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Design of algorithms that are able to estimate video quality as perceived by human observers is of interest for a number of applications. Depending on the video content, the artifacts introduced by the coding process can be more or less pronounced and diversely affect the quality of videos, as estimated by humans. While it is well understood that motion(More)
The paper describes a neuro-fuzzy identification approach, which uses numerical data as a starting point. The proposed method generates a Takagi-Sugeno fuzzy model, characterized with transparency, high accuracy and a small number of rules. The process of self-organizing of the identification model consists of three phases: clustering of the input-output(More)
This paper investigates a nonparametric modular neural network (MNN) model to price the S&P-500 European call options. The modules are based on time to maturity and moneyness of the options. The option price function of interest is homogeneous of degree one with respect to the underlying index price and the strike price. When compared to an array of(More)
This paper presents a novel no-reference video quality assessment (VQA) model which is based on non-linear statistical modeling. In devised non-linear VQA model, an ensemble of neural networks is introduced, where each neural network is allocated to the specific group of video content and features based on artifacts. The algorithm is specifically trained to(More)