Stereoscopic Video Quality Assessment in the Context of Internet of Things

Abstract

Under the background of Internet of Things (IoT), users pursue a higher quality of immer-sive visual experience, including virtual reality (VR), augmented reality and stereoscopic information. In order to ensure the quality of experience (QoE), an effective quality assessment method is proposed to accurately determine the quality of information. Specifically, for the large amount of stereo video data, the representative sequence of stereo video is extract to reduce the data volume. Inspired by binocular channel theory, operators of the difference map and the novel fusion map are executed on these sequences. Then a dynamic texture descriptor, known as volume local binary pattern (VLBP), is employed to represent the spatio-temporal domain to predict the stereo video quality. Experiments have been executed on two public databases and results show that our method has excellent quality prediction capability.