Video acuity assessment in mobile devices
Eilwoo Baik, Amit Pande, Chris Stover, Prasant Mohapatra · 2015
The quality of mobile videos is usually quantified through the Quality of Experience (QoE), which is usually based on network QoS measurements, user engagement, or post-view subjective scores. Such quantifications are not adequate for real-time evaluation. They cannot provide on-line feedback for improvement of visual acuity, which represents the actual viewing experience of the end user. We present a visual acuity framework which makes fast online computations in a mobile device and provide an accurate estimate of mobile video QoE. We identify and study the three main causes that impact visual acuity in mobile videos: spatial distortions, types of buffering and resolution changes. Each of them can be accurately modeled using our framework. We use machine learning techniques to build a prediction model for visual acuity, which depicts more than 78% accuracy. We present an experimental implementation on iPhone 4 and 5s to show that the proposed visual acuity framework is feasible to deploy in mobile devices. Using a data corpus of over 2852 mobile video clips for the experiments, we validate the proposed framework.