Image quality evaluating method based on support vector machine

An image quality evaluation and support vector machine technology, applied in the field of image processing, can solve problems such as poor generalization ability, inconsistent subjective evaluation and objective observation, and poor reliability, and achieve the effect of increasing reliability.

Inactive Publication Date: 2009-09-23
BEIHANG UNIV
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Problems solved by technology

[0011] The purpose of the present invention is to address the deficiencies in the prior art: slow processing, poor reliability, poor generalization ability, subjective evaluatio

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  • Image quality evaluating method based on support vector machine
  • Image quality evaluating method based on support vector machine
  • Image quality evaluating method based on support vector machine

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Embodiment Construction

[0052] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0053] The image quality evaluation method of the present invention can be divided into four steps to complete, and the step flow is as follows: figure 1 Shown:

[0054] Step 1, establish a sample set;

[0055] Such as figure 2 As shown, after denoising the image sample, four eigenvalues ​​of contrast, entropy, texture and blur are extracted to form a eigenvalue vector (p1, p2, p3, p4), and the coordinates are normalized for post-processing.

[0056] Subjective expert quality assessment of image samples. In order to ensure that the subjective evaluation is statistically meaningful, it is necessary to consider both untrained "lay" observers and "expert" observers with certain experience in image technology when selecting observers; There must be at least 20 observers, and the test conditions should match the conditions of u...

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Abstract

The invention provides an image quality evaluating method based on a support vector machine. The method comprises the following steps: first, a preprocessed image sample is selected and extracted according to characteristic value, a processed sample set is respectively divided into a training set and a testing set; secondly, the training set is used for training the support vector machine, the number of the support vector machine is ensured according to a certain level which is required by a system, thus ensuring each support vector machine to be trained, wherein, an input sample is the characteristic value of the image and an output sample is the level of the image quality; thirdly, the trained support vector machine is used for adjusting and optimizing correlation parameters with the testing set and determining the parameter of the decision function of the optimal hyperplane of the support vector machine model; and finally, the support vector machine model which is trained and optimized is used for evaluating the quality level of the image sample. The invention has the advantages of little required sample, fast arithmetic speed, high precision, good performance, strong popularization, etc.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image quality evaluation method based on a support vector machine. Background technique [0002] Correct evaluation of image quality is a very meaningful research topic in the field of image information engineering. Image quality evaluation methods are generally divided into two categories: subjective and objective. The image is ultimately viewed by people, so the most accurate evaluation method for its quality is subjective evaluation, but there are many problems in the practical application of subjective evaluation methods, so people are unremittingly devoting themselves to designing objective evaluation methods to approximately reflect the subjective feeling requirements. [0003] At present, the grading estimation and evaluation of remote sensing images based on the degree of interpretation has been carried out internationally. Some countries have for...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
Inventor 丁文锐王磊李红光
Owner BEIHANG UNIV
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