Full-reference image quality evaluation method based on masking texture features

An image quality evaluation and texture feature technology, applied in image enhancement, image analysis, image data processing and other directions, can solve problems such as the inability to accurately reflect the visual masking effect of the human eye.

Active Publication Date: 2019-01-11
XIAN UNIV OF TECH
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Problems solved by technology

[0005] The purpose of the present invention is to provide a full-reference image quality evaluation method based on masked texture features, which solves the problem that the existing evaluation methods cannot accurately reflect the masking effect of human vision and ignore the

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  • Full-reference image quality evaluation method based on masking texture features
  • Full-reference image quality evaluation method based on masking texture features
  • Full-reference image quality evaluation method based on masking texture features

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[0056] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0057] The full-reference image quality evaluation method based on masked texture features of the present invention, such as figure 1 As shown, it can be divided into two parts, respectively: the establishment of the RF model and the prediction of image quality evaluation: the establishment part of the RF model, the processing object is the reference image and the distorted image in the image database, extracting the The mean and variance of the three similarity features, combined with the subjective MOS value in the database, use random forest RF to build a regression model;

[0058] The prediction part of the image quality evaluation calculates the gradient magnitude similarity, gradient direction similarity, texture similarity mean, texture similarity standard deviation, color difference mean and color difference standard deviation of the ...

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Abstract

The invention discloses a full-reference image quality evaluation method based on masking texture features, belonging to the technical field of image processing and image quality evaluation. Firstly,the reference image and the distorted image are converted into color space. Secondly, the gradient amplitude and gradient direction of the reference image and the distorted image are extracted and thesimilarity of the gradient information of the image is calculated. Then, the similarity and color difference of the texture features are calculated and the mean and standard deviation of the similarity and color deviation are counted, a 6-Deigenvector is formed, according to that random forest, a regression model is established to fuse the characteristic vector and the subjective MOS value, and the training is carried out. Finally, the 6-D eigenvectors are input into the trained regression model to complete the objective image quality evaluation. The evaluation method disclosed by the invention adopts three different similarity characteristics, uses random forest to establish regression model, realizes full-reference image quality to carry out high-precision objective evaluation, and canmaintain high consistency with human visual characteristics.

Description

technical field [0001] The invention belongs to the technical field of image processing and image quality evaluation, and relates to a full-reference image quality evaluation method based on masked texture features. Background technique [0002] With the advent of the era of big data, more and more images are shared on the Internet. As an important carrier for people to obtain information and communicate, digital images are gradually changing people's lifestyles. With the large increase in data size, it also brings great challenges. During the process of image collection, storage, transmission and processing, a certain degree of distortion may occur. Therefore, how to effectively process and transmit images and accurately evaluate image quality has become an urgent research problem. [0003] In recent years, full-reference image quality assessment algorithms and corresponding devices are widely used in various image processing systems to optimize parameters, so full-refere...

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Application Information

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IPC IPC(8): G06T7/00G06T7/90
CPCG06T7/0002G06T2207/20081G06T2207/30168G06T7/90
Inventor 郑元林王玮唐梽森廖开阳于淼淼
Owner XIAN UNIV OF TECH
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