compression is very picky about the complexity of the encoding algorithm. Although the current compression methods such as 3D SPIHT and 3D SPECK methods are used in many hyperspectral image compression systems, they can achieve effective hyperspectral image compression. compression, bu...

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Problems solved by technology

[0003] As we all know, hyperspectral image compression is very picky about the complexity of the encoding algorithm. Although the current compression methods such as 3D SPIHT and 3D SPECK methods are used in many hyperspectral image compression systems, they can achieve effective hyperspectral image compression. compression, but its practical application is limited due to the high coding complexity of its method

Hyperspectral image compression methods such as JPEG2000 and two-dimensional SPIHT, although the complexity of the method is low, the compression performance is not high

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