A No-Reference Natural Image Quality Assessment Method Combining Multiple Features

Abstract

The present invention relates to a non-reference natural image quality evaluation method combining multiple features, by calculating 8 features, and then constructing a vector f i =[feature HBC feature NCSD feature H feature L feature HDW feature EAV feature ASD feature N ] T Transform the feature vector of the image into the evaluation probability matrix of the unlabeled image, and finally get the score d of the unlabeled image i i =[x 1 x 2 x 3 x 4 x 5 ], the final evaluation probability weighted a of the unlabeled image i i =0*x 1 +1*x 2 +2*x 3 +3*x 4 +4*x 5 ; The non-reference image quality evaluation method combined with multiple features, combined with multiple image features to evaluate image quality, can comprehensively consider image noise, image clarity and other features, can better classify images, and has better universality It can adapt to the image quality evaluation of various characteristics and facilitate the scientific classification of images.

Description

technical field [0001] The invention belongs to the technical field of image quality evaluation, and in particular relates to a non-reference natural image quality evaluation method combining multiple features. Background technique [0002] With the rapid development of multimedia equipment in recent years, the public's requirements for image quality have increased dramatically, so image quality evaluation has received great attention in recent years. Image quality assessment is divided into three categories: full reference, semi-reference, and no-reference image quality assessment. Since the undistorted ontology information cannot be obtained in most practical situations, the no-reference image quality assessment method is the most practical. [0003] 1. Full reference algorithm: The basic idea is to compare the local differences between the distorted image and the reference image by designing features, and then find a total average statistic on the entire image, and associ...

AI Technical Summary

Problems solved by technology

However, the current no-reference algorithm cannot get rid of the influence of image content on its evaluation results

[0006] The existing no-reference image quality evaluation methods are mainly divided into two categories. The first category mainly evaluates one feature. Due to the complexity of the image, it is impossible to accurately simulate the mapping relationship between the image and the perceived quality. A single type of feature only Can judge specific distortion, lack of universality

The second type of method is mainly based on statistical laws to judge, but there are many types of images, and the statistical laws of different types of images are very different. This method is only effective for specific types of images.

Images