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This paper discusses how to assess video Quality of Experience (QoE) with image feature information which includes texture and saliency information. In order to compress and transmit the feature information, wavelet transform is conducted and the high-frequency component histograms are fitted using generalized Gaussian distribution. At end user side, the video distortion is measured by using Kullback-Leibler...
In order to assess the quality of experience (QoE) of HTTP video streaming, the model of three levels of quality of service (QoS): network QoS, application QoS and QoE, is employed in this paper. We mainly study the effects of pause position, and therefore propose two new application performance metrics: location of each pause and time interval of pauses. We first focus on the buffer behaviors of...
In this paper, we discuss how to assess video Quality of Experience (QoE) with image saliency and texture information extracted from original and distorted video sequences. Based on this information, we proposed two categories of full-reference quality metrics. The first category of metrics considers spatial distortions measured by MSE and SSIM in terms of saliency weighted images, saliency maps and...
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