Image quality evaluation method based on sparse structure

Abstract

The invention discloses an image quality evaluation method based on a sparse structure. The method is used for solving the technical problem that the evaluation effect of an existing image quality evaluation method is poor. According to the technical scheme, firstly, input reference images and input degraded images are sampled to obtain a reference image sampling matrix and a degraded image sampling matrix; then, a dictionary is obtained in a studying mode through the reference image sampling matrix, and in the process of working out a sparse solution, sparse representation is carried out on the reference image sampling matrix and the degraded image sampling matrix through the dictionary obtained in the studying mode to obtain a reference image sparse representation coefficient matrix and a degraded image sparse representation coefficient matrix; finally, the image quality is evaluated according to the change degree of the sparse coefficient structure. According to the method, by the adoption of the sparse structure in image quality evaluation, the image quality can be better evaluated. In addition, calculation is simpler, and the robustness is higher because the amplitude of a specific sparse representation coefficient is not involved.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an image quality evaluation method based on a sparse structure. Background technique [0002] As an important research direction in the field of image processing, image quality evaluation involves many fields such as image recognition, biomedicine, industrial process monitoring, and battlefield surveillance, and has attracted the attention of researchers. [0003] Document 1 "Guha.Tanaya,Nezhadarya.Ehsan,Ward.Rabab K.Learning sparse models for image quality assessment.Acoustics,Speech and Signal Processing(ICASSP),2014IEEE International Conference on,4-9May2014,Florence Italy.P151-155" An image quality evaluation method based on a sparse representation model is disclosed. The method is divided into two stages. The first stage is the dictionary learning stage: firstly, the reference image is randomly sampled to obtain training samples, and then the dictionary is learned from ...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of poor evaluation effect of existing image quality evaluation methods, the present invention provides an image quality evaluation method based on sparse structure

Then use the reference image sampling matrix to learn the dictionary

Finally, the image quality is evaluated using the sparse coefficient structure change degree

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