No-reference perceptual quality assessment of streamed videos using optical flow features

Abstract

This paper proposes a novel perceptual video quality assessment metric for streamed videos using optical flow statistical features. We analyze the impact of network losses on the decoded videos and the resulting error propagation. We show that the statistical features of the optical flow of the corrupted frames can be used to measure the distortion in the received video. We show that this approach is suitable for videos with complex motion patterns. Our technique does not make any assumptions on the coding conditions, network loss patterns or error concealment techniques. The proposed approach is pixel-based and relies only on the inconsistency of the optical flow of the corrupted frames. We validate our proposed quality metric by testing it on a variety of coded sequences subject to network losses from the recently proposed LIVE mobile database. Our results show that the proposed metric can estimate perceptual quality of channel-induced distortions at the frame and sequence levels. For the test videos, we report Pearson's and Spearman's correlation coefficients with the temporal mean opinion scores (MOSs) reported in the database. The results show average correlations of 0.91 and 0.92 for the test sequences, respectively.