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In this paper we present a parametric model for audiovisual quality estimation in IPTV and similar services. The proposed model takes advantage of signal characteristics calculated at the sender (in particular related to levels of motion in the content), but is purely parametric on the estimation (i.e. it does not require peeking into the bitstream), which makes it suitable for large-scale real-time monitoring applications. In order to obtain the model, we followed the Pseudo-Subjective Quality Assessment (PSQA) methodology, and compared different kinds of statistical estimators, namely Multilayer Perceptrons (MLP) and Random Neural Networks (RNN).