Network video quality assessment method using fuzzy decision tree

Shi, Zhiming; Huang, Chengti

doi:10.1049/iet-com.2019.0062

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…Florin Dobrain and others are researchers who have accessed data analyses through real online video platforms for the first time. They mainly explore the correlation between the member service scene and user participation (Shi and Huang) [ 13 ]. This is the first experiment using real scene data analyses, so the experimental results have certain reliability.…”

Section: Related Workmentioning

confidence: 99%

MOOC Teaching Model of Basic Education Based on Fuzzy Decision Tree Algorithm

Yuanyuan

2022

Computational Intelligence and Neuroscience

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In recent years, the development of science and technology in China has greatly affected people’s ways of entertainment. In the traditional industrial model, new industries and Internet industries represented by the Internet have emerged, and the Internet video business is an emerging business that has been gradually emerging in the Internet industry in recent years. Moreover, this new teaching method has been gradually noticed in simple education, such as MOOC, I want to self-study network, and Smart Tree, and other online learning websites have sprung up. At present, the epidemic environment makes people pay more attention to this convenient and wide range of online video education. Therefore, we need to evaluate this kind of online video teaching model from the effectiveness of this kind of method and the quality of user experience. This paper takes this as the starting point and chooses the earliest online video platform, MOOC, as the model to establish a set of perfect user experience quality evaluation methods suitable for domestic online video education mode. Considering the data source, the accuracy of the results, and other factors, we chose the industry-leading platform MOOC network as an example. Through the exploration of the MOOC teaching mode in basic education, a member experience evaluation model is established based on fuzzy decision tree algorithm. The experimental results show that the model has high accuracy and high reliability.

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Section: Related Workmentioning

confidence: 99%

MOOC Teaching Model of Basic Education Based on Fuzzy Decision Tree Algorithm

Yuanyuan

2022

Computational Intelligence and Neuroscience

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“…Garcia-Pineda et al [15] proposed an MLR algorithm, with a high perception accuracy, to analyze the relationship between evaluation indicators such as delay jitter and packet loss rate and QoE. Shi and Huang [16] proposed a video perception algorithm based on a decision tree, which comprehensively considered the mapping relationship between grid and video parameters (such as noise standard deviation, ambiguity and block effect, etc.) and users' subjective feelings, and designed a fuzzy logic system to give objective scores, which greatly improved the accuracy of user experience perception.…”

Section: Related Workmentioning

confidence: 99%

User Experience Perception Algorithm for Long and Short Videos Based on Multiple Nonlinear Regression

Dai

Zhou

Tian

et al. 2022

Mobile Information Systems

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The low correlation of evaluation indices from the current user perception evaluation model and the neglect of the nonlinear relationship between diversified indices and user experience in different duration videos result in low user perception accuracy of long and short videos. To address these issues, we propose a user experience perception algorithm for long and short videos based on multiple nonlinear regression (LSMNR). First, to improve the efficiency and accuracy of modeling, the algorithm involves preprocessing of video data in edge servers and subdivides the videos based on their duration and popularity. Then, we introduce a new multidimensional quantitative evaluation index that fits the user’s subjective experience and further analyze the influence between multiple evaluation indices (video lag, black screen, etc.) and user quality of experience (QoE) for different video types. Moreover, the characteristics of the data in the multiple evaluation indices are extracted; user subjective evaluation experiments are designed using the video quality expert group (VQEG) standard; and sample and test databases were established. Finally, the optimal model parameters were trained by applying the nonlinear least square method and support vector machine (SVM) to fit and cross-verify the sample data. Our simulation results revealed that the Pearson correlation coefficient of the proposed LSMNR algorithm acquires a value of 0.9810. Compared with algorithms based on multinomial linear regression (MLR), linear SVM, and neural network (NN), the perceptual accuracy of the proposed algorithm is improved by at least 4.0%, and it is applicable to a wider range of video types.

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“…All the users are provided a controllable environment to watch videos. Next the subjective scores are given by users and analyzed [3]. The objective quality assessment (OQA) is the second category.…”

Section: Introductionmentioning

confidence: 99%

Using Fuzzy Neural Network to Assess Network Video Quality

2022

KSII TIIS

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At present people have higher and higher requirements for network video quality, but video quality will be impaired by various factors, so video quality assessment has become more and more important. This paper focuses on the video quality assessment method using different fuzzy neural networks. Firstly, the main factors that impair the video quality are introduced, such as unit time jamming times, average pause time, blur degree and block effect. Secondly, two fuzzy neural network models are used to build the objective assessment method. By adjusting the network structure to optimize the assessment model, the objective assessment value of video quality is obtained. Meanwhile the advantages and disadvantages of the two models are analysed. Lastly, the proposed method is compared with many recent related assessment methods. This paper will give the experimental results and the detail of assessment process.

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Network video quality assessment method using fuzzy decision tree

Cited by 5 publications

References 35 publications

MOOC Teaching Model of Basic Education Based on Fuzzy Decision Tree Algorithm

MOOC Teaching Model of Basic Education Based on Fuzzy Decision Tree Algorithm

User Experience Perception Algorithm for Long and Short Videos Based on Multiple Nonlinear Regression

Using Fuzzy Neural Network to Assess Network Video Quality

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