Image compression method combining wavelet packet transformation and singular value decomposition

Abstract

The invention discloses an image compression method combining wavelet packet transformation and singular value decomposition, which comprises the following steps of: performing wavelet packet decomposition on an original image to obtain a complete binary tree; taking logarithmic energy entropy as a cost function, sequentially and upwards comparing logarithmic energy entropy values of child nodes and father nodes from the bottom layer of the complete binary tree, retaining nodes with smaller logarithmic energy entropy values, deleting nodes with larger logarithmic energy entropy values, and further obtaining an optimal wavelet tree; reconstructing the optimal wavelet tree by adopting a wavelet packet reconstruction algorithm to obtain a reconstructed image; performing singular value decomposition on the two-dimensional time-frequency information of the reconstructed image to decompose the two-dimensional time-frequency information into texture vectors, geometric vectors and singular values; processing the data in an energy spectrum mode to obtain a characteristic value set; processing the feature value set by adopting a self-adaptive singular value decomposition method to obtain a singular value threshold number K, and selecting the first K feature values in the feature value set to form an approximate matrix; according to the invention, the image compression quality can be improved, and a high compression ratio can be obtained.

Description

technical field [0001] The invention relates to the technical field of image compression, in particular to an image compression method combining wavelet packet transformation and singular value decomposition. 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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