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Image Quality Evaluation Method Based on Local Average Eigenvalue

A technology of image quality evaluation and local averaging, applied in the field of image processing, to achieve the effect of comprehensive and accurate objective evaluation

Active Publication Date: 2022-03-15
TIANJIN UNIV
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Problems solved by technology

Although these algorithms have good performance, there is still a lot of room for development in non-parametric quality evaluation. According to the current research, there are few studies on using eigenvalues ​​for image quality evaluation. Therefore, the present invention proposes a method based on An Efficient Non-parametric Quality Evaluation Method for Eigenvalues

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  • Image Quality Evaluation Method Based on Local Average Eigenvalue
  • Image Quality Evaluation Method Based on Local Average Eigenvalue
  • Image Quality Evaluation Method Based on Local Average Eigenvalue

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Embodiment approach

[0020] The present invention first calculates its local mean eigenvalues ​​(LMEs) from the normalized image matrix, analyzes its correlation with image perception quality, and then calculates the magnitude, variance, entropy and contrast (NSS feature) of the image matrix , and then use LMEs and the calculated multiple features to perform simple dictionary learning, and calculate the predicted quality score, and then make a more comprehensive and accurate objective evaluation of the image. The implementation is as follows:

[0021] 1. The image quality evaluation method based on local mean characteristic value, comprises the following steps:

[0022] Step 1: Normalize the pixel values ​​of the image to obtain the normalized image matrix A, and calculate its local mean eigenvalues ​​(LMEs). The calculation process of LMEs is as follows: segment the image matrix, divide it into N small square matrices of size S*S, calculate S eigenvalues ​​of each small square matrix, and then c...

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Abstract

The invention relates to an image quality evaluation method based on local average eigenvalues, comprising the following steps: normalizing the pixel values ​​of the image to obtain a normalized image matrix A, and calculating the local average eigenvalue LME of the image matrix A; From the normalized image matrix A, calculate its amplitude, variance, entropy and contrast, that is, NSS features, and combine the local average eigenvalues ​​of the image matrix A obtained in the first step, and use the obtained features for the sparse dictionary Learning; find the closest and sparsest feature representation to the test picture; the learned sparse representation coefficients will be used to calculate the quality Q of the test picture.

Description

technical field [0001] The invention belongs to the field of image processing and relates to an image quality evaluation method. Background technique [0002] With the rapid development of the Internet and the improvement of science and technology, many image information technologies are widely used. However, in the process of acquisition, processing, transmission and reception of images that people come into contact with in daily life, due to imperfect imaging systems, processing methods, transmission media and recording equipment, coupled with object movement and noise pollution, etc. It will inevitably bring some image distortion and degradation, which will affect human perception of images. Therefore, it is very important to evaluate the image quality, and it is necessary to understand the degree of image distortion. For example, it is necessary to evaluate the image quality of the distorted image after encoding and compression, so as to adjust the corresponding strateg...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06K9/62G06V10/772G06V10/774
CPCG06T7/0002G06T2207/10024G06T2207/20081G06T2207/30168G06F18/28G06F18/214
Inventor 杨嘉琛武建鹏
Owner TIANJIN UNIV