Estimating Perceived Video Quality from Objective Parameters in Video over IP Services
Pedro de la Cruz Ramos, Joaquín Navarro Salmerón, Raquel Pérez Leal, Francisco González Vidal
Abstract
Pedro de la Cruz Ramos, Joaquín Navarro Salmerón, Raquel Pérez Leal, Francisco González Vidal
Abstract
In Video over IP services, perceived video quality heavily depends on parameters such as video coding and network Quality of Service. This paper proposes a model for the estimation of perceived video quality in video streaming and broadcasting services that combines the aforementioned parameters with other that depend mainly on the information contents of the video sequences. These fitting parameters are derived from the Spatial and Temporal Information contents of the sequences. This model does not require reference to the original video sequence so it can be used for online, real-time monitoring of perceived video quality in Video over IP services. Furthermore, this paper proposes a measurement workbench designed to acquire both training data for model fitting and test data for model validation. Preliminary results show good correlation between measured and predicted values.
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In Video over IP services, perceived video quality heavily depends on parameters such as video coding and network Quality of Service. This paper proposes a model for the estimation of perceived video quality in video streaming and broadcasting services that combines the aforementioned parameters with other that depend mainly on the information contents of the video sequences. These fitting parameters are derived from the Spatial and Temporal Information contents of the sequences. This model does not require reference to the original video sequence so it can be used for online, real-time monitoring of perceived video quality in Video over IP services. Furthermore, this paper proposes a measurement workbench designed to acquire both training data for model fitting and test data for model validation. Preliminary results show good correlation between measured and predicted values.
Key concepts: Video quality, Computer science, PEVQ, Subjective video quality, Video compression picture types, Multiview Video Coding, Broadcasting (networking), Rate–distortion optimization