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Video quality evaluation method and system combining support vector machine and fuzzy reasoning

A support vector machine and video quality technology, applied in reasoning methods, character and pattern recognition, computer components, etc., can solve problems such as simplification of reasoning process, high complexity of evaluation methods, low accuracy of evaluation methods, etc., to improve Effects of subjective and objective similarity, reduction of reasoning steps, and complexity reduction

Active Publication Date: 2021-02-05
HUAQIAO UNIVERSITY
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  • Application Information

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Problems solved by technology

[0003] The existing objective evaluation method based on fuzzy reasoning is to linearize all the inference rules, which easily leads to high complexity of the evaluation method, simplification of the reasoning process, and low accuracy of the evaluation method

Method used

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  • Video quality evaluation method and system combining support vector machine and fuzzy reasoning
  • Video quality evaluation method and system combining support vector machine and fuzzy reasoning
  • Video quality evaluation method and system combining support vector machine and fuzzy reasoning

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Experimental program
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Embodiment 1

[0065] This embodiment provides a video quality assessment method of joint support vector machine and fuzzy reasoning, such as figure 1 As shown, including: support vector machine model optimization process and video quality assessment process;

[0066] The support vector machine model optimization process includes:

[0067] Obtain the subjective evaluation values ​​of a plurality of training videos, classify the training videos according to the subjective evaluation values; extract the influencing factors of each type of training videos respectively, and then input the influencing factors and corresponding classification input support vector machine models for Training, get optimized support vector machine model;

[0068] The video quality assessment process includes:

[0069] Extracting the influencing factors of the video to be evaluated, inputting the influencing factors of the video to be evaluated into the optimized support vector machine model, obtaining the classifie...

Embodiment 2

[0093] In this embodiment, a video quality assessment system of a joint support vector machine and fuzzy reasoning is provided, such as figure 2 As shown, including: support vector machine model optimization module and video quality evaluation module;

[0094] The support vector machine model optimization module is used to obtain the subjective evaluation values ​​of a plurality of training videos, and classify the training videos according to the subjective evaluation values; extract the influencing factors of each type of training videos respectively, and then Factors and corresponding classification input support vector machine model for training to obtain optimized support vector machine model;

[0095] The video quality evaluation module is used to extract the influencing factors of the video to be evaluated, and input the influencing factors of the video to be evaluated into the optimized support vector machine model to obtain the classified influencing factors;

[009...

Embodiment 3

[0117] A specific embodiment of the present application, the general idea is as follows:

[0118] This embodiment combines support vector machines and fuzzy inference algorithms. First, an experimental platform is built to extract video quality influencing factors. Second, nonlinear support vector machines are used to classify video quality, and the classified influencing factors are divided into two groups, respectively. Carry out fuzzy reasoning, and finally weight the result of reasoning to get the final objective value. The embodiment of the present invention will give the above simulation process, and verify the effectiveness of the method.

[0119] 1. Experimental process

[0120] The experiment extracted the main influencing factors of video quality, they are respectively, the application index: the average number of pauses (F rebuf ), the average pause time (T rebuf ), the higher these two indicators per unit time will cause repeated buffering of the video, reducing...

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Abstract

The invention provides a video quality evaluation method and system combining a support vector machine and fuzzy reasoning, and the method comprises the steps: firstly extracting main influence factors of video quality, classifying the video quality through an optimized nonlinear support vector machine model, and then dividing the classified influence factors into two groups, and carrying out fuzzy reasoning according to the rule corresponding to each classification, and weighting reasoning result value to obtain a final objective value. Experimental results show that the method can effectively improve subjective and objective similarity of video quality evaluation.

Description

technical field [0001] The invention relates to the field of video quality assessment, in particular to a video quality assessment method and system of a joint support vector machine and fuzzy reasoning. Background technique [0002] At present, people watch online video anytime and anywhere, and it is the busiest network service. However, during the transmission and encoding process of video, it will be affected by various factors. For this reason, various methods for evaluating video quality have been devised. Subjective and objective evaluation methods of video quality are two commonly used evaluation methods. Since subjective evaluation methods require human participation, many research institutions have focused on objective evaluation methods. [0003] The existing objective evaluation method based on fuzzy reasoning is to linearize all the inference rules, which easily leads to high complexity of the evaluation method, simplification of the reasoning process, and lo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N5/04
CPCG06N5/048G06V20/41G06F18/2411Y02T10/40
Inventor 史志明黄诚惕唐加能
Owner HUAQIAO UNIVERSITY
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