REDUCED-REFERENCE PERCEPTUAL QUALITY ASSESSMENT FOR VIDEO STREAMING

We propose a perceptual video quality monitoring metric for streaming applications using the optical flow features. This approach is a reduced-reference pixel-based and relies only on the deviation of the optical flow of the corrupted frames. This techniques compares an optical flow descriptor from the corrupted frame against the descriptor obtained from the anchor frame. 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. We validate our proposed metric by testing it on a variety of distorted sequences from the LIVE mobile database. Our results show that our metric estimates the perceptual quality at the sequence level accurately. We report the correlation coefficients with the differential mean opinion scores (DMOS) reported in the database. The results show Spearman's correlations of 0.88 for the tested sequences.