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This paper presents a study that evaluates the performance of multi-view human activity recognition with videos having degraded quality. For the activity recognition models, a support vector machine-based approach using spatiotemporal features and a deep learning-based approach using convolutional and recurrent layers are built. We investigate the recognition performance of the two models with respect...
This paper deals with the issue of the perceptual quality evaluation of user-generated videos shared online, which is an important step toward designing video-sharing services that maximize users’ satisfaction in terms of quality. We first analyze viewers’ quality perception patterns by applying graph analysis techniques to subjective rating data. We then examine the performance of existing state-of-the-art...
When human subjects assess the quality of multimedia data, high level perceptual processes such as focus of attention (FoA) and eye movements are believed to play an important role in such tasks. While prior art reports incorporation of visual FoA into objective quality metrics, audio-visual FoA has been rarely addressed and utilized in spite of the importance and presence of both audio and video...
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