Non-reference image objective quality evaluation method

Abstract

The invention discloses a non-reference image objective quality evaluation method. The non-reference image objective quality evaluation method comprises the following steps: respectively applying Gaussian smoothing gradient filter and laplace operator Gaussian filter to a distorted image by deeply excavating perception characteristic of human vision to image structure to correspondingly obtain a Gaussian smoothing gradient filter image and a laplace operator Gaussian filter image, then respectively carrying out local binaryzation mode operation on the two filter images to obtain respective local binaryzation mode feature images, then calculating respective marginal probability feature and conditional probability feature of the two local binaryzation mode feature images, and finally forecasting on objective quality evaluation predicted value of the to-be-evaluated distorted images by adopting support vector regression according to the marginal probability feature and conditional probability feature. The obtained objective quality evaluation predicted value can accurately reflect subjective perceived quality of human vision and can be used for effectively improving correlation between objective evaluation result and subjective perception.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an objective quality evaluation method without a reference image. Background technique [0002] Image quality is the main performance index for evaluating the quality of image processing systems and algorithms. Digital image quality evaluation methods can be divided into two categories: subjective evaluation methods and objective evaluation methods. The former is scored by the observers on the image quality, and the average evaluation score is obtained to measure the image quality; the latter uses a mathematical model to calculate the image quality. The experimental results of the subjective evaluation method are relatively reliable, but time-consuming and labor-intensive. Objective evaluation methods can be divided into three categories: full-reference image objective quality evaluation methods, semi-reference image objective quality evaluation methods, and no-reference im...

AI Technical Summary

Problems solved by technology

The experimental results of the subjective evaluation method are relatively reliable, but time-consuming and labor-intensive

Objective evaluation methods can be divided into three categories: full-reference image objective quality evaluation methods, semi-reference image objective quality evaluation methods, and no-reference image objective quality evaluation methods.

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