Image compression method combining singular value decomposition and wavelet packet transformation

Abstract

The invention discloses an image compression method combining singular value decomposition and wavelet packet transformation, and the method comprises the steps: carrying out the singular value decomposition of an original image, and decomposing the original image into a texture vector U, a geometric vector V and a singular value S; adopting an energy spectrum mode to process the characteristic value set to obtain a characteristic value set; processing the feature value set by adopting an adaptive singular value decomposition method to obtain a singular value threshold number K, and selectingthe first K feature values in the feature value set to form an approximate matrix; performing wavelet packet transformation on the first K left and right odd vectors of the approximate matrix, so thata wavelet complete binary tree can be obtained; taking the logarithm energy entropy as a cost function, sequentially comparing the logarithm energy entropy values of the child node and the father node upwards from the bottom layer of the wavelet complete binary tree, reserving the node with the smaller entropy value, deleting the node with the larger entropy value, and further obtaining an optimal wavelet tree; and reconstructing the optimal wavelet tree to obtain a reconstructed image. According to the invention, the image compression quality can be improved and a high compression ratio canbe obtained.

Description

technical field [0001] The invention relates to the technical field of image compression, in particular to an image compression method combining singular value decomposition and wavelet packet transformation. Background technique [0002] If a digital image file is not compressed, its data volume is relatively large, which has a relatively large adverse effect on storage and transmission. If you just need to browse the image quickly, you can use a lossy compression method to remove some non-key information from the image to reduce the amount of data. However, when high-quality images are required, the texture components and detail information of the image need to be preserved, and these high-frequency subbands containing texture and details need to be preserved when compressing the image, which limits the compression ratio of the image. [0003] Wavelet analysis theory is a fine analysis method. When using wavelet packet for image compression, a higher compression ratio can...

AI Technical Summary

Problems solved by technology

However, since the wavelet packet transform is a two-dimensional wavelet transform on the image, it is a tensor product of one-dimensional wavelet, and its direction is limited, which cannot express the singularity characteristics of the image well, so that the compressed image edge and textures may appear more noticeable distortion

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