Reference-free image objective quality evaluation method based on gradient self-similarity

Abstract

The invention discloses a reference-free image objective quality evaluation method based on gradient self-similarity. According to the process of the method, first, gradient filtering is performed on a to-be-evaluated distorted image to obtain an amplitude image of gradient information; second, information images in four directions are obtained according to the amplitude image of the gradient information, and a self-similarity image between the information image in each direction and the amplitude image is calculated; third, inter-pixel feature extraction method operation and intra-pixel feature extraction method operation are performed on the self-similarity images to obtain an inter-pixel feature graph and an intra-pixel feature graph; fourth, a histogram statistical method is adopted to perform statistical analysis on the inter-pixel feature graph and the intra-pixel feature graph; and last, support vectors are adopted to perform regression prediction on an objective quality evaluation predicted value of the to-be-evaluated distorted image according to histogram statistical feature vectors. The method has the advantages that influences of changes of image gradient self-similarity on visual quality can be fully considered, and therefore the correlation between an 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 non-reference image objective quality evaluation method based on gradient self-similarity. Background technique [0002] Image is an important way for humans to obtain information. Image quality indicates the ability of an 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, degradation problems will inevitably occur, which brings great difficulties to information acquisition or image post-processing. 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 the processing of image denoising and image fusion; it can be used to guide the entire image tran...

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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