The increasing use of compression standards in broadcasting digital TV has raised the need for established criteria to measure perceived quality. This paper presents a methodology using circular backpropagation (CBP) neural networks for the objective quality assessment of MPEG video streams. Objective features are continuously extracted from compressed video streams on a frame-by-frame basis; they feed the CBP network estimating the corresponding perceived quality. The resulting adaptive modeling of subjective perception supports a real-time system for monitoring displayed video quality.
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Gastaldo et al. (2002) studied this question.
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