Reduced-reference video QoE assessment method based on image feature information

Abstract

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 Divergence (KLD) and therefore MOS is evaluated using neural network fitting. The LIVE Video Quality Database is used for testing the performance of proposed method. result confirms that the proposed method is competitive and suitable for assessing the QoE of real-time video service.