Adaptive direction lifting wavelet compression algorithm on basis of gray level co-occurrence matrix

Abstract

The invention relates to the technical field of image processing and relates to a direction lifting image encoding method on the basis of a gray level co-occurrence matrix. The method comprises the following steps of: dividing an image into many sub-blocks with equal size, and differentiating texture parts and non-texture parts of each sub-block by an angular second-order method in the gray level co-occurrence matrix; directly performing ordinary horizontal and vertical lifting on the blocks with less direction information; applying a direction lifting wavelet principle to the blocks with more direction information; and combining a Spiht encoding method and an arithmetic coding method to respectively encode conversion coefficients and the direction information, and applying the encoded conversion coefficients and the direction information to image compression. Experiment results show that: compared with an automatic data logger (ADL) algorithm, the direction lifting image encoding method can obviously reduce the direction wavelet conversion time. Under the same code rate, the power signal-to-noise ratio (PSNR) is not greatly changed; and at the same time, under a low code rate, the PSNR is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an adaptive direction-lifting wavelet compression algorithm based on a gray level co-occurrence matrix. Background technique [0002] Discrete wavelet transform is a very good image processing method, which provides a multi-resolution image display method and perfect image reconstruction ability, so it is widely used in image analysis, compression coding and other fields. For example, JPEG2000, an international standard for still image compression. In 1995, Swelden proposed a new wavelet construction scheme that does not depend on Fourier transform—lifting wavelet transform. This method not only obtains a new wavelet transform, but also reduces the computational complexity of the existing wavelet transform. Practice has proved that all wavelet transforms can be realized by lifting methods, so the lifting wavelet transform is also called the second generation...

AI Technical Summary

Problems solved by technology

These methods have achieved good results, but there are still some shortcomings: high computational complexity and complex filter design

However, this direction needs to be interpolated during the transformation, and it is also necessary to judge and choose the optimal direction, and the amount of calculation is relatively large.

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