No-reference image quality evaluation method based on gradient information

Abstract

The invention discloses a no-reference image quality evaluation method based on gradient information. Through deep digging of perception characteristics of human vision for image structure, gradient filtering is performed on a distorted image, so as to obtain an amplitude image and a phase image of the gradient information; a local binary pattern operation is performed on the amplitude image and the phase image so as to obtain a local binary pattern characteristic image of the amplitude image and a local binary pattern characteristic image of the phase image; then condition probability characteristics of all pixel points of different pixel values in the amplitude image and the phase image are gained; and finally, according to the condition probability characteristics, an objective quality evaluation prediction value of the distorted image to be evaluated is predicted by use of support vector regression. The method has the advantages that the impact of gradient structure change on visual quality is fully considered, so that the obtained objective quality evaluation prediction value can accurately reflect the subjective perception quality of human vision, and the correlation between an objective evaluation result and subjective perception can be improved effectively.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to a non-reference image quality evaluation method based on gradient information. Background technique [0002] Image quality is the main performance index for evaluating the quality of image processing systems and algorithms. 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 quality evaluation methods, semi-reference image quality evaluation methods, and no-reference image quality eva...

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 quality evaluation methods, semi-reference image quality evaluation methods, and n

Images