Objective image quality evaluation method based on manifold feature similarity

Abstract

The invention discloses an objective image quality evaluation method based on manifold feature similarity. The method comprises the following steps: at first, using a visual salience strategy and a visual threshold strategy to remove an image block which is not important to visual perception, namely a rough selection process and a fine selection process of the image block, extracting a manifold feature vector of the selected image block from an original undistorted natural scene image and a distorted image to be evaluated by using an optimal mapping matrix after selecting the image block, and then evaluating the structural distortion of the distorted image through the manifold feature similarity; thereafter, considering the influence of the image brightness change on human eyes, and calculating the brightness distortion of the distorted image based on a mean value of the image block; and finally, obtaining a quality score according to the structural distortion and the brightness distortion. Therefore, the method has higher evaluation accuracy, the evaluation ability of all kinds of distortion is expanded, the evaluation performance is not influenced by the image contents or the distortion type, and the consistency with the subjective perception quality of the human eyes is relatively high.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an objective image quality evaluation method based on manifold feature similarity. Background technique [0002] Quantitative evaluation of image quality is a challenging problem in the field of image processing. Since humans are the ultimate recipients when viewing images, image quality assessment methods should be as effective in predicting perceived visual quality as humans. Although traditional image quality evaluation methods based on fidelity such as Peak Signal-to-Noise Ratio (PSNR) can better evaluate the image quality with the same content and distortion, they face multiple images and various distortions. However, the evaluation results are far from the subjective perception. The purpose of the perceptual quality evaluation method is to obtain an evaluation result with high consistency with the visual perception quality by simulating the overall perception mechanis...

AI Technical Summary

Problems solved by technology

The above structure-based image quality evaluation methods all obtain image quality from structural information such as the edge and contrast of the image, while the image quality evaluation methods designed from the characteristics of the human visual system mainly focus on the ability of the human eye to detect distortion. The evaluation of image quality is based on the nonlinear geometric structure of the image and the perception of the human eye; but some studies have shown that for visual perception phenomena, the manifold is the basis of perception, and the brain uses the manifold Perceive things, and natural scene images usually contain manifold structures, which have the nature of manifold nonlinearity

Therefore, traditional image quality evaluation methods cannot obtain objective evaluation results that are consistent with subjective perception quality

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