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Network video quality assessment based on fuzzy inference system

The Journal of China Universities of Posts and Telecommunications 2018年第1期25卷 70-77页

作者： Shi Zhiming Huang Chengti College of engineering Huaqiao University Quanzhou 362021 China fujian provincial academic engineering research centre in industrial intellectual techniques and systems Quanzhou 362021 China

The objective assessment method of network video quality is a challenge, because the video quality will be distorted by various factors, including transmission and compression. In order to improve the objective method, an objective assessment method based on fuzzy inference system of Mamdani is proposed. Firstly, six quality parameters are introduced. All the quality parameters are inputted to fuzzy logic controller system. Secondly, the outputs are used as next inputs and inferred by another fuzzy logic controller system to obtain the objective quality of network video. Lastly, the performance of proposed method is validated on four videos with different network environment. Meanwhile this method is compared with other methods. The experimental results show that the proposed method can improve the similarity between subjective and objective assessment.

关键词： network video quality parameter fuzzy inference system objective assessment

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Quality of experience models for network video quality

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The Journal of China Universities of Posts and Telecommunications 2019年第4期26卷 80-88页

作者： Shi Zhiming Huang Chengti College of engineering Huaqiao UniversityQuanzhou 362021China fujian provincial academic engineering research centre in industrial intellectual techniques and systems Huaqiao UniversityQuanzhou 362021China

Nowadays,the service of network video is increasing explosively.But the quality of experience(QoE)model of network video quality is not stable.The video quality may be impaired by many factors.This paper proposes QoE models for network video quality.It consists of two components:1)the perceptual video quality model considering the impair factors related to video content as well as distortion caused by content and transmission.Next the model is built through a decision tree using a set of measured features form the network video.This proposed model can qualitatively give the grade of video quality and improve the accuracy of prediction.2)Based on the above model,another model is proposed to give the concrete objective score of video quality.It also considers original impair factors and predicts the video quality using fuzzy decision tree.The two models have their own advantages.The first model has a good computational complexity;the second model is more precise.All the models are simulated by actual experiments.They can improve the accuracy of objective model.The detail results are shown.

关键词： perceptual video quality QoE objective model fuzzy decision tree accuracy

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维普期刊数据库

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Pedestrian attribute classification with multi-scale and multi-label convolutional neural networks

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High Technology Letters 2018年第1期24卷 53-61页

作者：朱建清 Zeng Huanqiang Zhang Yuzhao Zheng Lixin Cai Canhui fujian academic engineering research centre in industrial intellectual techniques and systems College of Engineering Huaqiao University Quanzhou 362021 P. R. China School of Information Science and engineering Huaqiao University Xiamen 361021 P. R. China

Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin.

关键词： pedestrian attribute classification multi-scale features multi-label classification convolutional neural network (CNN)

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