Universal no-reference image quality evaluation method based on phase selection mechanism

Abstract

The invention discloses a universal no-reference image quality evaluation method based on a phase selection mechanism. The method comprises the steps of firstly implementing Log-Gabor filtering on a distorted image to be evaluated, thus obtaining a multi-scale and multi-directional phase image; comparing the pixel value of each pixel point in the phase image with the pixel values of surrounding pixel points to obtain a local feature map; then obtaining the local feature mode chart of the local feature map by a rotational invariance method, and performing statistics on the local feature mode chart by a histogram statistic method, thus obtaining the histogram statistic feature vector of the distorted image to be evaluated; and at last obtaining the objective quality evaluation predicted value of the distorted image to be evaluated according to the distance between the histogram statistic feature vector of the distorted image to be evaluated and the histogram statistic feature vector of each distorted image in a training set. The method has the advantages that the influence of the change of the phase information on the visual quality can be fully considered, and the correlation between the objective evaluation result and subjective perception can be effectively improved.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a general non-reference image quality evaluation method based on a phase selectivity mechanism. 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; in the field of i...

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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