An Objective Evaluation Method of Image Quality Based on Relative Gradient

Abstract

The invention discloses a relative gradient-based image quality objective evaluation method. According to the method, the respective gradient magnitude images and gradient phase images of a reference image and a distorted image are acquired; the respective horizontal gradient mean images, vertical gradient mean images and gradient phase mean images of the reference image and the distorted image are acquired; the respective gradient relative amplitude images of the reference image and the distorted image are obtained according to the respective horizontal gradient images, vertical gradient images, horizontal gradient mean images and vertical gradient mean images of the reference image and the distorted image; the respective gradient relative phase images of the reference image and the distorted image are obtained according to the respective gradient phase images and the gradient phase mean images of the reference image and the distorted image; and the objective quality evaluation scores of the distorted image are obtained according to the gradient amplitude images, gradient relative amplitude images and gradient relative phase images of the reference image and the distorted image. According to the relative gradient-based image quality objective evaluation method of the invention, the influence of the change of the relative gradient information of the distorted image on the quality of the distorted image is considered, and therefore, the correlation of an objective quality evaluation result and the subjective perception of the eyes of a person can be improved.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an objective image quality evaluation method based on a relative gradient. 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 to guide the entire imag...

AI Technical Summary

Problems solved by technology

Traditional full-reference image quality evaluation methods, such as PSNR and other indicators, have been widely used in image processing and coding technologies due to their low computational complexity and clear mathematical meaning. However, they are based on pixel error statistics and are not sufficient. Considering the correlation between pixels and the visual characteristics of the human eye, it cannot reflect the subjective feeling of the image well.

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