General reference-free image quality evaluation method based on local contrast mode

Abstract

The invention discloses a general reference-free image quality evaluation method based on a local contrast mode. The method comprises acquiring histogram statistical characteristic vectors for the respective local binary mode characteristic image for 8 direction information images and 1 directionless information image for every distortionless image in the training stage, and making all the histogram statistical characteristic vectors to form a dictionary learning characteristic matrix; acquiring histogram statistical characteristic vectors for the respective local binary mode characteristic image for 8 direction information images and 1 directionless information image for a distortion image to be evaluated in the test stage, and making all the histogram statistical characteristic vectors to form a characteristic vector; and according to the dictionary learning characteristic matrix and the characteristic vector, utilizing a sparse algorithm to acquire a visual perception sparse characteristic vector, and then utilizing a support vector to realize regression, and obtaining an objective quality evaluation predicted value through prediction according to the visual perception sparse characteristic vector. The general reference-free image quality evaluation method based on a local contrast mode has the advantage of being effectively improve the correlation between an objective evaluation result and a subjective perception.

Description

technical field [0001] The invention relates to an objective image quality evaluation method, in particular to a general non-reference image quality evaluation method based on a local contrast mode. Background technique [0002] Image is an important way for human beings to obtain information. Image quality indicates the ability of image to provide information to people or equipment, and is directly related to the adequacy and accuracy of the information obtained. However, in the process of image acquisition, processing, transmission and storage, due to various factors, there will inevitably be degradation problems, which brings great difficulties to information acquisition or post-processing of images. Therefore, it is very important to establish an effective image quality evaluation mechanism. For example, it can be used for performance comparison and parameter selection of various algorithms in image denoising, image fusion and other processing processes; it can be used t...

AI Technical Summary

Problems solved by technology

Objective evaluation methods can be divided into three categories: full-reference image quality evaluation methods, semi-reference image quality evaluation methods, and no-reference image quality evaluation methods. Therefore, the research of no-reference image quality evaluation method is more practical

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