Screen image quality evaluation method based on channel features and convolutional neural network

Abstract

The present invention discloses a screen image quality evaluation method based on channel features and a convolutional neural network. According to the method, ten channel feature images of a distorted screen image to be evaluated are extracted, the respective normalized images of the channel feature images are obtained; the ten channel feature images of each distorted screen image in a training set and the respective normalized images of the channel feature images are obtained through the same manner; the convolutional neural network is adopted to train the respective subjective scores of allthe distorted screen images in the training set and the normalized images of the ten channel features so as to obtain an optimal weight vector and a bias term, so that a convolutional neural networkregression training model is constructed; and a normalized image corresponding to the distorted screen image to be evaluated is tested according to the convolutional neural network regression trainingmodel, so that the objective quality evaluation predictive value of the distorted screen image to be evaluated can be obtained. Since the influence of various features of the screen image on visual quality is fully taken into consideration, and correlation between an objective evaluation result and subjective perception can be improved.

Description

technical field [0001] The invention relates to a no-reference image quality evaluation method, in particular to a screen image quality evaluation method based on channel features and a convolutional neural network. Background technique [0002] With the rapid development of the image processing industry, image quality evaluation has become an increasingly important component, and people's requirements for image quality are also increasing. Due to the process of image acquisition, storage, transmission and display, there are often different degrees of distortion, such as image blur, video terminal image distortion, image quality in the system is not up to standard, etc. 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 process of image denoising and image fusion; it can be used to guide the entire image transmission process an...

AI Technical Summary

Problems solved by technology

[0005] The existing general-purpose no-reference image quality evaluation methods are mainly aimed at general images, but there are relatively few studies on special images (such as screen images). Since screen images contain text, graphics, and images, etc., the general It is more challenging to perform quality assessment without reference image quality assessment methods

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