Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies

By Anush K. Moorthy, Lark Kwon Choi, A. Bovik, Gustavo De Veciana. IEEE Journal on Selected Topics in Signal Processing, 2012

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Large-Scale Study of Perceptual Video Quality (2018, background): “simulated varying channel induced distortions, and the LIVE Mobile Video Quality Database [8] includes channel induced”
Quality Assessment of In-the-Wild Videos (2019, methodology): “re temporally heterogeneous. On account of this, current state-of-the-art video quality assessment (VQA) methods (e.g., VBLIINDS [35] and VIIDEO [28]) validated on traditional synthetic VQA databases [30, 38] fail in predicting the quality of in-the-wild videos [10, 23, 31, 42]. This work focuses on”
A Survey on Quality of Experience of HTTP Adaptive Streaming (2015, unknown): “This finding is confirmed by [99] for mobile devices.”
The Konstanz natural video database (KoNViD-1k) (2017, background): “It was further extended to LIVE Mobile [19], which also modelled distortions in heavily trafficked wireless networks, containing dynamical changing distortions including frame-freezes and temporally varying compression rates.”