Using Fuzzy Neural Network to Assess Network Video Quality

Abstract

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.

Aeroengine performance degradation prediction method considering operating conditions

“…These methods are mainly aimed at building a data model, adjusting the internal parameters of the model by inputting training data samples, and making predictions by testing data samples. Nowadays, many prediction methods have been proposed to realize system behavior prediction, including improved autoregressive movingaverage-model (ARMA) methods [10], support-vector machine (SVM) [11][12][13], neural networks [14][15], etc. In general, data-driven methods have shown great advantages in some fields, but most of these methods belong to black box models and lack of interpretability.…”