A Novel No-Reference Metric for Estimating the Impact of Frame Freezing Artifacts on Perceptual Quality of Streamed Videos

Abstract

Online monitoring of multimedia networks is required to ensure seamless and ubiquitous delivery of services to the end users. Quality of multimedia content, such as video streams, often gets degraded due to network losses such as packet loss. Frame freezing artifacts are introduced in a video stream when packet loss or packet delay takes place. Estimating the perceptual impact of these artifacts on quality of experience of end users helps service providers to maintain quality of service. In this paper, we have presented a novel no-reference video quality metric, which measures the impact of frame freezing due to packet loss and delay in video streaming networks. The proposed metric is based on several features that directly impact the quality of experience of end users. These features, including motion characteristics of videos, are calculated using the temporal information between video frames and then combined mathematically to form a video quality metric. Different weights are assigned to different features for better performance of the proposed metric. With detailed experiments, we have shown that our method outperforms other contemporary methods in terms of high accuracy and low computation time in frame freeze detection, low root mean square values, high coefficient of determination, and high correlation between subjective and objective measurements. We have used five video databases for our model's evaluation and validation. Furthermore, we have shown that our method is statistically superior to the other models in comparison.