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corresponds to the equipment impairment factor introduced by low bit-rate codecs.
Fog seems to be the most important impairment factor for FSO links [13].
Table 5 Impairment factor values for WB and NB speech codecs, derived on the basis of WB-PES Qestimations.
The degradations due to WB speech coding are captured by a one-dimensional parameter, called WB equipment impairment factor.
Two parameters are derived from signal-based speech quality model estimations: (i) a wideband equipment impairment factor and (ii) a wideband packet-loss robustness factor.
Table 6 Average impairment factor values for WB and NB speech codecs, derived using different signal-based models and auditory tests.
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In the following, we will use the term "impairment-factor-based"—or "IF-based"—for the model based on impairment factors, and the term "quality-based"—or "Q-based"—for the model based on audio and video qualities.
Figures 5 and 6 show the performance of the content-blind impairment-factor-based model on the evaluation dataset for HD and SD, when the impairment factors are derived from the subjective tests.
Thus, for predicting the audiovisual quality of IPTV services using the impairment-factor-based model, we first extract parameters from the audio and video packet trace, insert these into (5), (6), (7), and (8), and finally insert the predicted impairment factors into (4).
However, the proposed models have some limitations: when the audio and video qualities and impairment factors are predicted from audio and video quality models, the impairment-factor-based variants are less robust to audio and video quality prediction errors than the quality-based variants.
Impairment factors are derived from the subjective tests.
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