Image quality evaluation method based on combination neural network and classification neural network

Abstract

The invention discloses an image quality evaluation method based on a combination neural network and a classification neural network. In the training stage, an objective reality quality image of a distortion image obtained by adopting a full-reference image quality evaluation method is adopted as supervision, a normalized image of the distortion image is trained to obtain a combination neural network regression training model for different distortion types; a classification label of the distortion image is adopted as supervision, and the normalized image of the distortion image is trained to obtain a classification neural network training model; in the testing stage, the normalized image of the distortion image to be evaluated is input into the classification neural network training model,and a distortion type is obtained; according to the distortion type, the normalized image is input into the corresponding combination neural network regression training model to obtain an objective quality evaluation prediction quality map, and adopting a saliency map for performing weighing pooling on the objective quality evaluation prediction quality map, and obtaining an objective quality evaluation prediction value. The method has the advantage that the correlation between the objective evaluation result and subjective perception is effectively improved.

Description

technical field [0001] The invention relates to an image quality evaluation method, in particular to an image quality evaluation method based on a combined neural network and a classification neural network. Background technique [0002] With the rapid development of image processing, machine learning, and computer vision, image quality assessment has become a research field that has attracted more and more attention, because it is an important technique in practical applications and can be used to accurately evaluate the quality of images. In the process of image acquisition, transmission, compression, storage, and display, there are often different degrees of distortion, such as image blur, video terminal image distortion, and image quality in the system are not up to standard. Therefore, an effective image quality evaluation mechanism should be established Very important. [0003] In general, image quality assessment can be roughly divided into two different categories: ...

AI Technical Summary

Problems solved by technology

Objective image quality evaluation methods are mainly divided into three evaluation methods, namely, full-reference image quality evaluation method, semi-reference image quality evaluation method and no-reference image quality evaluation method. The corresponding original image cannot be obtained in the application, so the research of no reference image quality evaluation method is more practical

[0004] The existing general-purpose non-reference image quality evaluation methods are mainly for multiple distortions, and the pertinence for a specific distortion is not strong enough. Therefore, the various distortions are first classified, and then for a specific Image quality evaluation based on distortion has become a direction that people pay more and more attention to

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